Sample records for optimization simulated annealing
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Wang, Lipo; Li, Sa; Tian, Fuyu; Fu, Xiuju
2004-10-01
Recently Chen and Aihara have demonstrated both experimentally and mathematically that their chaotic simulated annealing (CSA) has better search ability for solving combinatorial optimization problems compared to both the Hopfield-Tank approach and stochastic simulated annealing (SSA). However, CSA may not find a globally optimal solution no matter how slowly annealing is carried out, because the chaotic dynamics are completely deterministic. In contrast, SSA tends to settle down to a global optimum if the temperature is reduced sufficiently slowly. Here we combine the best features of both SSA and CSA, thereby proposing a new approach for solving optimization problems, i.e., stochastic chaotic simulated annealing, by using a noisy chaotic neural network. We show the effectiveness of this new approach with two difficult combinatorial optimization problems, i.e., a traveling salesman problem and a channel assignment problem for cellular mobile communications.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Liang, Faming; Cheng, Yichen; Lin, Guang
2014-06-13
Simulated annealing has been widely used in the solution of optimization problems. As known by many researchers, the global optima cannot be guaranteed to be located by simulated annealing unless a logarithmic cooling schedule is used. However, the logarithmic cooling schedule is so slow that no one can afford to have such a long CPU time. This paper proposes a new stochastic optimization algorithm, the so-called simulated stochastic approximation annealing algorithm, which is a combination of simulated annealing and the stochastic approximation Monte Carlo algorithm. Under the framework of stochastic approximation Markov chain Monte Carlo, it is shown that themore » new algorithm can work with a cooling schedule in which the temperature can decrease much faster than in the logarithmic cooling schedule, e.g., a square-root cooling schedule, while guaranteeing the global optima to be reached when the temperature tends to zero. The new algorithm has been tested on a few benchmark optimization problems, including feed-forward neural network training and protein-folding. The numerical results indicate that the new algorithm can significantly outperform simulated annealing and other competitors.« less
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Analysis and optimization of population annealing
NASA Astrophysics Data System (ADS)
Amey, Christopher; Machta, Jonathan
2018-03-01
Population annealing is an easily parallelizable sequential Monte Carlo algorithm that is well suited for simulating the equilibrium properties of systems with rough free-energy landscapes. In this work we seek to understand and improve the performance of population annealing. We derive several useful relations between quantities that describe the performance of population annealing and use these relations to suggest methods to optimize the algorithm. These optimization methods were tested by performing large-scale simulations of the three-dimensional (3D) Edwards-Anderson (Ising) spin glass and measuring several observables. The optimization methods were found to substantially decrease the amount of computational work necessary as compared to previously used, unoptimized versions of population annealing. We also obtain more accurate values of several important observables for the 3D Edwards-Anderson model.
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Stochastic search in structural optimization - Genetic algorithms and simulated annealing
NASA Technical Reports Server (NTRS)
Hajela, Prabhat
1993-01-01
An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.
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Comparison of optimization algorithms in intensity-modulated radiation therapy planning
NASA Astrophysics Data System (ADS)
Kendrick, Rachel
Intensity-modulated radiation therapy is used to better conform the radiation dose to the target, which includes avoiding healthy tissue. Planning programs employ optimization methods to search for the best fluence of each photon beam, and therefore to create the best treatment plan. The Computational Environment for Radiotherapy Research (CERR), a program written in MATLAB, was used to examine some commonly-used algorithms for one 5-beam plan. Algorithms include the genetic algorithm, quadratic programming, pattern search, constrained nonlinear optimization, simulated annealing, the optimization method used in Varian EclipseTM, and some hybrids of these. Quadratic programing, simulated annealing, and a quadratic/simulated annealing hybrid were also separately compared using different prescription doses. The results of each dose-volume histogram as well as the visual dose color wash were used to compare the plans. CERR's built-in quadratic programming provided the best overall plan, but avoidance of the organ-at-risk was rivaled by other programs. Hybrids of quadratic programming with some of these algorithms seems to suggest the possibility of better planning programs, as shown by the improved quadratic/simulated annealing plan when compared to the simulated annealing algorithm alone. Further experimentation will be done to improve cost functions and computational time.
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Image reconstruction through thin scattering media by simulated annealing algorithm
NASA Astrophysics Data System (ADS)
Fang, Longjie; Zuo, Haoyi; Pang, Lin; Yang, Zuogang; Zhang, Xicheng; Zhu, Jianhua
2018-07-01
An idea for reconstructing the image of an object behind thin scattering media is proposed by phase modulation. The optimized phase mask is achieved by modulating the scattered light using simulated annealing algorithm. The correlation coefficient is exploited as a fitness function to evaluate the quality of reconstructed image. The reconstructed images optimized from simulated annealing algorithm and genetic algorithm are compared in detail. The experimental results show that our proposed method has better definition and higher speed than genetic algorithm.
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Simulated parallel annealing within a neighborhood for optimization of biomechanical systems.
Higginson, J S; Neptune, R R; Anderson, F C
2005-09-01
Optimization problems for biomechanical systems have become extremely complex. Simulated annealing (SA) algorithms have performed well in a variety of test problems and biomechanical applications; however, despite advances in computer speed, convergence to optimal solutions for systems of even moderate complexity has remained prohibitive. The objective of this study was to develop a portable parallel version of a SA algorithm for solving optimization problems in biomechanics. The algorithm for simulated parallel annealing within a neighborhood (SPAN) was designed to minimize interprocessor communication time and closely retain the heuristics of the serial SA algorithm. The computational speed of the SPAN algorithm scaled linearly with the number of processors on different computer platforms for a simple quadratic test problem and for a more complex forward dynamic simulation of human pedaling.
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Efficiency of quantum vs. classical annealing in nonconvex learning problems
Zecchina, Riccardo
2018-01-01
Quantum annealers aim at solving nonconvex optimization problems by exploiting cooperative tunneling effects to escape local minima. The underlying idea consists of designing a classical energy function whose ground states are the sought optimal solutions of the original optimization problem and add a controllable quantum transverse field to generate tunneling processes. A key challenge is to identify classes of nonconvex optimization problems for which quantum annealing remains efficient while thermal annealing fails. We show that this happens for a wide class of problems which are central to machine learning. Their energy landscapes are dominated by local minima that cause exponential slowdown of classical thermal annealers while simulated quantum annealing converges efficiently to rare dense regions of optimal solutions. PMID:29382764
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Temperature Scaling Law for Quantum Annealing Optimizers.
Albash, Tameem; Martin-Mayor, Victor; Hen, Itay
2017-09-15
Physical implementations of quantum annealing unavoidably operate at finite temperatures. We point to a fundamental limitation of fixed finite temperature quantum annealers that prevents them from functioning as competitive scalable optimizers and show that to serve as optimizers annealer temperatures must be appropriately scaled down with problem size. We derive a temperature scaling law dictating that temperature must drop at the very least in a logarithmic manner but also possibly as a power law with problem size. We corroborate our results by experiment and simulations and discuss the implications of these to practical annealers.
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Simulated annealing in orbital flight planning
NASA Technical Reports Server (NTRS)
Soller, Jeffrey
1990-01-01
Simulated annealing is used to solve a minimum fuel trajectory problem in the space station environment. The environment is unique because the space station will define the first true multivehicle environment in space. The optimization yields surfaces which are potentially complex, with multiple local minima. Because of the likelihood of these local minima, descent techniques are unable to offer robust solutions. Other deterministic optimization techniques were explored without success. The simulated annealing optimization is capable of identifying a minimum-fuel, two-burn trajectory subject to four constraints. Furthermore, the computational efforts involved in the optimization are such that missions could be planned on board the space station. Potential applications could include the on-site planning of rendezvous with a target craft of the emergency rescue of an astronaut. Future research will include multiwaypoint maneuvers, using a knowledge base to guide the optimization.
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A theoretical comparison of evolutionary algorithms and simulated annealing
DOE Office of Scientific and Technical Information (OSTI.GOV)
Hart, W.E.
1995-08-28
This paper theoretically compares the performance of simulated annealing and evolutionary algorithms. Our main result is that under mild conditions a wide variety of evolutionary algorithms can be shown to have greater performance than simulated annealing after a sufficiently large number of function evaluations. This class of EAs includes variants of evolutionary strategie and evolutionary programming, the canonical genetic algorithm, as well as a variety of genetic algorithms that have been applied to combinatorial optimization problems. The proof of this result is based on a performance analysis of a very general class of stochastic optimization algorithms, which has implications formore » the performance of a variety of other optimization algorithm.« less
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Beckner, B.L.; Xong, X.
1995-12-31
A method for optimizing the net present value of a full field development by varying the placement and sequence of production wells is presented. This approach is automated and combines an economics package and Mobil`s in-house simulator, PEGASUS, within a simulated annealing optimization engine. A novel framing of the well placement and scheduling problem as a classic {open_quotes}travelling salesman problem{close_quotes} is required before optimization via simulated annealing can be applied practically. An example of a full field development using this technique shows that non-uniform well spacings are optimal (from an NPV standpoint) when the effects of well interference and variablemore » reservoir properties are considered. Examples of optimizing field NPV with variable well costs also show that non-uniform wells spacings are optimal. Project NPV increases of 25 to 30 million dollars were shown using the optimal, nonuniform development versus reasonable, uniform developments. The ability of this technology to deduce these non-uniform well spacings opens up many potential applications that should materially impact the economic performance of field developments.« less
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NASA Technical Reports Server (NTRS)
Soller, Jeffrey Alan; Grunwald, Arthur J.; Ellis, Stephen R.
1991-01-01
Simulated annealing is used to solve a minimum fuel trajectory problem in the space station environment. The environment is special because the space station will define a multivehicle environment in space. The optimization surface is a complex nonlinear function of the initial conditions of the chase and target crafts. Small permutations in the input conditions can result in abrupt changes to the optimization surface. Since no prior knowledge about the number or location of local minima on the surface is available, the optimization must be capable of functioning on a multimodal surface. It was reported in the literature that the simulated annealing algorithm is more effective on such surfaces than descent techniques using random starting points. The simulated annealing optimization was found to be capable of identifying a minimum fuel, two-burn trajectory subject to four constraints which are integrated into the optimization using a barrier method. The computations required to solve the optimization are fast enough that missions could be planned on board the space station. Potential applications for on board planning of missions are numerous. Future research topics may include optimal planning of multi-waypoint maneuvers using a knowledge base to guide the optimization, and a study aimed at developing robust annealing schedules for potential on board missions.
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Experiences with serial and parallel algorithms for channel routing using simulated annealing
NASA Technical Reports Server (NTRS)
Brouwer, Randall Jay
1988-01-01
Two algorithms for channel routing using simulated annealing are presented. Simulated annealing is an optimization methodology which allows the solution process to back up out of local minima that may be encountered by inappropriate selections. By properly controlling the annealing process, it is very likely that the optimal solution to an NP-complete problem such as channel routing may be found. The algorithm presented proposes very relaxed restrictions on the types of allowable transformations, including overlapping nets. By freeing that restriction and controlling overlap situations with an appropriate cost function, the algorithm becomes very flexible and can be applied to many extensions of channel routing. The selection of the transformation utilizes a number of heuristics, still retaining the pseudorandom nature of simulated annealing. The algorithm was implemented as a serial program for a workstation, and a parallel program designed for a hypercube computer. The details of the serial implementation are presented, including many of the heuristics used and some of the resulting solutions.
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EIT image regularization by a new Multi-Objective Simulated Annealing algorithm.
Castro Martins, Thiago; Sales Guerra Tsuzuki, Marcos
2015-01-01
Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.
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Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing
Abubaker, Ahmad; Baharum, Adam; Alrefaei, Mahmoud
2015-01-01
This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, “MOPSOSA”. The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets. PMID:26132309
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Wang, Hailong; Sun, Yuqiu; Su, Qinghua; Xia, Xuewen
2018-01-01
The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F) is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed. PMID:29666635
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Parallel tempering for the traveling salesman problem
DOE Office of Scientific and Technical Information (OSTI.GOV)
Percus, Allon; Wang, Richard; Hyman, Jeffrey
We explore the potential of parallel tempering as a combinatorial optimization method, applying it to the traveling salesman problem. We compare simulation results of parallel tempering with a benchmark implementation of simulated annealing, and study how different choices of parameters affect the relative performance of the two methods. We find that a straightforward implementation of parallel tempering can outperform simulated annealing in several crucial respects. When parameters are chosen appropriately, both methods yield close approximation to the actual minimum distance for an instance with 200 nodes. However, parallel tempering yields more consistently accurate results when a series of independent simulationsmore » are performed. Our results suggest that parallel tempering might offer a simple but powerful alternative to simulated annealing for combinatorial optimization problems.« less
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An adaptive approach to the physical annealing strategy for simulated annealing
NASA Astrophysics Data System (ADS)
Hasegawa, M.
2013-02-01
A new and reasonable method for adaptive implementation of simulated annealing (SA) is studied on two types of random traveling salesman problems. The idea is based on the previous finding on the search characteristics of the threshold algorithms, that is, the primary role of the relaxation dynamics in their finite-time optimization process. It is shown that the effective temperature for optimization can be predicted from the system's behavior analogous to the stabilization phenomenon occurring in the heating process starting from a quenched solution. The subsequent slow cooling near the predicted point draws out the inherent optimizing ability of finite-time SA in more straightforward manner than the conventional adaptive approach.
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Robust quantum optimizer with full connectivity.
Nigg, Simon E; Lörch, Niels; Tiwari, Rakesh P
2017-04-01
Quantum phenomena have the potential to speed up the solution of hard optimization problems. For example, quantum annealing, based on the quantum tunneling effect, has recently been shown to scale exponentially better with system size than classical simulated annealing. However, current realizations of quantum annealers with superconducting qubits face two major challenges. First, the connectivity between the qubits is limited, excluding many optimization problems from a direct implementation. Second, decoherence degrades the success probability of the optimization. We address both of these shortcomings and propose an architecture in which the qubits are robustly encoded in continuous variable degrees of freedom. By leveraging the phenomenon of flux quantization, all-to-all connectivity with sufficient tunability to implement many relevant optimization problems is obtained without overhead. Furthermore, we demonstrate the robustness of this architecture by simulating the optimal solution of a small instance of the nondeterministic polynomial-time hard (NP-hard) and fully connected number partitioning problem in the presence of dissipation.
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Recursive Branching Simulated Annealing Algorithm
NASA Technical Reports Server (NTRS)
Bolcar, Matthew; Smith, J. Scott; Aronstein, David
2012-01-01
This innovation is a variation of a simulated-annealing optimization algorithm that uses a recursive-branching structure to parallelize the search of a parameter space for the globally optimal solution to an objective. The algorithm has been demonstrated to be more effective at searching a parameter space than traditional simulated-annealing methods for a particular problem of interest, and it can readily be applied to a wide variety of optimization problems, including those with a parameter space having both discrete-value parameters (combinatorial) and continuous-variable parameters. It can take the place of a conventional simulated- annealing, Monte-Carlo, or random- walk algorithm. In a conventional simulated-annealing (SA) algorithm, a starting configuration is randomly selected within the parameter space. The algorithm randomly selects another configuration from the parameter space and evaluates the objective function for that configuration. If the objective function value is better than the previous value, the new configuration is adopted as the new point of interest in the parameter space. If the objective function value is worse than the previous value, the new configuration may be adopted, with a probability determined by a temperature parameter, used in analogy to annealing in metals. As the optimization continues, the region of the parameter space from which new configurations can be selected shrinks, and in conjunction with lowering the annealing temperature (and thus lowering the probability for adopting configurations in parameter space with worse objective functions), the algorithm can converge on the globally optimal configuration. The Recursive Branching Simulated Annealing (RBSA) algorithm shares some features with the SA algorithm, notably including the basic principles that a starting configuration is randomly selected from within the parameter space, the algorithm tests other configurations with the goal of finding the globally optimal solution, and the region from which new configurations can be selected shrinks as the search continues. The key difference between these algorithms is that in the SA algorithm, a single path, or trajectory, is taken in parameter space, from the starting point to the globally optimal solution, while in the RBSA algorithm, many trajectories are taken; by exploring multiple regions of the parameter space simultaneously, the algorithm has been shown to converge on the globally optimal solution about an order of magnitude faster than when using conventional algorithms. Novel features of the RBSA algorithm include: 1. More efficient searching of the parameter space due to the branching structure, in which multiple random configurations are generated and multiple promising regions of the parameter space are explored; 2. The implementation of a trust region for each parameter in the parameter space, which provides a natural way of enforcing upper- and lower-bound constraints on the parameters; and 3. The optional use of a constrained gradient- search optimization, performed on the continuous variables around each branch s configuration in parameter space to improve search efficiency by allowing for fast fine-tuning of the continuous variables within the trust region at that configuration point.
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Quantum versus simulated annealing in wireless interference network optimization.
Wang, Chi; Chen, Huo; Jonckheere, Edmond
2016-05-16
Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking-more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes. In addition, D-Wave implementation is made error insensitive by a novel Hamiltonian extra penalty weight adjustment that enlarges the gap and substantially reduces the occurrence of interference violations resulting from inevitable spin bias and coupling errors. The major result of this paper is that quantum annealing benefits more than simulated annealing from this gap expansion process, both in terms of ST99 speedup and network queue occupancy. It is the hope that this could become a real-word application niche where potential benefits of quantum annealing could be objectively assessed.
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Quantum versus simulated annealing in wireless interference network optimization
Wang, Chi; Chen, Huo; Jonckheere, Edmond
2016-01-01
Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking—more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes. In addition, D-Wave implementation is made error insensitive by a novel Hamiltonian extra penalty weight adjustment that enlarges the gap and substantially reduces the occurrence of interference violations resulting from inevitable spin bias and coupling errors. The major result of this paper is that quantum annealing benefits more than simulated annealing from this gap expansion process, both in terms of ST99 speedup and network queue occupancy. It is the hope that this could become a real-word application niche where potential benefits of quantum annealing could be objectively assessed. PMID:27181056
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Quantum versus simulated annealing in wireless interference network optimization
NASA Astrophysics Data System (ADS)
Wang, Chi; Chen, Huo; Jonckheere, Edmond
2016-05-01
Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking—more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes. In addition, D-Wave implementation is made error insensitive by a novel Hamiltonian extra penalty weight adjustment that enlarges the gap and substantially reduces the occurrence of interference violations resulting from inevitable spin bias and coupling errors. The major result of this paper is that quantum annealing benefits more than simulated annealing from this gap expansion process, both in terms of ST99 speedup and network queue occupancy. It is the hope that this could become a real-word application niche where potential benefits of quantum annealing could be objectively assessed.
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Compiling Planning into Quantum Optimization Problems: A Comparative Study
2015-06-07
and Sipser, M. 2000. Quantum computation by adiabatic evolution. arXiv:quant- ph/0001106. Fikes, R. E., and Nilsson, N. J. 1972. STRIPS: A new...become available: quantum annealing. Quantum annealing is one of the most accessible quantum algorithms for a computer sci- ence audience not versed...in quantum computing because of its close ties to classical optimization algorithms such as simulated annealing. While large-scale universal quantum
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Hybrid General Pattern Search and Simulated Annealing for Industrail Production Planning Problems
NASA Astrophysics Data System (ADS)
Vasant, P.; Barsoum, N.
2010-06-01
In this paper, the hybridization of GPS (General Pattern Search) method and SA (Simulated Annealing) incorporated in the optimization process in order to look for the global optimal solution for the fitness function and decision variables as well as minimum computational CPU time. The real strength of SA approach been tested in this case study problem of industrial production planning. This is due to the great advantage of SA for being easily escaping from trapped in local minima by accepting up-hill move through a probabilistic procedure in the final stages of optimization process. Vasant [1] in his Ph. D thesis has provided 16 different techniques of heuristic and meta-heuristic in solving industrial production problems with non-linear cubic objective functions, eight decision variables and 29 constraints. In this paper, fuzzy technological problems have been solved using hybrid techniques of general pattern search and simulated annealing. The simulated and computational results are compared to other various evolutionary techniques.
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Asset Allocation to Cover a Region of Piracy
2011-09-01
1087-1092. 8. Kirkpatrick, S., Optimization by Simulated Annealing. Science, 1983. 220(4598): p. 671-680. 9. Daskin , M. S., A bibliography for some...... a uniform piracy risk and where some areas are more vulnerable than others. Simulated annealing was used to allocate the patrolling naval assets
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An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities.
Amer, Hayder; Salman, Naveed; Hawes, Matthew; Chaqfeh, Moumena; Mihaylova, Lyudmila; Mayfield, Martin
2016-06-30
Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads' length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO₂ emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario.
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An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities
Amer, Hayder; Salman, Naveed; Hawes, Matthew; Chaqfeh, Moumena; Mihaylova, Lyudmila; Mayfield, Martin
2016-01-01
Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads’ length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO2 emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario. PMID:27376289
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Robust quantum optimizer with full connectivity
Nigg, Simon E.; Lörch, Niels; Tiwari, Rakesh P.
2017-01-01
Quantum phenomena have the potential to speed up the solution of hard optimization problems. For example, quantum annealing, based on the quantum tunneling effect, has recently been shown to scale exponentially better with system size than classical simulated annealing. However, current realizations of quantum annealers with superconducting qubits face two major challenges. First, the connectivity between the qubits is limited, excluding many optimization problems from a direct implementation. Second, decoherence degrades the success probability of the optimization. We address both of these shortcomings and propose an architecture in which the qubits are robustly encoded in continuous variable degrees of freedom. By leveraging the phenomenon of flux quantization, all-to-all connectivity with sufficient tunability to implement many relevant optimization problems is obtained without overhead. Furthermore, we demonstrate the robustness of this architecture by simulating the optimal solution of a small instance of the nondeterministic polynomial-time hard (NP-hard) and fully connected number partitioning problem in the presence of dissipation. PMID:28435880
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Hybrid annealing: Coupling a quantum simulator to a classical computer
NASA Astrophysics Data System (ADS)
Graß, Tobias; Lewenstein, Maciej
2017-05-01
Finding the global minimum in a rugged potential landscape is a computationally hard task, often equivalent to relevant optimization problems. Annealing strategies, either classical or quantum, explore the configuration space by evolving the system under the influence of thermal or quantum fluctuations. The thermal annealing dynamics can rapidly freeze the system into a low-energy configuration, and it can be simulated well on a classical computer, but it easily gets stuck in local minima. Quantum annealing, on the other hand, can be guaranteed to find the true ground state and can be implemented in modern quantum simulators; however, quantum adiabatic schemes become prohibitively slow in the presence of quasidegeneracies. Here, we propose a strategy which combines ideas from simulated annealing and quantum annealing. In such a hybrid algorithm, the outcome of a quantum simulator is processed on a classical device. While the quantum simulator explores the configuration space by repeatedly applying quantum fluctuations and performing projective measurements, the classical computer evaluates each configuration and enforces a lowering of the energy. We have simulated this algorithm for small instances of the random energy model, showing that it potentially outperforms both simulated thermal annealing and adiabatic quantum annealing. It becomes most efficient for problems involving many quasidegenerate ground states.
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Improving Simulated Annealing by Recasting it as a Non-Cooperative Game
NASA Technical Reports Server (NTRS)
Wolpert, David; Bandari, Esfandiar; Tumer, Kagan
2001-01-01
The game-theoretic field of COllective INtelligence (COIN) concerns the design of computer-based players engaged in a non-cooperative game so that as those players pursue their self-interests, a pre-specified global goal for the collective computational system is achieved "as a side-effect". Previous implementations of COIN algorithms have outperformed conventional techniques by up to several orders of magnitude, on domains ranging from telecommunications control to optimization in congestion problems. Recent mathematical developments have revealed that these previously developed game-theory-motivated algorithms were based on only two of the three factors determining performance. Consideration of only the third factor would instead lead to conventional optimization techniques like simulated annealing that have little to do with non-cooperative games. In this paper we present an algorithm based on all three terms at once. This algorithm can be viewed as a way to modify simulated annealing by recasting it as a non-cooperative game, with each variable replaced by a player. This recasting allows us to leverage the intelligent behavior of the individual players to substantially improve the exploration step of the simulated annealing. Experiments are presented demonstrating that this recasting improves simulated annealing by several orders of magnitude for spin glass relaxation and bin-packing.
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Advantages of Unfair Quantum Ground-State Sampling.
Zhang, Brian Hu; Wagenbreth, Gene; Martin-Mayor, Victor; Hen, Itay
2017-04-21
The debate around the potential superiority of quantum annealers over their classical counterparts has been ongoing since the inception of the field. Recent technological breakthroughs, which have led to the manufacture of experimental prototypes of quantum annealing optimizers with sizes approaching the practical regime, have reignited this discussion. However, the demonstration of quantum annealing speedups remains to this day an elusive albeit coveted goal. We examine the power of quantum annealers to provide a different type of quantum enhancement of practical relevance, namely, their ability to serve as useful samplers from the ground-state manifolds of combinatorial optimization problems. We study, both numerically by simulating stoquastic and non-stoquastic quantum annealing processes, and experimentally, using a prototypical quantum annealing processor, the ability of quantum annealers to sample the ground-states of spin glasses differently than thermal samplers. We demonstrate that (i) quantum annealers sample the ground-state manifolds of spin glasses very differently than thermal optimizers (ii) the nature of the quantum fluctuations driving the annealing process has a decisive effect on the final distribution, and (iii) the experimental quantum annealer samples ground-state manifolds significantly differently than thermal and ideal quantum annealers. We illustrate how quantum annealers may serve as powerful tools when complementing standard sampling algorithms.
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Shape optimization of road tunnel cross-section by simulated annealing
NASA Astrophysics Data System (ADS)
Sobótka, Maciej; Pachnicz, Michał
2016-06-01
The paper concerns shape optimization of a tunnel excavation cross-section. The study incorporates optimization procedure of the simulated annealing (SA). The form of a cost function derives from the energetic optimality condition, formulated in the authors' previous papers. The utilized algorithm takes advantage of the optimization procedure already published by the authors. Unlike other approaches presented in literature, the one introduced in this paper takes into consideration a practical requirement of preserving fixed clearance gauge. Itasca Flac software is utilized in numerical examples. The optimal excavation shapes are determined for five different in situ stress ratios. This factor significantly affects the optimal topology of excavation. The resulting shapes are elongated in the direction of a principal stress greater value. Moreover, the obtained optimal shapes have smooth contours circumscribing the gauge.
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A Simulated Annealing Algorithm for the Optimization of Multistage Depressed Collector Efficiency
NASA Technical Reports Server (NTRS)
Vaden, Karl R.; Wilson, Jeffrey D.; Bulson, Brian A.
2002-01-01
The microwave traveling wave tube amplifier (TWTA) is widely used as a high-power transmitting source for space and airborne communications. One critical factor in designing a TWTA is the overall efficiency. However, overall efficiency is highly dependent upon collector efficiency; so collector design is critical to the performance of a TWTA. Therefore, NASA Glenn Research Center has developed an optimization algorithm based on Simulated Annealing to quickly design highly efficient multi-stage depressed collectors (MDC).
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Sheng, Zheng, E-mail: 19994035@sina.com; Wang, Jun; Zhou, Bihua
2014-03-15
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented tomore » tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.« less
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Vehicle routing problem with time windows using natural inspired algorithms
NASA Astrophysics Data System (ADS)
Pratiwi, A. B.; Pratama, A.; Sa’diyah, I.; Suprajitno, H.
2018-03-01
Process of distribution of goods needs a strategy to make the total cost spent for operational activities minimized. But there are several constrains have to be satisfied which are the capacity of the vehicles and the service time of the customers. This Vehicle Routing Problem with Time Windows (VRPTW) gives complex constrains problem. This paper proposes natural inspired algorithms for dealing with constrains of VRPTW which involves Bat Algorithm and Cat Swarm Optimization. Bat Algorithm is being hybrid with Simulated Annealing, the worst solution of Bat Algorithm is replaced by the solution from Simulated Annealing. Algorithm which is based on behavior of cats, Cat Swarm Optimization, is improved using Crow Search Algorithm to make simplier and faster convergence. From the computational result, these algorithms give good performances in finding the minimized total distance. Higher number of population causes better computational performance. The improved Cat Swarm Optimization with Crow Search gives better performance than the hybridization of Bat Algorithm and Simulated Annealing in dealing with big data.
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Population Annealing Monte Carlo for Frustrated Systems
NASA Astrophysics Data System (ADS)
Amey, Christopher; Machta, Jonathan
Population annealing is a sequential Monte Carlo algorithm that efficiently simulates equilibrium systems with rough free energy landscapes such as spin glasses and glassy fluids. A large population of configurations is initially thermalized at high temperature and then cooled to low temperature according to an annealing schedule. The population is kept in thermal equilibrium at every annealing step via resampling configurations according to their Boltzmann weights. Population annealing is comparable to parallel tempering in terms of efficiency, but has several distinct and useful features. In this talk I will give an introduction to population annealing and present recent progress in understanding its equilibration properties and optimizing it for spin glasses. Results from large-scale population annealing simulations for the Ising spin glass in 3D and 4D will be presented. NSF Grant DMR-1507506.
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Hybrid algorithms for fuzzy reverse supply chain network design.
Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua
2014-01-01
In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.
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Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design
Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.
2014-01-01
In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057
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Efficient Monte Carlo Methods for Biomolecular Simulations.
NASA Astrophysics Data System (ADS)
Bouzida, Djamal
A new approach to efficient Monte Carlo simulations of biological molecules is presented. By relaxing the usual restriction to Markov processes, we are able to optimize performance while dealing directly with the inhomogeneity and anisotropy inherent in these systems. The advantage of this approach is that we can introduce a wide variety of Monte Carlo moves to deal with complicated motions of the molecule, while maintaining full optimization at every step. This enables the use of a variety of collective rotational moves that relax long-wavelength modes. We were able to show by explicit simulations that the resulting algorithms substantially increase the speed of the simulation while reproducing the correct equilibrium behavior. This approach is particularly intended for simulations of macromolecules, although we expect it to be useful in other situations. The dynamic optimization of the new Monte Carlo methods makes them very suitable for simulated annealing experiments on all systems whose state space is continuous in general, and to the protein folding problem in particular. We introduce an efficient annealing schedule using preferential bias moves. Our simulated annealing experiments yield structures whose free energies were lower than the equilibrated X-ray structure, which leads us to believe that the empirical energy function used does not fully represent the interatomic interactions. Furthermore, we believe that the largest discrepancies involve the solvent effects in particular.
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Improving Simulated Annealing by Replacing Its Variables with Game-Theoretic Utility Maximizers
NASA Technical Reports Server (NTRS)
Wolpert, David H.; Bandari, Esfandiar; Tumer, Kagan
2001-01-01
The game-theory field of Collective INtelligence (COIN) concerns the design of computer-based players engaged in a non-cooperative game so that as those players pursue their self-interests, a pre-specified global goal for the collective computational system is achieved as a side-effect. Previous implementations of COIN algorithms have outperformed conventional techniques by up to several orders of magnitude, on domains ranging from telecommunications control to optimization in congestion problems. Recent mathematical developments have revealed that these previously developed algorithms were based on only two of the three factors determining performance. Consideration of only the third factor would instead lead to conventional optimization techniques like simulated annealing that have little to do with non-cooperative games. In this paper we present an algorithm based on all three terms at once. This algorithm can be viewed as a way to modify simulated annealing by recasting it as a non-cooperative game, with each variable replaced by a player. This recasting allows us to leverage the intelligent behavior of the individual players to substantially improve the exploration step of the simulated annealing. Experiments are presented demonstrating that this recasting significantly improves simulated annealing for a model of an economic process run over an underlying small-worlds topology. Furthermore, these experiments reveal novel small-worlds phenomena, and highlight the shortcomings of conventional mechanism design in bounded rationality domains.
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First-order design of geodetic networks using the simulated annealing method
NASA Astrophysics Data System (ADS)
Berné, J. L.; Baselga, S.
2004-09-01
The general problem of the optimal design for a geodetic network subject to any extrinsic factors, namely the first-order design problem, can be dealt with as a numeric optimization problem. The classic theory of this problem and the optimization methods are revised. Then the innovative use of the simulated annealing method, which has been successfully applied in other fields, is presented for this classical geodetic problem. This method, belonging to iterative heuristic techniques in operational research, uses a thermodynamical analogy to crystalline networks to offer a solution that converges probabilistically to the global optimum. Basic formulation and some examples are studied.
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Quantum approach to classical statistical mechanics.
Somma, R D; Batista, C D; Ortiz, G
2007-07-20
We present a new approach to study the thermodynamic properties of d-dimensional classical systems by reducing the problem to the computation of ground state properties of a d-dimensional quantum model. This classical-to-quantum mapping allows us to extend the scope of standard optimization methods by unifying them under a general framework. The quantum annealing method is naturally extended to simulate classical systems at finite temperatures. We derive the rates to assure convergence to the optimal thermodynamic state using the adiabatic theorem of quantum mechanics. For simulated and quantum annealing, we obtain the asymptotic rates of T(t) approximately (pN)/(k(B)logt) and gamma(t) approximately (Nt)(-c/N), for the temperature and magnetic field, respectively. Other annealing strategies are also discussed.
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Functionality limit of classical simulated annealing
NASA Astrophysics Data System (ADS)
Hasegawa, M.
2015-09-01
By analyzing the system dynamics in the landscape paradigm, optimization function of classical simulated annealing is reviewed on the random traveling salesman problems. The properly functioning region of the algorithm is experimentally determined in the size-time plane and the influence of its boundary on the scalability test is examined in the standard framework of this method. From both results, an empirical choice of temperature length is plausibly explained as a minimum requirement that the algorithm maintains its scalability within its functionality limit. The study exemplifies the applicability of computational physics analysis to the optimization algorithm research.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Chen Hongwei; High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031; Kong Xi
The method of quantum annealing (QA) is a promising way for solving many optimization problems in both classical and quantum information theory. The main advantage of this approach, compared with the gate model, is the robustness of the operations against errors originated from both external controls and the environment. In this work, we succeed in demonstrating experimentally an application of the method of QA to a simplified version of the traveling salesman problem by simulating the corresponding Schroedinger evolution with a NMR quantum simulator. The experimental results unambiguously yielded the optimal traveling route, in good agreement with the theoretical prediction.
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NASA Astrophysics Data System (ADS)
Balan, A. V.; Shivasankaran, N.; Magibalan, S.
2018-04-01
Low carbon steels used in chemical industries are frequently affected by corrosion. Cladding is a surfacing process used for depositing a thick layer of filler metal in a highly corrosive materials to achieve corrosion resistance. Flux cored arc welding (FCAW) is preferred in cladding process due to its augmented efficiency and higher deposition rate. In this cladding process, the effect of corrosion can be minimized by controlling the output responses such as minimizing dilution, penetration and maximizing bead width, reinforcement and ferrite number. This paper deals with the multi-objective optimization of flux cored arc welding responses by controlling the process parameters such as wire feed rate, welding speed, Nozzle to plate distance, welding gun angle for super duplex stainless steel material using simulated annealing technique. Regression equation has been developed and validated using ANOVA technique. The multi-objective optimization of weld bead parameters was carried out using simulated annealing to obtain optimum bead geometry for reducing corrosion. The potentiodynamic polarization test reveals the balanced formation of fine particles of ferrite and autenite content with desensitized nature of the microstructure in the optimized clad bead.
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An Introduction to Simulated Annealing
ERIC Educational Resources Information Center
Albright, Brian
2007-01-01
An attempt to model the physical process of annealing lead to the development of a type of combinatorial optimization algorithm that takes on the problem of getting trapped in a local minimum. The author presents a Microsoft Excel spreadsheet that illustrates how this works.
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Quantum Optimization of Fully Connected Spin Glasses
NASA Astrophysics Data System (ADS)
Venturelli, Davide; Mandrà, Salvatore; Knysh, Sergey; O'Gorman, Bryan; Biswas, Rupak; Smelyanskiy, Vadim
2015-07-01
Many NP-hard problems can be seen as the task of finding a ground state of a disordered highly connected Ising spin glass. If solutions are sought by means of quantum annealing, it is often necessary to represent those graphs in the annealer's hardware by means of the graph-minor embedding technique, generating a final Hamiltonian consisting of coupled chains of ferromagnetically bound spins, whose binding energy is a free parameter. In order to investigate the effect of embedding on problems of interest, the fully connected Sherrington-Kirkpatrick model with random ±1 couplings is programmed on the D-Wave TwoTM annealer using up to 270 qubits interacting on a Chimera-type graph. We present the best embedding prescriptions for encoding the Sherrington-Kirkpatrick problem in the Chimera graph. The results indicate that the optimal choice of embedding parameters could be associated with the emergence of the spin-glass phase of the embedded problem, whose presence was previously uncertain. This optimal parameter setting allows the performance of the quantum annealer to compete with (and potentially outperform, in the absence of analog control errors) optimized simulated annealing algorithms.
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Optimal mapping of irregular finite element domains to parallel processors
NASA Technical Reports Server (NTRS)
Flower, J.; Otto, S.; Salama, M.
1987-01-01
Mapping the solution domain of n-finite elements into N-subdomains that may be processed in parallel by N-processors is an optimal one if the subdomain decomposition results in a well-balanced workload distribution among the processors. The problem is discussed in the context of irregular finite element domains as an important aspect of the efficient utilization of the capabilities of emerging multiprocessor computers. Finding the optimal mapping is an intractable combinatorial optimization problem, for which a satisfactory approximate solution is obtained here by analogy to a method used in statistical mechanics for simulating the annealing process in solids. The simulated annealing analogy and algorithm are described, and numerical results are given for mapping an irregular two-dimensional finite element domain containing a singularity onto the Hypercube computer.
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[The utility boiler low NOx combustion optimization based on ANN and simulated annealing algorithm].
Zhou, Hao; Qian, Xinping; Zheng, Ligang; Weng, Anxin; Cen, Kefa
2003-11-01
With the developing restrict environmental protection demand, more attention was paid on the low NOx combustion optimizing technology for its cheap and easy property. In this work, field experiments on the NOx emissions characteristics of a 600 MW coal-fired boiler were carried out, on the base of the artificial neural network (ANN) modeling, the simulated annealing (SA) algorithm was employed to optimize the boiler combustion to achieve a low NOx emissions concentration, and the combustion scheme was obtained. Two sets of SA parameters were adopted to find a better SA scheme, the result show that the parameters of T0 = 50 K, alpha = 0.6 can lead to a better optimizing process. This work can give the foundation of the boiler low NOx combustion on-line control technology.
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Quantum annealing with all-to-all connected nonlinear oscillators
Puri, Shruti; Andersen, Christian Kraglund; Grimsmo, Arne L.; Blais, Alexandre
2017-01-01
Quantum annealing aims at solving combinatorial optimization problems mapped to Ising interactions between quantum spins. Here, with the objective of developing a noise-resilient annealer, we propose a paradigm for quantum annealing with a scalable network of two-photon-driven Kerr-nonlinear resonators. Each resonator encodes an Ising spin in a robust degenerate subspace formed by two coherent states of opposite phases. A fully connected optimization problem is mapped to local fields driving the resonators, which are connected with only local four-body interactions. We describe an adiabatic annealing protocol in this system and analyse its performance in the presence of photon loss. Numerical simulations indicate substantial resilience to this noise channel, leading to a high success probability for quantum annealing. Finally, we propose a realistic circuit QED implementation of this promising platform for implementing a large-scale quantum Ising machine. PMID:28593952
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Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier
2009-01-01
The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989
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NASA Astrophysics Data System (ADS)
Orito, Yukiko; Yamamoto, Hisashi; Tsujimura, Yasuhiro; Kambayashi, Yasushi
The portfolio optimizations are to determine the proportion-weighted combination in the portfolio in order to achieve investment targets. This optimization is one of the multi-dimensional combinatorial optimizations and it is difficult for the portfolio constructed in the past period to keep its performance in the future period. In order to keep the good performances of portfolios, we propose the extended information ratio as an objective function, using the information ratio, beta, prime beta, or correlation coefficient in this paper. We apply the simulated annealing (SA) to optimize the portfolio employing the proposed ratio. For the SA, we make the neighbor by the operation that changes the structure of the weights in the portfolio. In the numerical experiments, we show that our portfolios keep the good performances when the market trend of the future period becomes different from that of the past period.
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Epstein, F H; Mugler, J P; Brookeman, J R
1994-02-01
A number of pulse sequence techniques, including magnetization-prepared gradient echo (MP-GRE), segmented GRE, and hybrid RARE, employ a relatively large number of variable pulse sequence parameters and acquire the image data during a transient signal evolution. These sequences have recently been proposed and/or used for clinical applications in the brain, spine, liver, and coronary arteries. Thus, the need for a method of deriving optimal pulse sequence parameter values for this class of sequences now exists. Due to the complexity of these sequences, conventional optimization approaches, such as applying differential calculus to signal difference equations, are inadequate. We have developed a general framework for adapting the simulated annealing algorithm to pulse sequence parameter value optimization, and applied this framework to the specific case of optimizing the white matter-gray matter signal difference for a T1-weighted variable flip angle 3D MP-RAGE sequence. Using our algorithm, the values of 35 sequence parameters, including the magnetization-preparation RF pulse flip angle and delay time, 32 flip angles in the variable flip angle gradient-echo acquisition sequence, and the magnetization recovery time, were derived. Optimized 3D MP-RAGE achieved up to a 130% increase in white matter-gray matter signal difference compared with optimized 3D RF-spoiled FLASH with the same total acquisition time. The simulated annealing approach was effective at deriving optimal parameter values for a specific 3D MP-RAGE imaging objective, and may be useful for other imaging objectives and sequences in this general class.
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On simulated annealing phase transitions in phylogeny reconstruction.
Strobl, Maximilian A R; Barker, Daniel
2016-08-01
Phylogeny reconstruction with global criteria is NP-complete or NP-hard, hence in general requires a heuristic search. We investigate the powerful, physically inspired, general-purpose heuristic simulated annealing, applied to phylogeny reconstruction. Simulated annealing mimics the physical process of annealing, where a liquid is gently cooled to form a crystal. During the search, periods of elevated specific heat occur, analogous to physical phase transitions. These simulated annealing phase transitions play a crucial role in the outcome of the search. Nevertheless, they have received comparably little attention, for phylogeny or other optimisation problems. We analyse simulated annealing phase transitions during searches for the optimal phylogenetic tree for 34 real-world multiple alignments. In the same way in which melting temperatures differ between materials, we observe distinct specific heat profiles for each input file. We propose this reflects differences in the search landscape and can serve as a measure for problem difficulty and for suitability of the algorithm's parameters. We discuss application in algorithmic optimisation and as a diagnostic to assess parameterisation before computationally costly, large phylogeny reconstructions are launched. Whilst the focus here lies on phylogeny reconstruction under maximum parsimony, it is plausible that our results are more widely applicable to optimisation procedures in science and industry. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
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NASA Astrophysics Data System (ADS)
Louie, J. N.; Basler-Reeder, K.; Kent, G. M.; Pullammanappallil, S. K.
2015-12-01
Simultaneous joint seismic-gravity optimization improves P-wave velocity models in areas with sharp lateral velocity contrasts. Optimization is achieved using simulated annealing, a metaheuristic global optimization algorithm that does not require an accurate initial model. Balancing the seismic-gravity objective function is accomplished by a novel approach based on analysis of Pareto charts. Gravity modeling uses a newly developed convolution algorithm, while seismic modeling utilizes the highly efficient Vidale eikonal equation traveltime generation technique. Synthetic tests show that joint optimization improves velocity model accuracy and provides velocity control below the deepest headwave raypath. Detailed first arrival picking followed by trial velocity modeling remediates inconsistent data. We use a set of highly refined first arrival picks to compare results of a convergent joint seismic-gravity optimization to the Plotrefa™ and SeisOpt® Pro™ velocity modeling packages. Plotrefa™ uses a nonlinear least squares approach that is initial model dependent and produces shallow velocity artifacts. SeisOpt® Pro™ utilizes the simulated annealing algorithm and is limited to depths above the deepest raypath. Joint optimization increases the depth of constrained velocities, improving reflector coherency at depth. Kirchoff prestack depth migrations reveal that joint optimization ameliorates shallow velocity artifacts caused by limitations in refraction ray coverage. Seismic and gravity data from the San Emidio Geothermal field of the northwest Basin and Range province demonstrate that joint optimization changes interpretation outcomes. The prior shallow-valley interpretation gives way to a deep valley model, while shallow antiformal reflectors that could have been interpreted as antiformal folds are flattened. Furthermore, joint optimization provides a clearer image of the rangefront fault. This technique can readily be applied to existing datasets and could replace the existing strategy of forward modeling to match gravity data.
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SU-F-BRD-13: Quantum Annealing Applied to IMRT Beamlet Intensity Optimization
DOE Office of Scientific and Technical Information (OSTI.GOV)
Nazareth, D; Spaans, J
Purpose: We report on the first application of quantum annealing (QA) to the process of beamlet intensity optimization for IMRT. QA is a new technology, which employs novel hardware and software techniques to address various discrete optimization problems in many fields. Methods: We apply the D-Wave Inc. proprietary hardware, which natively exploits quantum mechanical effects for improved optimization. The new QA algorithm, running on this hardware, is most similar to simulated annealing, but relies on natural processes to directly minimize the free energy of a system. A simple quantum system is slowly evolved into a classical system, representing the objectivemore » function. To apply QA to IMRT-type optimization, two prostate cases were considered. A reduced number of beamlets were employed, due to the current QA hardware limitation of ∼500 binary variables. The beamlet dose matrices were computed using CERR, and an objective function was defined based on typical clinical constraints, including dose-volume objectives. The objective function was discretized, and the QA method was compared to two standard optimization Methods: simulated annealing and Tabu search, run on a conventional computing cluster. Results: Based on several runs, the average final objective function value achieved by the QA was 16.9 for the first patient, compared with 10.0 for Tabu and 6.7 for the SA. For the second patient, the values were 70.7 for the QA, 120.0 for Tabu, and 22.9 for the SA. The QA algorithm required 27–38% of the time required by the other two methods. Conclusion: In terms of objective function value, the QA performance was similar to Tabu but less effective than the SA. However, its speed was 3–4 times faster than the other two methods. This initial experiment suggests that QA-based heuristics may offer significant speedup over conventional clinical optimization methods, as quantum annealing hardware scales to larger sizes.« less
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NASA Astrophysics Data System (ADS)
Soltani-Mohammadi, Saeed; Safa, Mohammad; Mokhtari, Hadi
2016-10-01
One of the most important stages in complementary exploration is optimal designing the additional drilling pattern or defining the optimum number and location of additional boreholes. Quite a lot research has been carried out in this regard in which for most of the proposed algorithms, kriging variance minimization as a criterion for uncertainty assessment is defined as objective function and the problem could be solved through optimization methods. Although kriging variance implementation is known to have many advantages in objective function definition, it is not sensitive to local variability. As a result, the only factors evaluated for locating the additional boreholes are initial data configuration and variogram model parameters and the effects of local variability are omitted. In this paper, with the goal of considering the local variability in boundaries uncertainty assessment, the application of combined variance is investigated to define the objective function. Thus in order to verify the applicability of the proposed objective function, it is used to locate the additional boreholes in Esfordi phosphate mine through the implementation of metaheuristic optimization methods such as simulated annealing and particle swarm optimization. Comparison of results from the proposed objective function and conventional methods indicates that the new changes imposed on the objective function has caused the algorithm output to be sensitive to the variations of grade, domain's boundaries and the thickness of mineralization domain. The comparison between the results of different optimization algorithms proved that for the presented case the application of particle swarm optimization is more appropriate than simulated annealing.
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Discrete-State Simulated Annealing For Traveling-Wave Tube Slow-Wave Circuit Optimization
NASA Technical Reports Server (NTRS)
Wilson, Jeffrey D.; Bulson, Brian A.; Kory, Carol L.; Williams, W. Dan (Technical Monitor)
2001-01-01
Algorithms based on the global optimization technique of simulated annealing (SA) have proven useful in designing traveling-wave tube (TWT) slow-wave circuits for high RF power efficiency. The characteristic of SA that enables it to determine a globally optimized solution is its ability to accept non-improving moves in a controlled manner. In the initial stages of the optimization, the algorithm moves freely through configuration space, accepting most of the proposed designs. This freedom of movement allows non-intuitive designs to be explored rather than restricting the optimization to local improvement upon the initial configuration. As the optimization proceeds, the rate of acceptance of non-improving moves is gradually reduced until the algorithm converges to the optimized solution. The rate at which the freedom of movement is decreased is known as the annealing or cooling schedule of the SA algorithm. The main disadvantage of SA is that there is not a rigorous theoretical foundation for determining the parameters of the cooling schedule. The choice of these parameters is highly problem dependent and the designer needs to experiment in order to determine values that will provide a good optimization in a reasonable amount of computational time. This experimentation can absorb a large amount of time especially when the algorithm is being applied to a new type of design. In order to eliminate this disadvantage, a variation of SA known as discrete-state simulated annealing (DSSA), was recently developed. DSSA provides the theoretical foundation for a generic cooling schedule which is problem independent, Results of similar quality to SA can be obtained, but without the extra computational time required to tune the cooling parameters. Two algorithm variations based on DSSA were developed and programmed into a Microsoft Excel spreadsheet graphical user interface (GUI) to the two-dimensional nonlinear multisignal helix traveling-wave amplifier analysis program TWA3. The algorithms were used to optimize the computed RF efficiency of a TWT by determining the phase velocity profile of the slow-wave circuit. The mathematical theory and computational details of the DSSA algorithms will be presented and results will be compared to those obtained with a SA algorithm.
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Lee, Jong-Seok; Park, Cheol Hoon
2010-08-01
We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectation-maximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments.
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Yao, Rui; Templeton, Alistair K; Liao, Yixiang; Turian, Julius V; Kiel, Krystyna D; Chu, James C H
2014-01-01
To validate an in-house optimization program that uses adaptive simulated annealing (ASA) and gradient descent (GD) algorithms and investigate features of physical dose and generalized equivalent uniform dose (gEUD)-based objective functions in high-dose-rate (HDR) brachytherapy for cervical cancer. Eight Syed/Neblett template-based cervical cancer HDR interstitial brachytherapy cases were used for this study. Brachytherapy treatment plans were first generated using inverse planning simulated annealing (IPSA). Using the same dwell positions designated in IPSA, plans were then optimized with both physical dose and gEUD-based objective functions, using both ASA and GD algorithms. Comparisons were made between plans both qualitatively and based on dose-volume parameters, evaluating each optimization method and objective function. A hybrid objective function was also designed and implemented in the in-house program. The ASA plans are higher on bladder V75% and D2cc (p=0.034) and lower on rectum V75% and D2cc (p=0.034) than the IPSA plans. The ASA and GD plans are not significantly different. The gEUD-based plans have higher homogeneity index (p=0.034), lower overdose index (p=0.005), and lower rectum gEUD and normal tissue complication probability (p=0.005) than the physical dose-based plans. The hybrid function can produce a plan with dosimetric parameters between the physical dose-based and gEUD-based plans. The optimized plans with the same objective value and dose-volume histogram could have different dose distributions. Our optimization program based on ASA and GD algorithms is flexible on objective functions, optimization parameters, and can generate optimized plans comparable with IPSA. Copyright © 2014 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.
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NASA Astrophysics Data System (ADS)
Trivedi, R. R.; Joglekar, M. M.; Shimpi, R. P.; Pawaskar, D. N.
2013-12-01
The objective of this paper is to present a systematic development of the generic shape optimization of elec- trostatically actuated microcantilever beams for extending their static travel range. Electrostatic actuators are widely used in micro electro mechanical system (MEMS) devices because of low power density and ease of fab- rication. However, their useful travel range is often restricted by a phenomenon known as pull-in instability. The Rayleigh- Ritz energy method is used for computation of pull-in parameters which includes electrostatic potential and fringing field effect. Appropriate width function and linear thickness functions are employed along the length of the non-prismatic beam to achieve enhanced travel range. Parameters used for varying the thick- ness and width functions are optimized using simulated annealing with pattern search method towards the end to refine the results. Appropriate penalties are imposed on the violation of volume, width, thickness and area constraints. Nine test cases are considered for demonstration of the said optimization method. Our results indicate that around 26% increase in the travel range of a non-prismatic beam can be achieved after optimiza- tion compared to that in a prismatic beam having the same volume. Our results also show an improvement in the pull-in displacement of around 5% compared to that of a variable width constant thickness actuator. We show that simulated annealing is an effective and flexible method to carry out design optimization of structural elements under electrostatic loading.
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NASA Astrophysics Data System (ADS)
Ellaby, Tom; Aarons, Jolyon; Varambhia, Aakash; Jones, Lewys; Nellist, Peter; Ozkaya, Dogan; Sarwar, Misbah; Thompsett, David; Skylaris, Chris-Kriton
2018-04-01
Platinum nanoparticles find significant use as catalysts in industrial applications such as fuel cells. Research into their design has focussed heavily on nanoparticle size and shape as they greatly influence activity. Using high throughput, high precision electron microscopy, the structures of commercially available Pt catalysts have been determined, and we have used classical and quantum atomistic simulations to examine and compare them with geometric cuboctahedral and truncated octahedral structures. A simulated annealing procedure was used both to explore the potential energy surface at different temperatures, and also to assess the effect on catalytic activity that annealing would have on nanoparticles with different geometries and sizes. The differences in response to annealing between the real and geometric nanoparticles are discussed in terms of thermal stability, coordination number and the proportion of optimal binding sites on the surface of the nanoparticles. We find that annealing both experimental and geometric nanoparticles results in structures that appear similar in shape and predicted activity, using oxygen adsorption as a measure. Annealing is predicted to increase the catalytic activity in all cases except the truncated octahedra, where it has the opposite effect. As our simulations have been performed with a classical force field, we also assess its suitability to describe the potential energy of such nanoparticles by comparing with large scale density functional theory calculations.
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NASA Astrophysics Data System (ADS)
Nemirsky, Kristofer Kevin
In this thesis, the history and evolution of rotor aircraft with simulated annealing-based PID application were reviewed and quadcopter dynamics are presented. The dynamics of a quadcopter were then modeled, analyzed, and linearized. A cascaded loop architecture with PID controllers was used to stabilize the plant dynamics, which was improved upon through the application of simulated annealing (SA). A Simulink model was developed to test the controllers and verify the functionality of the proposed control system design. In addition, the data that the Simulink model provided were compared with flight data to present the validity of derived dynamics as a proper mathematical model representing the true dynamics of the quadcopter system. Then, the SA-based global optimization procedure was applied to obtain optimized PID parameters. It was observed that the tuned gains through the SA algorithm produced a better performing PID controller than the original manually tuned one. Next, we investigated the uncertain dynamics of the quadcopter setup. After adding uncertainty to the gyroscopic effects associated with pitch-and-roll rate dynamics, the controllers were shown to be robust against the added uncertainty. A discussion follows to summarize SA-based algorithm PID controller design and performance outcomes. Lastly, future work on SA application on multi-input-multi-output (MIMO) systems is briefly discussed.
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Towards global optimization with adaptive simulated annealing
NASA Astrophysics Data System (ADS)
Forbes, Gregory W.; Jones, Andrew E.
1991-01-01
The structure of the simulated annealing algorithm is presented and its rationale is discussed. A unifying heuristic is then introduced which serves as a guide in the design of all of the sub-components of the algorithm. Simply put this heuristic principle states that at every cycle in the algorithm the occupation density should be kept as close as possible to the equilibrium distribution. This heuristic has been used as a guide to develop novel step generation and temperature control methods intended to improve the efficiency of the simulated annealing algorithm. The resulting algorithm has been used in attempts to locate good solutions for one of the lens design problems associated with this conference viz. the " monochromatic quartet" and a sample of the results is presented. 1 Global optimization in the context oflens design Whatever the context optimization algorithms relate to problems that take the following form: Given some configuration space with coordinates r (x1 . . x) and a merit function written asffr) find the point r whereftr) takes it lowest value. That is find the global minimum. In many cases there is also a set of auxiliary constraints that must be met so the problem statement becomes: Find the global minimum of the merit function within the region defined by E. (r) 0 j 1 2 . . . p and 0 j 1 2 . . . q.
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Minimizing distortion and internal forces in truss structures by simulated annealing
NASA Technical Reports Server (NTRS)
Kincaid, Rex K.; Padula, Sharon L.
1990-01-01
Inaccuracies in the length of members and the diameters of joints of large space structures may produce unacceptable levels of surface distortion and internal forces. Here, two discrete optimization problems are formulated, one to minimize surface distortion (DSQRMS) and the other to minimize internal forces (FSQRMS). Both of these problems are based on the influence matrices generated by a small-deformation linear analysis. Good solutions are obtained for DSQRMS and FSQRMS through the use of a simulated annealing heuristic.
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Memoryless cooperative graph search based on the simulated annealing algorithm
NASA Astrophysics Data System (ADS)
Hou, Jian; Yan, Gang-Feng; Fan, Zhen
2011-04-01
We have studied the problem of reaching a globally optimal segment for a graph-like environment with a single or a group of autonomous mobile agents. Firstly, two efficient simulated-annealing-like algorithms are given for a single agent to solve the problem in a partially known environment and an unknown environment, respectively. It shows that under both proposed control strategies, the agent will eventually converge to a globally optimal segment with probability 1. Secondly, we use multi-agent searching to simultaneously reduce the computation complexity and accelerate convergence based on the algorithms we have given for a single agent. By exploiting graph partition, a gossip-consensus method based scheme is presented to update the key parameter—radius of the graph, ensuring that the agents spend much less time finding a globally optimal segment.
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Scheduling Earth Observing Satellites with Evolutionary Algorithms
NASA Technical Reports Server (NTRS)
Globus, Al; Crawford, James; Lohn, Jason; Pryor, Anna
2003-01-01
We hypothesize that evolutionary algorithms can effectively schedule coordinated fleets of Earth observing satellites. The constraints are complex and the bottlenecks are not well understood, a condition where evolutionary algorithms are often effective. This is, in part, because evolutionary algorithms require only that one can represent solutions, modify solutions, and evaluate solution fitness. To test the hypothesis we have developed a representative set of problems, produced optimization software (in Java) to solve them, and run experiments comparing techniques. This paper presents initial results of a comparison of several evolutionary and other optimization techniques; namely the genetic algorithm, simulated annealing, squeaky wheel optimization, and stochastic hill climbing. We also compare separate satellite vs. integrated scheduling of a two satellite constellation. While the results are not definitive, tests to date suggest that simulated annealing is the best search technique and integrated scheduling is superior.
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Differential evolution-simulated annealing for multiple sequence alignment
NASA Astrophysics Data System (ADS)
Addawe, R. C.; Addawe, J. M.; Sueño, M. R. K.; Magadia, J. C.
2017-10-01
Multiple sequence alignments (MSA) are used in the analysis of molecular evolution and sequence structure relationships. In this paper, a hybrid algorithm, Differential Evolution - Simulated Annealing (DESA) is applied in optimizing multiple sequence alignments (MSAs) based on structural information, non-gaps percentage and totally conserved columns. DESA is a robust algorithm characterized by self-organization, mutation, crossover, and SA-like selection scheme of the strategy parameters. Here, the MSA problem is treated as a multi-objective optimization problem of the hybrid evolutionary algorithm, DESA. Thus, we name the algorithm as DESA-MSA. Simulated sequences and alignments were generated to evaluate the accuracy and efficiency of DESA-MSA using different indel sizes, sequence lengths, deletion rates and insertion rates. The proposed hybrid algorithm obtained acceptable solutions particularly for the MSA problem evaluated based on the three objectives.
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NASA Astrophysics Data System (ADS)
Szu, Harold H.
1993-09-01
Classical artificial neural networks (ANN) and neurocomputing are reviewed for implementing a real time medical image diagnosis. An algorithm known as the self-reference matched filter that emulates the spatio-temporal integration ability of the human visual system might be utilized for multi-frame processing of medical imaging data. A Cauchy machine, implementing a fast simulated annealing schedule, can determine the degree of abnormality by the degree of orthogonality between the patient imagery and the class of features of healthy persons. An automatic inspection process based on multiple modality image sequences is simulated by incorporating the following new developments: (1) 1-D space-filling Peano curves to preserve the 2-D neighborhood pixels' relationship; (2) fast simulated Cauchy annealing for the global optimization of self-feature extraction; and (3) a mini-max energy function for the intra-inter cluster-segregation respectively useful for top-down ANN designs.
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Solving Set Cover with Pairs Problem using Quantum Annealing
NASA Astrophysics Data System (ADS)
Cao, Yudong; Jiang, Shuxian; Perouli, Debbie; Kais, Sabre
2016-09-01
Here we consider using quantum annealing to solve Set Cover with Pairs (SCP), an NP-hard combinatorial optimization problem that plays an important role in networking, computational biology, and biochemistry. We show an explicit construction of Ising Hamiltonians whose ground states encode the solution of SCP instances. We numerically simulate the time-dependent Schrödinger equation in order to test the performance of quantum annealing for random instances and compare with that of simulated annealing. We also discuss explicit embedding strategies for realizing our Hamiltonian construction on the D-wave type restricted Ising Hamiltonian based on Chimera graphs. Our embedding on the Chimera graph preserves the structure of the original SCP instance and in particular, the embedding for general complete bipartite graphs and logical disjunctions may be of broader use than that the specific problem we deal with.
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Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem
Akutsah, Francis; Olusanya, Micheal O.; Adewumi, Aderemi O.
2018-01-01
The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calculation of the probability of acceptance criteria for the simulated annealing based techniques is computationally expensive. Therefore, in order to maximize the performance of the intelligent water drop algorithm using simulated annealing, a better way of calculating the probability of acceptance criteria is considered. The performance of the proposed hybrid algorithm is evaluated by using 33 standard test problems, with the results obtained compared with the solutions offered by four well-known techniques from the subject literature. Experimental results and statistical tests show that the new method possesses outstanding performance in terms of solution quality and runtime consumed. In addition, the proposed algorithm is suitable for solving large-scale problems. PMID:29554662
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Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem.
Ezugwu, Absalom E; Akutsah, Francis; Olusanya, Micheal O; Adewumi, Aderemi O
2018-01-01
The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calculation of the probability of acceptance criteria for the simulated annealing based techniques is computationally expensive. Therefore, in order to maximize the performance of the intelligent water drop algorithm using simulated annealing, a better way of calculating the probability of acceptance criteria is considered. The performance of the proposed hybrid algorithm is evaluated by using 33 standard test problems, with the results obtained compared with the solutions offered by four well-known techniques from the subject literature. Experimental results and statistical tests show that the new method possesses outstanding performance in terms of solution quality and runtime consumed. In addition, the proposed algorithm is suitable for solving large-scale problems.
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Design of a composite filter realizable on practical spatial light modulators
NASA Technical Reports Server (NTRS)
Rajan, P. K.; Ramakrishnan, Ramachandran
1994-01-01
Hybrid optical correlator systems use two spatial light modulators (SLM's), one at the input plane and the other at the filter plane. Currently available SLM's such as the deformable mirror device (DMD) and liquid crystal television (LCTV) SLM's exhibit arbitrarily constrained operating characteristics. The pattern recognition filters designed with the assumption that the SLM's have ideal operating characteristic may not behave as expected when implemented on the DMD or LCTV SLM's. Therefore it is necessary to incorporate the SLM constraints in the design of the filters. In this report, an iterative method is developed for the design of an unconstrained minimum average correlation energy (MACE) filter. Then using this algorithm a new approach for the design of a SLM constrained distortion invariant filter in the presence of input SLM is developed. Two different optimization algorithms are used to maximize the objective function during filter synthesis, one based on the simplex method and the other based on the Hooke and Jeeves method. Also, the simulated annealing based filter design algorithm proposed by Khan and Rajan is refined and improved. The performance of the filter is evaluated in terms of its recognition/discrimination capabilities using computer simulations and the results are compared with a simulated annealing optimization based MACE filter. The filters are designed for different LCTV SLM's operating characteristics and the correlation responses are compared. The distortion tolerance and the false class image discrimination qualities of the filter are comparable to those of the simulated annealing based filter but the new filter design takes about 1/6 of the computer time taken by the simulated annealing filter design.
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An Improved SoC Test Scheduling Method Based on Simulated Annealing Algorithm
NASA Astrophysics Data System (ADS)
Zheng, Jingjing; Shen, Zhihang; Gao, Huaien; Chen, Bianna; Zheng, Weida; Xiong, Xiaoming
2017-02-01
In this paper, we propose an improved SoC test scheduling method based on simulated annealing algorithm (SA). It is our first to disorganize IP core assignment for each TAM to produce a new solution for SA, allocate TAM width for each TAM using greedy algorithm and calculate corresponding testing time. And accepting the core assignment according to the principle of simulated annealing algorithm and finally attain the optimum solution. Simultaneously, we run the test scheduling experiment with the international reference circuits provided by International Test Conference 2002(ITC’02) and the result shows that our algorithm is superior to the conventional integer linear programming algorithm (ILP), simulated annealing algorithm (SA) and genetic algorithm(GA). When TAM width reaches to 48,56 and 64, the testing time based on our algorithm is lesser than the classic methods and the optimization rates are 30.74%, 3.32%, 16.13% respectively. Moreover, the testing time based on our algorithm is very close to that of improved genetic algorithm (IGA), which is state-of-the-art at present.
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Optimization of conditions for thermal smoothing GaAs surfaces
NASA Astrophysics Data System (ADS)
Akhundov, I. O.; Kazantsev, D. M.; Kozhuhov, A. S.; Alperovich, V. L.
2018-03-01
GaAs thermal smoothing by annealing in conditions which are close to equilibrium between the surface and vapors of As and Ga was earlier proved to be effective for the step-terraced surface formation on epi-ready substrates with a small root-mean-square roughness (Rq ≤ 0.15 nm). In the present study, this technique is further developed in order to reduce the annealing duration and to smooth GaAs samples with a larger initial roughness. To this end, we proposed a two-stage anneal with the first high-temperature stage aimed at smoothing "coarse" relief features and the second stage focused on "fine" smoothing at a lower temperature. The optimal temperatures and durations of two-stage annealing are found by Monte Carlo simulations and adjusted after experimentation. It is proved that the temperature and duration of the first high-temperature stage are restricted by the surface roughening, which occurs due to deviations from equilibrium conditions.
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NASA Astrophysics Data System (ADS)
Hsiao, Ming-Chih; Su, Ling-Huey
2018-02-01
This research addresses the problem of scheduling hybrid machine types, in which one type is a two-machine flowshop and another type is a single machine. A job is either processed on the two-machine flowshop or on the single machine. The objective is to determine a production schedule for all jobs so as to minimize the makespan. The problem is NP-hard since the two parallel machines problem was proved to be NP-hard. Simulated annealing algorithms are developed to solve the problem optimally. A mixed integer programming (MIP) is developed and used to evaluate the performance for two SAs. Computational experiments demonstrate the efficiency of the simulated annealing algorithms, the quality of the simulated annealing algorithms will also be reported.
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spsann - optimization of sample patterns using spatial simulated annealing
NASA Astrophysics Data System (ADS)
Samuel-Rosa, Alessandro; Heuvelink, Gerard; Vasques, Gustavo; Anjos, Lúcia
2015-04-01
There are many algorithms and computer programs to optimize sample patterns, some private and others publicly available. A few have only been presented in scientific articles and text books. This dispersion and somewhat poor availability is holds back to their wider adoption and further development. We introduce spsann, a new R-package for the optimization of sample patterns using spatial simulated annealing. R is the most popular environment for data processing and analysis. Spatial simulated annealing is a well known method with widespread use to solve optimization problems in the soil and geo-sciences. This is mainly due to its robustness against local optima and easiness of implementation. spsann offers many optimizing criteria for sampling for variogram estimation (number of points or point-pairs per lag distance class - PPL), trend estimation (association/correlation and marginal distribution of the covariates - ACDC), and spatial interpolation (mean squared shortest distance - MSSD). spsann also includes the mean or maximum universal kriging variance (MUKV) as an optimizing criterion, which is used when the model of spatial variation is known. PPL, ACDC and MSSD were combined (PAN) for sampling when we are ignorant about the model of spatial variation. spsann solves this multi-objective optimization problem scaling the objective function values using their maximum absolute value or the mean value computed over 1000 random samples. Scaled values are aggregated using the weighted sum method. A graphical display allows to follow how the sample pattern is being perturbed during the optimization, as well as the evolution of its energy state. It is possible to start perturbing many points and exponentially reduce the number of perturbed points. The maximum perturbation distance reduces linearly with the number of iterations. The acceptance probability also reduces exponentially with the number of iterations. R is memory hungry and spatial simulated annealing is a computationally intensive method. As such, many strategies were used to reduce the computation time and memory usage: a) bottlenecks were implemented in C++, b) a finite set of candidate locations is used for perturbing the sample points, and c) data matrices are computed only once and then updated at each iteration instead of being recomputed. spsann is available at GitHub under a licence GLP Version 2.0 and will be further developed to: a) allow the use of a cost surface, b) implement other sensitive parts of the source code in C++, c) implement other optimizing criteria, d) allow to add or delete points to/from an existing point pattern.
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Overall Traveling-Wave-Tube Efficiency Improved By Optimized Multistage Depressed Collector Design
NASA Technical Reports Server (NTRS)
Vaden, Karl R.
2002-01-01
Depressed Collector Design The microwave traveling wave tube (TWT) is used widely for space communications and high-power airborne transmitting sources. One of the most important features in designing a TWT is overall efficiency. Yet, overall TWT efficiency is strongly dependent on the efficiency of the electron beam collector, particularly for high values of collector efficiency. For these reasons, the NASA Glenn Research Center developed an optimization algorithm based on simulated annealing to quickly design highly efficient multistage depressed collectors (MDC's). Simulated annealing is a strategy for solving highly nonlinear combinatorial optimization problems. Its major advantage over other methods is its ability to avoid becoming trapped in local minima. Simulated annealing is based on an analogy to statistical thermodynamics, specifically the physical process of annealing: heating a material to a temperature that permits many atomic rearrangements and then cooling it carefully and slowly, until it freezes into a strong, minimum-energy crystalline structure. This minimum energy crystal corresponds to the optimal solution of a mathematical optimization problem. The TWT used as a baseline for optimization was the 32-GHz, 10-W, helical TWT developed for the Cassini mission to Saturn. The method of collector analysis and design used was a 2-1/2-dimensional computational procedure that employs two types of codes, a large signal analysis code and an electron trajectory code. The large signal analysis code produces the spatial, energetic, and temporal distributions of the spent beam entering the MDC. An electron trajectory code uses the resultant data to perform the actual collector analysis. The MDC was optimized for maximum MDC efficiency and minimum final kinetic energy of all collected electrons (to reduce heat transfer). The preceding figure shows the geometric and electrical configuration of an optimized collector with an efficiency of 93.8 percent. The results show the improvement in collector efficiency from 89.7 to 93.8 percent, resulting in an increase of three overall efficiency points. In addition, the time to design a highly efficient MDC was reduced from a month to a few days. All work was done in-house at Glenn for the High Rate Data Delivery Program. Future plans include optimizing the MDC and TWT interaction circuit in tandem to further improve overall TWT efficiency.
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NASA Astrophysics Data System (ADS)
Karimi, Hamed; Rosenberg, Gili; Katzgraber, Helmut G.
2017-10-01
We present and apply a general-purpose, multistart algorithm for improving the performance of low-energy samplers used for solving optimization problems. The algorithm iteratively fixes the value of a large portion of the variables to values that have a high probability of being optimal. The resulting problems are smaller and less connected, and samplers tend to give better low-energy samples for these problems. The algorithm is trivially parallelizable since each start in the multistart algorithm is independent, and could be applied to any heuristic solver that can be run multiple times to give a sample. We present results for several classes of hard problems solved using simulated annealing, path-integral quantum Monte Carlo, parallel tempering with isoenergetic cluster moves, and a quantum annealer, and show that the success metrics and the scaling are improved substantially. When combined with this algorithm, the quantum annealer's scaling was substantially improved for native Chimera graph problems. In addition, with this algorithm the scaling of the time to solution of the quantum annealer is comparable to the Hamze-de Freitas-Selby algorithm on the weak-strong cluster problems introduced by Boixo et al. Parallel tempering with isoenergetic cluster moves was able to consistently solve three-dimensional spin glass problems with 8000 variables when combined with our method, whereas without our method it could not solve any.
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Bai, Mingsian R; Hsieh, Ping-Ju; Hur, Kur-Nan
2009-02-01
The performance of the minimum mean-square error noise reduction (MMSE-NR) algorithm in conjunction with time-recursive averaging (TRA) for noise estimation is found to be very sensitive to the choice of two recursion parameters. To address this problem in a more systematic manner, this paper proposes an optimization method to efficiently search the optimal parameters of the MMSE-TRA-NR algorithms. The objective function is based on a regression model, whereas the optimization process is carried out with the simulated annealing algorithm that is well suited for problems with many local optima. Another NR algorithm proposed in the paper employs linear prediction coding as a preprocessor for extracting the correlated portion of human speech. Objective and subjective tests were undertaken to compare the optimized MMSE-TRA-NR algorithm with several conventional NR algorithms. The results of subjective tests were processed by using analysis of variance to justify the statistic significance. A post hoc test, Tukey's Honestly Significant Difference, was conducted to further assess the pairwise difference between the NR algorithms.
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UAV Mission Planning under Uncertainty
2006-06-01
algorithm , adapted from [13] . 57 4-5 Robust Optimization considers only a subset of the feasible region . 61 5-1 Overview of simulation with parameter...incorporates the robust optimization method suggested by Bertsimas and Sim [12], and is solved with a standard Branch- and-Cut algorithm . The chapter... algorithms , and the heuristic methods of Local Search methods and Simulated Annealing. With each method, we attempt to give a review of research that has
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List-Based Simulated Annealing Algorithm for Traveling Salesman Problem.
Zhan, Shi-hua; Lin, Juan; Zhang, Ze-jun; Zhong, Yi-wen
2016-01-01
Simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. Parameters' setting is a key factor for its performance, but it is also a tedious work. To simplify parameters setting, we present a list-based simulated annealing (LBSA) algorithm to solve traveling salesman problem (TSP). LBSA algorithm uses a novel list-based cooling schedule to control the decrease of temperature. Specifically, a list of temperatures is created first, and then the maximum temperature in list is used by Metropolis acceptance criterion to decide whether to accept a candidate solution. The temperature list is adapted iteratively according to the topology of the solution space of the problem. The effectiveness and the parameter sensitivity of the list-based cooling schedule are illustrated through benchmark TSP problems. The LBSA algorithm, whose performance is robust on a wide range of parameter values, shows competitive performance compared with some other state-of-the-art algorithms.
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NASA Astrophysics Data System (ADS)
Doualle, T.; Gallais, L.; Cormont, P.; Donval, T.; Lamaignère, L.; Rullier, J. L.
2016-06-01
We investigate the effect of different heat treatments on the laser-induced damage probabilities of fused silica samples. Isothermal annealing in a furnace is applied, with different temperatures in the range 700-1100 °C and 12 h annealing time, to super-polished fused silica samples. The surface flatness and laser damage probabilities at 3 ns, 351 nm are measured before and after the different annealing procedures. We have found a significant improvement of the initial laser damage probabilities of the silica surface after annealing at 1050 °C for 12 h. A similar study has been conducted on CO2 laser-processed sites on the surface of the samples. Before and after annealing, we have studied the morphology of the sites, the evolution of residual stress, and the laser-induced damage threshold measured at 351 nm, 3 ns. In this case, we observe that the laser damage resistance of the laser created craters can reach the damage level of the bare fused silica surface after the annealing process, with a complete stress relieve. The obtained results are then compared to the case of local annealing process by CO2 laser irradiation during 1 s, and we found similar improvements in both cases. The different results obtained in the study are compared to numerical simulations made with a thermo-mechanical model based on finite-element method that allows the simulation of the isothermal or the local annealing process, the evolution of stress and fictive temperature. The simulation results were found to be very consistent with experimental observations for the stresses evolution after annealing and estimation of the heat affected area during laser-processing based on the density dependence with fictive temperature. Following this work, the temperature for local annealing should reach 1330-1470 °C for an optimized reduction of damage probability and be below the threshold for material removal, whereas furnace annealing should be kept below the annealing point to avoid sample deformation.
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Discrete Optimization of Electronic Hyperpolarizabilities in a Chemical Subspace
2009-05-01
molecular design. Methods for optimization in discrete spaces have been studied extensively and recently reviewed ( 5). Optimization methods include...integer programming, as in branch-and-bound techniques (including dead-end elimination [ 6]), simulated annealing ( 7), and genetic algorithms ( 8...These algorithms have found renewed interest and application in molecular and materials design (9- 12) . Recently, new approaches have been
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Deterministic quantum annealing expectation-maximization algorithm
NASA Astrophysics Data System (ADS)
Miyahara, Hideyuki; Tsumura, Koji; Sughiyama, Yuki
2017-11-01
Maximum likelihood estimation (MLE) is one of the most important methods in machine learning, and the expectation-maximization (EM) algorithm is often used to obtain maximum likelihood estimates. However, EM heavily depends on initial configurations and fails to find the global optimum. On the other hand, in the field of physics, quantum annealing (QA) was proposed as a novel optimization approach. Motivated by QA, we propose a quantum annealing extension of EM, which we call the deterministic quantum annealing expectation-maximization (DQAEM) algorithm. We also discuss its advantage in terms of the path integral formulation. Furthermore, by employing numerical simulations, we illustrate how DQAEM works in MLE and show that DQAEM moderate the problem of local optima in EM.
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Optimally Stopped Optimization
NASA Astrophysics Data System (ADS)
Vinci, Walter; Lidar, Daniel A.
2016-11-01
We combine the fields of heuristic optimization and optimal stopping. We propose a strategy for benchmarking randomized optimization algorithms that minimizes the expected total cost for obtaining a good solution with an optimal number of calls to the solver. To do so, rather than letting the objective function alone define a cost to be minimized, we introduce a further cost-per-call of the algorithm. We show that this problem can be formulated using optimal stopping theory. The expected cost is a flexible figure of merit for benchmarking probabilistic solvers that can be computed when the optimal solution is not known and that avoids the biases and arbitrariness that affect other measures. The optimal stopping formulation of benchmarking directly leads to a real-time optimal-utilization strategy for probabilistic optimizers with practical impact. We apply our formulation to benchmark simulated annealing on a class of maximum-2-satisfiability (MAX2SAT) problems. We also compare the performance of a D-Wave 2X quantum annealer to the Hamze-Freitas-Selby (HFS) solver, a specialized classical heuristic algorithm designed for low-tree-width graphs. On a set of frustrated-loop instances with planted solutions defined on up to N =1098 variables, the D-Wave device is 2 orders of magnitude faster than the HFS solver, and, modulo known caveats related to suboptimal annealing times, exhibits identical scaling with problem size.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Doualle, T.; Gallais, L., E-mail: laurent.gallais@fresnel.fr; Cormont, P.
We investigate the effect of different heat treatments on the laser-induced damage probabilities of fused silica samples. Isothermal annealing in a furnace is applied, with different temperatures in the range 700–1100 °C and 12 h annealing time, to super-polished fused silica samples. The surface flatness and laser damage probabilities at 3 ns, 351 nm are measured before and after the different annealing procedures. We have found a significant improvement of the initial laser damage probabilities of the silica surface after annealing at 1050 °C for 12 h. A similar study has been conducted on CO{sub 2} laser-processed sites on the surface of the samples. Before andmore » after annealing, we have studied the morphology of the sites, the evolution of residual stress, and the laser-induced damage threshold measured at 351 nm, 3 ns. In this case, we observe that the laser damage resistance of the laser created craters can reach the damage level of the bare fused silica surface after the annealing process, with a complete stress relieve. The obtained results are then compared to the case of local annealing process by CO{sub 2} laser irradiation during 1 s, and we found similar improvements in both cases. The different results obtained in the study are compared to numerical simulations made with a thermo-mechanical model based on finite-element method that allows the simulation of the isothermal or the local annealing process, the evolution of stress and fictive temperature. The simulation results were found to be very consistent with experimental observations for the stresses evolution after annealing and estimation of the heat affected area during laser-processing based on the density dependence with fictive temperature. Following this work, the temperature for local annealing should reach 1330–1470 °C for an optimized reduction of damage probability and be below the threshold for material removal, whereas furnace annealing should be kept below the annealing point to avoid sample deformation.« less
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Roux, Emmanuel; Ramalli, Alessandro; Tortoli, Piero; Cachard, Christian; Robini, Marc C; Liebgott, Herve
2016-12-01
Full matrix arrays are excellent tools for 3-D ultrasound imaging, but the required number of active elements is too high to be individually controlled by an equal number of scanner channels. The number of active elements is significantly reduced by the sparse array techniques, but the position of the remaining elements must be carefully optimized. This issue is faced here by introducing novel energy functions in the simulated annealing (SA) algorithm. At each iteration step of the optimization process, one element is freely translated and the associated radiated pattern is simulated. To control the pressure field behavior at multiple depths, three energy functions inspired by the pressure field radiated by a Blackman-tapered spiral array are introduced. Such energy functions aim at limiting the main lobe width while lowering the side lobe and grating lobe levels at multiple depths. Numerical optimization results illustrate the influence of the number of iterations, pressure measurement points, and depths, as well as the influence of the energy function definition on the optimized layout. It is also shown that performance close to or even better than the one provided by a spiral array, here assumed as reference, may be obtained. The finite-time convergence properties of SA allow the duration of the optimization process to be set in advance.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Pereira, Ana I.; ALGORITMI,University of Minho; Lima, José
There are several approaches to create the Humanoid robot gait planning. This problem presents a large number of unknown parameters that should be found to make the humanoid robot to walk. Optimization in simulation models can be used to find the gait based on several criteria such as energy minimization, acceleration, step length among the others. The energy consumption can also be reduced with elastic elements coupled to each joint. The presented paper addresses an optimization method, the Stretched Simulated Annealing, that runs in an accurate and stable simulation model to find the optimal gait combined with elastic elements. Finalmore » results demonstrate that optimization is a valid gait planning technique.« less
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NASA Astrophysics Data System (ADS)
Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.
2016-10-01
Accurate forecasting of dengue cases would significantly improve epidemic prevention and control capabilities. This paper attempts to provide useful models in forecasting dengue epidemic specific to the young and adult population of Baguio City. To capture the seasonal variations in dengue incidence, this paper develops a robust modeling approach to identify and estimate seasonal autoregressive integrated moving average (SARIMA) models in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on winsorized and reweighted least squares estimators. A hybrid algorithm, Differential Evolution - Simulated Annealing (DESA), is used to identify and estimate the parameters of the optimal SARIMA model. The method is applied to the monthly reported dengue cases in Baguio City, Philippines.
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Technique Developed for Optimizing Traveling-Wave Tubes
NASA Technical Reports Server (NTRS)
Wilson, Jeffrey D.
1999-01-01
A traveling-wave tube (TWT) is an electron beam device that is used to amplify electromagnetic communication waves at radio and microwave frequencies. TWT s are critical components in deep-space probes, geosynchronous communication satellites, and high-power radar systems. Power efficiency is of paramount importance for TWT s employed in deep-space probes and communications satellites. Consequently, increasing the power efficiency of TWT s has been the primary goal of the TWT group at the NASA Lewis Research Center over the last 25 years. An in-house effort produced a technique (ref. 1) to design TWT's for optimized power efficiency. This technique is based on simulated annealing, which has an advantage over conventional optimization techniques in that it enables the best possible solution to be obtained (ref. 2). A simulated annealing algorithm was created and integrated into the NASA TWT computer model (ref. 3). The new technique almost doubled the computed conversion power efficiency of a TWT from 7.1 to 13.5 percent (ref. 1).
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An adaptive evolutionary multi-objective approach based on simulated annealing.
Li, H; Landa-Silva, D
2011-01-01
A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search toward the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well established multi-objective metaheuristic algorithms on both the (constrained) multi-objective knapsack problem and the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated.
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Stochastic Evolutionary Algorithms for Planning Robot Paths
NASA Technical Reports Server (NTRS)
Fink, Wolfgang; Aghazarian, Hrand; Huntsberger, Terrance; Terrile, Richard
2006-01-01
A computer program implements stochastic evolutionary algorithms for planning and optimizing collision-free paths for robots and their jointed limbs. Stochastic evolutionary algorithms can be made to produce acceptably close approximations to exact, optimal solutions for path-planning problems while often demanding much less computation than do exhaustive-search and deterministic inverse-kinematics algorithms that have been used previously for this purpose. Hence, the present software is better suited for application aboard robots having limited computing capabilities (see figure). The stochastic aspect lies in the use of simulated annealing to (1) prevent trapping of an optimization algorithm in local minima of an energy-like error measure by which the fitness of a trial solution is evaluated while (2) ensuring that the entire multidimensional configuration and parameter space of the path-planning problem is sampled efficiently with respect to both robot joint angles and computation time. Simulated annealing is an established technique for avoiding local minima in multidimensional optimization problems, but has not, until now, been applied to planning collision-free robot paths by use of low-power computers.
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Wang, Junxiao; Wang, Xiaorui; Zhou, Shenglu; Wu, Shaohua; Zhu, Yan; Lu, Chunfeng
2016-01-01
With China’s rapid economic development, the reduction in arable land has emerged as one of the most prominent problems in the nation. The long-term dynamic monitoring of arable land quality is important for protecting arable land resources. An efficient practice is to select optimal sample points while obtaining accurate predictions. To this end, the selection of effective points from a dense set of soil sample points is an urgent problem. In this study, data were collected from Donghai County, Jiangsu Province, China. The number and layout of soil sample points are optimized by considering the spatial variations in soil properties and by using an improved simulated annealing (SA) algorithm. The conclusions are as follows: (1) Optimization results in the retention of more sample points in the moderate- and high-variation partitions of the study area; (2) The number of optimal sample points obtained with the improved SA algorithm is markedly reduced, while the accuracy of the predicted soil properties is improved by approximately 5% compared with the raw data; (3) With regard to the monitoring of arable land quality, a dense distribution of sample points is needed to monitor the granularity. PMID:27706051
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Ry, Rexha Verdhora, E-mail: rexha.vry@gmail.com; Nugraha, Andri Dian, E-mail: nugraha@gf.itb.ac.id
Observation of earthquakes is routinely used widely in tectonic activity observation, and also in local scale such as volcano tectonic and geothermal activity observation. It is necessary for determining the location of precise hypocenter which the process involves finding a hypocenter location that has minimum error between the observed and the calculated travel times. When solving this nonlinear inverse problem, simulated annealing inversion method can be applied to such global optimization problems, which the convergence of its solution is independent of the initial model. In this study, we developed own program codeby applying adaptive simulated annealing inversion in Matlab environment.more » We applied this method to determine earthquake hypocenter using several data cases which are regional tectonic, volcano tectonic, and geothermal field. The travel times were calculated using ray tracing shooting method. We then compared its results with the results using Geiger’s method to analyze its reliability. Our results show hypocenter location has smaller RMS error compared to the Geiger’s result that can be statistically associated with better solution. The hypocenter of earthquakes also well correlated with geological structure in the study area. Werecommend using adaptive simulated annealing inversion to relocate hypocenter location in purpose to get precise and accurate earthquake location.« less
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Self-Tuning of Design Variables for Generalized Predictive Control
NASA Technical Reports Server (NTRS)
Lin, Chaung; Juang, Jer-Nan
2000-01-01
Three techniques are introduced to determine the order and control weighting for the design of a generalized predictive controller. These techniques are based on the application of fuzzy logic, genetic algorithms, and simulated annealing to conduct an optimal search on specific performance indexes or objective functions. Fuzzy logic is found to be feasible for real-time and on-line implementation due to its smooth and quick convergence. On the other hand, genetic algorithms and simulated annealing are applicable for initial estimation of the model order and control weighting, and final fine-tuning within a small region of the solution space, Several numerical simulations for a multiple-input and multiple-output system are given to illustrate the techniques developed in this paper.
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Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng
2015-01-01
The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior. PMID:26000011
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Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng
2015-01-01
The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.
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List-Based Simulated Annealing Algorithm for Traveling Salesman Problem
Zhan, Shi-hua; Lin, Juan; Zhang, Ze-jun
2016-01-01
Simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. Parameters' setting is a key factor for its performance, but it is also a tedious work. To simplify parameters setting, we present a list-based simulated annealing (LBSA) algorithm to solve traveling salesman problem (TSP). LBSA algorithm uses a novel list-based cooling schedule to control the decrease of temperature. Specifically, a list of temperatures is created first, and then the maximum temperature in list is used by Metropolis acceptance criterion to decide whether to accept a candidate solution. The temperature list is adapted iteratively according to the topology of the solution space of the problem. The effectiveness and the parameter sensitivity of the list-based cooling schedule are illustrated through benchmark TSP problems. The LBSA algorithm, whose performance is robust on a wide range of parameter values, shows competitive performance compared with some other state-of-the-art algorithms. PMID:27034650
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Parallel computing of physical maps--a comparative study in SIMD and MIMD parallelism.
Bhandarkar, S M; Chirravuri, S; Arnold, J
1996-01-01
Ordering clones from a genomic library into physical maps of whole chromosomes presents a central computational problem in genetics. Chromosome reconstruction via clone ordering is usually isomorphic to the NP-complete Optimal Linear Arrangement problem. Parallel SIMD and MIMD algorithms for simulated annealing based on Markov chain distribution are proposed and applied to the problem of chromosome reconstruction via clone ordering. Perturbation methods and problem-specific annealing heuristics are proposed and described. The SIMD algorithms are implemented on a 2048 processor MasPar MP-2 system which is an SIMD 2-D toroidal mesh architecture whereas the MIMD algorithms are implemented on an 8 processor Intel iPSC/860 which is an MIMD hypercube architecture. A comparative analysis of the various SIMD and MIMD algorithms is presented in which the convergence, speedup, and scalability characteristics of the various algorithms are analyzed and discussed. On a fine-grained, massively parallel SIMD architecture with a low synchronization overhead such as the MasPar MP-2, a parallel simulated annealing algorithm based on multiple periodically interacting searches performs the best. For a coarse-grained MIMD architecture with high synchronization overhead such as the Intel iPSC/860, a parallel simulated annealing algorithm based on multiple independent searches yields the best results. In either case, distribution of clonal data across multiple processors is shown to exacerbate the tendency of the parallel simulated annealing algorithm to get trapped in a local optimum.
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NASA Astrophysics Data System (ADS)
Jia, F.; Lichti, D.
2017-09-01
The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn't guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.
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Han, Zong-wei; Huang, Wei; Luo, Yun; Zhang, Chun-di; Qi, Da-cheng
2015-03-01
Taking the soil organic matter in eastern Zhongxiang County, Hubei Province, as a research object, thirteen sample sets from different regions were arranged surrounding the road network, the spatial configuration of which was optimized by the simulated annealing approach. The topographic factors of these thirteen sample sets, including slope, plane curvature, profile curvature, topographic wetness index, stream power index and sediment transport index, were extracted by the terrain analysis. Based on the results of optimization, a multiple linear regression model with topographic factors as independent variables was built. At the same time, a multilayer perception model on the basis of neural network approach was implemented. The comparison between these two models was carried out then. The results revealed that the proposed approach was practicable in optimizing soil sampling scheme. The optimal configuration was capable of gaining soil-landscape knowledge exactly, and the accuracy of optimal configuration was better than that of original samples. This study designed a sampling configuration to study the soil attribute distribution by referring to the spatial layout of road network, historical samples, and digital elevation data, which provided an effective means as well as a theoretical basis for determining the sampling configuration and displaying spatial distribution of soil organic matter with low cost and high efficiency.
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2015-01-01
Procedure. The simulated annealing (SA) algorithm is a well-known local search metaheuristic used to address discrete, continuous, and multiobjective...design of experiments (DOE) to tune the parameters of the optimiza- tion algorithm . Section 5 shows the results of the case study. Finally, concluding... metaheuristic . The proposed method is broken down into two phases. Phase I consists of a Monte Carlo simulation to obtain the simulated percentage of failure
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NASA Astrophysics Data System (ADS)
Yelkenci Köse, Simge; Demir, Leyla; Tunalı, Semra; Türsel Eliiyi, Deniz
2015-02-01
In manufacturing systems, optimal buffer allocation has a considerable impact on capacity improvement. This study presents a simulation optimization procedure to solve the buffer allocation problem in a heat exchanger production plant so as to improve the capacity of the system. For optimization, three metaheuristic-based search algorithms, i.e. a binary-genetic algorithm (B-GA), a binary-simulated annealing algorithm (B-SA) and a binary-tabu search algorithm (B-TS), are proposed. These algorithms are integrated with the simulation model of the production line. The simulation model, which captures the stochastic and dynamic nature of the production line, is used as an evaluation function for the proposed metaheuristics. The experimental study with benchmark problem instances from the literature and the real-life problem show that the proposed B-TS algorithm outperforms B-GA and B-SA in terms of solution quality.
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Barca, E; Castrignanò, A; Buttafuoco, G; De Benedetto, D; Passarella, G
2015-07-01
Soil survey is generally time-consuming, labor-intensive, and costly. Optimization of sampling scheme allows one to reduce the number of sampling points without decreasing or even increasing the accuracy of investigated attribute. Maps of bulk soil electrical conductivity (EC a ) recorded with electromagnetic induction (EMI) sensors could be effectively used to direct soil sampling design for assessing spatial variability of soil moisture. A protocol, using a field-scale bulk EC a survey, has been applied in an agricultural field in Apulia region (southeastern Italy). Spatial simulated annealing was used as a method to optimize spatial soil sampling scheme taking into account sampling constraints, field boundaries, and preliminary observations. Three optimization criteria were used. the first criterion (minimization of mean of the shortest distances, MMSD) optimizes the spreading of the point observations over the entire field by minimizing the expectation of the distance between an arbitrarily chosen point and its nearest observation; the second criterion (minimization of weighted mean of the shortest distances, MWMSD) is a weighted version of the MMSD, which uses the digital gradient of the grid EC a data as weighting function; and the third criterion (mean of average ordinary kriging variance, MAOKV) minimizes mean kriging estimation variance of the target variable. The last criterion utilizes the variogram model of soil water content estimated in a previous trial. The procedures, or a combination of them, were tested and compared in a real case. Simulated annealing was implemented by the software MSANOS able to define or redesign any sampling scheme by increasing or decreasing the original sampling locations. The output consists of the computed sampling scheme, the convergence time, and the cooling law, which can be an invaluable support to the process of sampling design. The proposed approach has found the optimal solution in a reasonable computation time. The use of bulk EC a gradient as an exhaustive variable, known at any node of an interpolation grid, has allowed the optimization of the sampling scheme, distinguishing among areas with different priority levels.
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NASA Astrophysics Data System (ADS)
Mousavi, Seyed Hosein; Nazemi, Ali; Hafezalkotob, Ashkan
2015-03-01
With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous optimization of multiple parameters. The problem is formulated analytically using the Nash equilibrium concept for games composed of large numbers of players having discrete and large strategy spaces. The solution methodology is based on a characterization of Nash equilibrium in terms of minima of a function and relies on a metaheuristic optimization approach to find these minima. This paper presents some metaheuristic algorithms to simulate how generators bid in the spot electricity market viewpoint of their profit maximization according to the other generators' strategies, such as genetic algorithm (GA), simulated annealing (SA) and hybrid simulated annealing genetic algorithm (HSAGA) and compares their results. As both GA and SA are generic search methods, HSAGA is also a generic search method. The model based on the actual data is implemented in a peak hour of Tehran's wholesale spot market in 2012. The results of the simulations show that GA outperforms SA and HSAGA on computing time, number of function evaluation and computing stability, as well as the results of calculated Nash equilibriums by GA are less various and different from each other than the other algorithms.
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NASA Astrophysics Data System (ADS)
Lima, José; Pereira, Ana I.; Costa, Paulo; Pinto, Andry; Costa, Pedro
2017-07-01
This paper describes an optimization procedure for a robot with 12 degrees of freedom avoiding the inverse kinematics problem, which is a hard task for this type of robot manipulator. This robot can be used to pick and place tasks in complex designs. Combining an accurate and fast direct kinematics model with optimization strategies, it is possible to achieve the joints angles for a desired end-effector position and orientation. The optimization methods stretched simulated annealing algorithm and genetic algorithm were used. The solutions found were validated using data originated by a real and by a simulated robot formed by 12 servomotors with a gripper.
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A graph-based watershed merging using fuzzy C-means and simulated annealing for image segmentation
NASA Astrophysics Data System (ADS)
Vadiveloo, Mogana; Abdullah, Rosni; Rajeswari, Mandava
2015-12-01
In this paper, we have addressed the issue of over-segmented regions produced in watershed by merging the regions using global feature. The global feature information is obtained from clustering the image in its feature space using Fuzzy C-Means (FCM) clustering. The over-segmented regions produced by performing watershed on the gradient of the image are then mapped to this global information in the feature space. Further to this, the global feature information is optimized using Simulated Annealing (SA). The optimal global feature information is used to derive the similarity criterion to merge the over-segmented watershed regions which are represented by the region adjacency graph (RAG). The proposed method has been tested on digital brain phantom simulated dataset to segment white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) soft tissues regions. The experiments showed that the proposed method performs statistically better, with average of 95.242% regions are merged, than the immersion watershed and average accuracy improvement of 8.850% in comparison with RAG-based immersion watershed merging using global and local features.
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NASA Astrophysics Data System (ADS)
Okazaki, Yuji; Uno, Takanori; Asai, Hideki
In this paper, we propose an optimization system with parallel processing for reducing electromagnetic interference (EMI) on electronic control unit (ECU). We adopt simulated annealing (SA), genetic algorithm (GA) and taboo search (TS) to seek optimal solutions, and a Spice-like circuit simulator to analyze common-mode current. Therefore, the proposed system can determine the adequate combinations of the parasitic inductance and capacitance values on printed circuit board (PCB) efficiently and practically, to reduce EMI caused by the common-mode current. Finally, we apply the proposed system to an example circuit to verify the validity and efficiency of the system.
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Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam SM, Jahangir
2017-01-01
As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems. PMID:28422080
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Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam Sm, Jahangir
2017-04-19
As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems.
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Minimizing distortion and internal forces in truss structures by simulated annealing
NASA Technical Reports Server (NTRS)
Kincaid, Rex K.
1989-01-01
Inaccuracies in the length of members and the diameters of joints of large truss reflector backup structures may produce unacceptable levels of surface distortion and member forces. However, if the member lengths and joint diameters can be measured accurately it is possible to configure the members and joints so that root-mean-square (rms) surface error and/or rms member forces is minimized. Following Greene and Haftka (1989) it is assumed that the force vector f is linearly proportional to the member length errors e(sub M) of dimension NMEMB (the number of members) and joint errors e(sub J) of dimension NJOINT (the number of joints), and that the best-fit displacement vector d is a linear function of f. Let NNODES denote the number of positions on the surface of the truss where error influences are measured. The solution of the problem is discussed. To classify, this problem was compared to a similar combinatorial optimization problem. In particular, when only the member length errors are considered, minimizing d(sup 2)(sub rms) is equivalent to the quadratic assignment problem. The quadratic assignment problem is a well known NP-complete problem in operations research literature. Hence minimizing d(sup 2)(sub rms) is is also an NP-complete problem. The focus of the research is the development of a simulated annealing algorithm to reduce d(sup 2)(sub rms). The plausibility of this technique is its recent success on a variety of NP-complete combinatorial optimization problems including the quadratic assignment problem. A physical analogy for simulated annealing is the way liquids freeze and crystallize. All computational experiments were done on a MicroVAX. The two interchange heuristic is very fast but produces widely varying results. The two and three interchange heuristic provides less variability in the final objective function values but runs much more slowly. Simulated annealing produced the best objective function values for every starting configuration and was faster than the two and three interchange heuristic.
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Angular filter refractometry analysis using simulated annealing.
Angland, P; Haberberger, D; Ivancic, S T; Froula, D H
2017-10-01
Angular filter refractometry (AFR) is a novel technique used to characterize the density profiles of laser-produced, long-scale-length plasmas [Haberberger et al., Phys. Plasmas 21, 056304 (2014)]. A new method of analysis for AFR images was developed using an annealing algorithm to iteratively converge upon a solution. A synthetic AFR image is constructed by a user-defined density profile described by eight parameters, and the algorithm systematically alters the parameters until the comparison is optimized. The optimization and statistical uncertainty calculation is based on the minimization of the χ 2 test statistic. The algorithm was successfully applied to experimental data of plasma expanding from a flat, laser-irradiated target, resulting in an average uncertainty in the density profile of 5%-20% in the region of interest.
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NASA Astrophysics Data System (ADS)
Joung, InSuk; Kim, Jong Yun; Gross, Steven P.; Joo, Keehyoung; Lee, Jooyoung
2018-02-01
Many problems in science and engineering can be formulated as optimization problems. One way to solve these problems is to develop tailored problem-specific approaches. As such development is challenging, an alternative is to develop good generally-applicable algorithms. Such algorithms are easy to apply, typically function robustly, and reduce development time. Here we provide a description for one such algorithm called Conformational Space Annealing (CSA) along with its python version, PyCSA. We previously applied it to many optimization problems including protein structure prediction and graph community detection. To demonstrate its utility, we have applied PyCSA to two continuous test functions, namely Ackley and Eggholder functions. In addition, in order to provide complete generality of PyCSA to any types of an objective function, we demonstrate the way PyCSA can be applied to a discrete objective function, namely a parameter optimization problem. Based on the benchmarking results of the three problems, the performance of CSA is shown to be better than or similar to the most popular optimization method, simulated annealing. For continuous objective functions, we found that, L-BFGS-B was the best performing local optimization method, while for a discrete objective function Nelder-Mead was the best. The current version of PyCSA can be run in parallel at the coarse grained level by calculating multiple independent local optimizations separately. The source code of PyCSA is available from http://lee.kias.re.kr.
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Elevated temperature strengthening of a melt spun austenitic steel by TiB2
NASA Technical Reports Server (NTRS)
Michal, G. M.; Glasgow, T. K.; Moore, T. J.
1986-01-01
Mechanical properties of an iron-based alloy containing (by wt pct) 33Ni, 2Al, 6Ti, and 2B (resulting in an alloy containing 10 vol pct TiB2) were evaluated by hardness and tensile testing. The alloy was cast as a ribbon using a dual 'free-jet' variation of Jech et al. (1984) method of chill-block melt-spinning against a copper wheel; to simulate thermal cycles the alloy ribbon would experience during compaction into shapes, various segments of the ribbon were annealed under a vacuum at temperatures ranging from 500 to 1150 C. The results show that maximum strengths at 650 and 760 C were developed in ribbons annealed at 1100 C; in these ribbons an optimal combination of grain coarsening with minimum TiB2 particle growth was observed. However, the elevated-temperature strength of the TiB2-strengthened alloy under optimal annealing conditions was poorer than that of conventional iron-based superalloys strengthened by gamma-prime precipitates.
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Xia, Yin; Liu, Dianfeng; Liu, Yaolin; He, Jianhua; Hong, Xiaofeng
2014-01-01
Alternative land use zoning scenarios provide guidance for sustainable land use controls. This study focused on an ecologically vulnerable catchment on the Loess Plateau in China, proposed a novel land use zoning model, and generated alternative zoning solutions to satisfy the various requirements of land use stakeholders and managers. This model combined multiple zoning objectives, i.e., maximum zoning suitability, maximum planning compatibility and maximum spatial compactness, with land use constraints by using goal programming technique, and employed a modified simulated annealing algorithm to search for the optimal zoning solutions. The land use zoning knowledge was incorporated into the initialisation operator and neighbourhood selection strategy of the simulated annealing algorithm to improve its efficiency. The case study indicates that the model is both effective and robust. Five optimal zoning scenarios of the study area were helpful for satisfying the requirements of land use controls in loess hilly regions, e.g., land use intensification, agricultural protection and environmental conservation. PMID:25170679
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Aziz, Mohd Khairul Bazli Mohd, E-mail: mkbazli@yahoo.com; Yusof, Fadhilah, E-mail: fadhilahy@utm.my; Daud, Zalina Mohd, E-mail: zalina@ic.utm.my
Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during themore » monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system.« less
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QSPIN: A High Level Java API for Quantum Computing Experimentation
NASA Technical Reports Server (NTRS)
Barth, Tim
2017-01-01
QSPIN is a high level Java language API for experimentation in QC models used in the calculation of Ising spin glass ground states and related quadratic unconstrained binary optimization (QUBO) problems. The Java API is intended to facilitate research in advanced QC algorithms such as hybrid quantum-classical solvers, automatic selection of constraint and optimization parameters, and techniques for the correction and mitigation of model and solution errors. QSPIN includes high level solver objects tailored to the D-Wave quantum annealing architecture that implement hybrid quantum-classical algorithms [Booth et al.] for solving large problems on small quantum devices, elimination of variables via roof duality, and classical computing optimization methods such as GPU accelerated simulated annealing and tabu search for comparison. A test suite of documented NP-complete applications ranging from graph coloring, covering, and partitioning to integer programming and scheduling are provided to demonstrate current capabilities.
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Annealing Ant Colony Optimization with Mutation Operator for Solving TSP.
Mohsen, Abdulqader M
2016-01-01
Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.
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System Design under Uncertainty: Evolutionary Optimization of the Gravity Probe-B Spacecraft
NASA Technical Reports Server (NTRS)
Pullen, Samuel P.; Parkinson, Bradford W.
1994-01-01
This paper discusses the application of evolutionary random-search algorithms (Simulated Annealing and Genetic Algorithms) to the problem of spacecraft design under performance uncertainty. Traditionally, spacecraft performance uncertainty has been measured by reliability. Published algorithms for reliability optimization are seldom used in practice because they oversimplify reality. The algorithm developed here uses random-search optimization to allow us to model the problem more realistically. Monte Carlo simulations are used to evaluate the objective function for each trial design solution. These methods have been applied to the Gravity Probe-B (GP-B) spacecraft being developed at Stanford University for launch in 1999, Results of the algorithm developed here for GP-13 are shown, and their implications for design optimization by evolutionary algorithms are discussed.
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Deist, T M; Gorissen, B L
2016-02-07
High-dose-rate brachytherapy is a tumor treatment method where a highly radioactive source is brought in close proximity to the tumor. In this paper we develop a simulated annealing algorithm to optimize the dwell times at preselected dwell positions to maximize tumor coverage under dose-volume constraints on the organs at risk. Compared to existing algorithms, our algorithm has advantages in terms of speed and objective value and does not require an expensive general purpose solver. Its success mainly depends on exploiting the efficiency of matrix multiplication and a careful selection of the neighboring states. In this paper we outline its details and make an in-depth comparison with existing methods using real patient data.
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Saha, Rajarshi; Muthuswamy, Jit
2007-06-01
We had earlier demonstrated the use of polysilicon microelectrodes for recording electrical activity from single neurons in vivo. Good machinability and compatibility with CMOS processing further make polysilicon an attractive interface material between biological environments on one hand and MEMS technology and digital circuits on the other hand. In this study, we focus on optimizing the polysilicon thin films for (a) electrical recording and (b) stimulation of single neurons by minimizing its electrochemical impedance spectra and maximizing its charge storage/injection capacity respectively. The structure-property relationships in ion-implanted (phosphorus) LPCVD polysilicon thin films under different annealing and doping conditions were carefully assessed during this optimization process. A 2D model of the polysilicon thin film consisting of 4 grains and 3 grain boundaries was constructed and the effect of grain size and grain boundaries on dc resistivity was simulated using device simulator ATLAS. Optimal processing conditions and doping concentrations resulted in a 10-fold decrease in electrochemical impedance from 1.1 kOmega to 0.1 kOmega at 1 kHz (area of polysilicon interface = 4.8 mm(2)). Subsequent characterizations showed that evolution of secondary grains within the polysilicon thin films at optimal doping and annealing conditions (10(21)/cm(3) of phosphorus and annealed at 1200 degrees C) was responsible for decreasing the impedance. Cyclic voltammetry studies demonstrated that charge storage properties of low doped (10(15)/cm(3)) thin films was 111.4 microC/cm(2) in phosphate buffered saline which compares well with platinum wires (approximately 50 microC/cm(2)) and the double-layered capacitance (C(dl)) could be sustained between -1 to 1 V before breakdown and hydrolysis. We conclude that polysilicon can be optimized for recording and stimulating single neurons and can be a valuable interface material between neurons and CMOS or MEMS devices.
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NASA Astrophysics Data System (ADS)
Tajuddin, Wan Ahmad
1994-02-01
Ease in finding the configuration at the global energy minimum in a symmetric neural network is important for combinatorial optimization problems. We carry out a comprehensive survey of available strategies for seeking global minima by comparing their performances in the binary representation problem. We recall our previous comparison of steepest descent with analog dynamics, genetic hill-climbing, simulated diffusion, simulated annealing, threshold accepting and simulated tunneling. To this, we add comparisons to other strategies including taboo search and one with field-ordered updating.
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Quantum Monte Carlo tunneling from quantum chemistry to quantum annealing
NASA Astrophysics Data System (ADS)
Mazzola, Guglielmo; Smelyanskiy, Vadim N.; Troyer, Matthias
2017-10-01
Quantum tunneling is ubiquitous across different fields, from quantum chemical reactions and magnetic materials to quantum simulators and quantum computers. While simulating the real-time quantum dynamics of tunneling is infeasible for high-dimensional systems, quantum tunneling also shows up in quantum Monte Carlo (QMC) simulations, which aim to simulate quantum statistics with resources growing only polynomially with the system size. Here we extend the recent results obtained for quantum spin models [Phys. Rev. Lett. 117, 180402 (2016), 10.1103/PhysRevLett.117.180402], and we study continuous-variable models for proton transfer reactions. We demonstrate that QMC simulations efficiently recover the scaling of ground-state tunneling rates due to the existence of an instanton path, which always connects the reactant state with the product. We discuss the implications of our results in the context of quantum chemical reactions and quantum annealing, where quantum tunneling is expected to be a valuable resource for solving combinatorial optimization problems.
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Adaptive temperature-accelerated dynamics
NASA Astrophysics Data System (ADS)
Shim, Yunsic; Amar, Jacques G.
2011-02-01
We present three adaptive methods for optimizing the high temperature Thigh on-the-fly in temperature-accelerated dynamics (TAD) simulations. In all three methods, the high temperature is adjusted periodically in order to maximize the performance. While in the first two methods the adjustment depends on the number of observed events, the third method depends on the minimum activation barrier observed so far and requires an a priori knowledge of the optimal high temperature T^{opt}_{high}(E_a) as a function of the activation barrier Ea for each accepted event. In order to determine the functional form of T^{opt}_{high}(E_a), we have carried out extensive simulations of submonolayer annealing on the (100) surface for a variety of metals (Ag, Cu, Ni, Pd, and Au). While the results for all five metals are different, when they are scaled with the melting temperature Tm, we find that they all lie on a single scaling curve. Similar results have also been obtained for (111) surfaces although in this case the scaling function is slightly different. In order to test the performance of all three methods, we have also carried out adaptive TAD simulations of Ag/Ag(100) annealing and growth at T = 80 K and compared with fixed high-temperature TAD simulations for different values of Thigh. We find that the performance of all three adaptive methods is typically as good as or better than that obtained in fixed high-temperature TAD simulations carried out using the effective optimal fixed high temperature. In addition, we find that the final high temperatures obtained in our adaptive TAD simulations are very close to our results for T^{opt}_{high}(E_a). The applicability of the adaptive methods to a variety of TAD simulations is also briefly discussed.
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Synthesizing optimal waste blends
DOE Office of Scientific and Technical Information (OSTI.GOV)
Narayan, V.; Diwekar, W.M.; Hoza, M.
Vitrification of tank wastes to form glass is a technique that will be used for the disposal of high-level waste at Hanford. Process and storage economics show that minimizing the total number of glass logs produced is the key to keeping cost as low as possible. The amount of glass produced can be reduced by blending of the wastes. The optimal way to combine the tanks to minimize the vole of glass can be determined from a discrete blend calculation. However, this problem results in a combinatorial explosion as the number of tanks increases. Moreover, the property constraints make thismore » problem highly nonconvex where many algorithms get trapped in local minima. In this paper the authors examine the use of different combinatorial optimization approaches to solve this problem. A two-stage approach using a combination of simulated annealing and nonlinear programming (NLP) is developed. The results of different methods such as the heuristics approach based on human knowledge and judgment, the mixed integer nonlinear programming (MINLP) approach with GAMS, and branch and bound with lower bound derived from the structure of the given blending problem are compared with this coupled simulated annealing and NLP approach.« less
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Application of Simulated Annealing and Related Algorithms to TWTA Design
NASA Technical Reports Server (NTRS)
Radke, Eric M.
2004-01-01
Simulated Annealing (SA) is a stochastic optimization algorithm used to search for global minima in complex design surfaces where exhaustive searches are not computationally feasible. The algorithm is derived by simulating the annealing process, whereby a solid is heated to a liquid state and then cooled slowly to reach thermodynamic equilibrium at each temperature. The idea is that atoms in the solid continually bond and re-bond at various quantum energy levels, and with sufficient cooling time they will rearrange at the minimum energy state to form a perfect crystal. The distribution of energy levels is given by the Boltzmann distribution: as temperature drops, the probability of the presence of high-energy bonds decreases. In searching for an optimal design, local minima and discontinuities are often present in a design surface. SA presents a distinct advantage over other optimization algorithms in its ability to escape from these local minima. Just as high-energy atomic configurations are visited in the actual annealing process in order to eventually reach the minimum energy state, in SA highly non-optimal configurations are visited in order to find otherwise inaccessible global minima. The SA algorithm produces a Markov chain of points in the design space at each temperature, with a monotonically decreasing temperature. A random point is started upon, and the objective function is evaluated at that point. A stochastic perturbation is then made to the parameters of the point to arrive at a proposed new point in the design space, at which the objection function is evaluated as well. If the change in objective function values (Delta)E is negative, the proposed new point is accepted. If (Delta)E is positive, the proposed new point is accepted according to the Metropolis criterion: rho((Delta)f) = exp((-Delta)E/T), where T is the temperature for the current Markov chain. The process then repeats for the remainder of the Markov chain, after which the temperature is decremented and the process repeats. Eventually (and hopefully), a near-globally optimal solution is attained as T approaches zero. Several exciting variants of SA have recently emerged, including Discrete-State Simulated Annealing (DSSA) and Simulated Tempering (ST). The DSSA algorithm takes the thermodynamic analogy one step further by categorizing objective function evaluations into discrete states. In doing so, many of the case-specific problems associated with fine-tuning the SA algorithm can be avoided; for example, theoretical approximations for the initial and final temperature can be derived independently of the case. In this manner, DSSA provides a scheme that is more robust with respect to widely differing design surfaces. ST differs from SA in that the temperature T becomes an additional random variable in the optimization. The system is also kept in equilibrium as the temperature changes, as opposed to the system being driven out of equilibrium as temperature changes in SA. ST is designed to overcome obstacles in design surfaces where numerous local minima are separated by high barriers. These algorithms are incorporated into the optimal design of the traveling-wave tube amplifier (TWTA). The area under scrutiny is the collector, in which it would be ideal to use negative potential to decelerate the spent electron beam to zero kinetic energy just as it reaches the collector surface. In reality this is not plausible due to a number of physical limitations, including repulsion and differing levels of kinetic energy among individual electrons. Instead, the collector is designed with multiple stages depressed below ground potential. The design of this multiple-stage collector is the optimization problem of interest. One remaining problem in SA and DSSA is the difficulty in determining when equilibrium has been reached so that the current Markov chain can be terminated. It has been suggested in recent literature that simulating the thermodynamic properties opecific heat, entropy, and internal energy from the Boltzmann distribution can provide good indicators of having reached equilibrium at a certain temperature. These properties are tested for their efficacy and implemented in SA and DSSA code with respect to TWTA collector optimization.
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Sparse approximation problem: how rapid simulated annealing succeeds and fails
NASA Astrophysics Data System (ADS)
Obuchi, Tomoyuki; Kabashima, Yoshiyuki
2016-03-01
Information processing techniques based on sparseness have been actively studied in several disciplines. Among them, a mathematical framework to approximately express a given dataset by a combination of a small number of basis vectors of an overcomplete basis is termed the sparse approximation. In this paper, we apply simulated annealing, a metaheuristic algorithm for general optimization problems, to sparse approximation in the situation where the given data have a planted sparse representation and noise is present. The result in the noiseless case shows that our simulated annealing works well in a reasonable parameter region: the planted solution is found fairly rapidly. This is true even in the case where a common relaxation of the sparse approximation problem, the G-relaxation, is ineffective. On the other hand, when the dimensionality of the data is close to the number of non-zero components, another metastable state emerges, and our algorithm fails to find the planted solution. This phenomenon is associated with a first-order phase transition. In the case of very strong noise, it is no longer meaningful to search for the planted solution. In this situation, our algorithm determines a solution with close-to-minimum distortion fairly quickly.
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Close packing in curved space by simulated annealing
NASA Astrophysics Data System (ADS)
Wille, L. T.
1987-12-01
The problem of packing spheres of a maximum radius on the surface of a four-dimensional hypersphere is considered. It is shown how near-optimal solutions can be obtained by packing soft spheres, modelled as classical particles interacting under an inverse power potential, followed by a subsequent hardening of the interaction. In order to avoid trapping in high-lying local minima, the simulated annealing method is used to optimise the soft-sphere packing. Several improvements over other work (based on local optimisation of random initial configurations of hard spheres) have been found. The freezing behaviour of this system is discussed as a function of particle number, softness of the potential and cooling rate. Apart from their geometric interest, these results are useful in the study of topological frustration, metallic glasses and quasicrystals.
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NASA Astrophysics Data System (ADS)
Bina, C. R.
An optimization algorithm based upon the method of simulated annealing is of utility in calculating equilibrium phase assemblages as functions of pressure, temperature, and chemical composi tion. Operating by analogy to the statistical mechanics of the chemical system, it is applicable both to problems of strict chemical equilibrium and to problems involving metastability. The method reproduces known phase diagrams and illustrates the expected thermal deflection of phase transitions in thermal models of subducting lithospheric slabs and buoyant mantle plumes. It reveals temperature-induced changes in phase transition sharpness and the stability of Fe-rich γ phase within an α+γ field in cold slab thermal models, and it suggests that transitions such as the possible breakdown of silicate perovskite to mixed oxides can amplify velocity anomalies.
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Parameter estimation of a pulp digester model with derivative-free optimization strategies
NASA Astrophysics Data System (ADS)
Seiça, João C.; Romanenko, Andrey; Fernandes, Florbela P.; Santos, Lino O.; Fernandes, Natércia C. P.
2017-07-01
The work concerns the parameter estimation in the context of the mechanistic modelling of a pulp digester. The problem is cast as a box bounded nonlinear global optimization problem in order to minimize the mismatch between the model outputs with the experimental data observed at a real pulp and paper plant. MCSFilter and Simulated Annealing global optimization methods were used to solve the optimization problem. While the former took longer to converge to the global minimum, the latter terminated faster at a significantly higher value of the objective function and, thus, failed to find the global solution.
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Optimizing Adversary Training and the Structure of the Navy Adversary Fleet
2013-09-01
ORGANIZATION REPORT NUMBER 9. SPONSORING /MONITORING AGENCY NAME(S) AND ADDRESS(ES) OPNAV N98 2000 Navy Pentagon , Room 5C469 Washington DC, 20350...an overhaul of existing computers and encryption in the range operations centers (CDR R. Van Diepen, OPNAV Simulator Requirements Officer, personal...1.0. Using a simulated annealing heuristic algorithm in conjunction with the utility assignments, CNA found, in order of priority, that the following
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Optimal placement of excitations and sensors for verification of large dynamical systems
NASA Technical Reports Server (NTRS)
Salama, M.; Rose, T.; Garba, J.
1987-01-01
The computationally difficult problem of the optimal placement of excitations and sensors to maximize the observed measurements is studied within the framework of combinatorial optimization, and is solved numerically using a variation of the simulated annealing heuristic algorithm. Results of numerical experiments including a square plate and a 960 degrees-of-freedom Control of Flexible Structure (COFS) truss structure, are presented. Though the algorithm produces suboptimal solutions, its generality and simplicity allow the treatment of complex dynamical systems which would otherwise be difficult to handle.
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Design and development of multilayer wideband antireflection coating and its annealing study
NASA Astrophysics Data System (ADS)
Jena, S.; Tokas, R. B.; Udupa, D. V.; Thakur, S.; Sahoo, N. K.
2018-04-01
Reflection loss occurs at the glass-air interface, limits performance of many optical devices such as eyeglass, camera lenses, and photovoltaic solar cells. Antireflection (AR) coating on the glass reduces the reflection loss and improves efficiency of such devices. In this paper, wideband AR coating in the visible region has been designed and developed using ZrO2-MgO/SiO2 multilayer. The thicknesses of individual thin layers are numerically optimized to get maximum transmission of the visible light. The optimized four thin layers have been deposited on BK7 glass substrate using electron beam evaporation technique. The measured transmission spectrum of the 4-layer AR coating is compared with that of simulated spectrum. The transmission of the single side AR coating increases by more than 3% as compared to that of bare glass substrate in the wavelength region of 470 nm - 810 nm. The wideband AR coating has been annealed at 200°C for 4 hours in ambient condition. The transmission of the AR coating decreases after the annealing, resulting degradation in its wideband AR characteristics.
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Simulated annealing algorithm for solving chambering student-case assignment problem
NASA Astrophysics Data System (ADS)
Ghazali, Saadiah; Abdul-Rahman, Syariza
2015-12-01
The problem related to project assignment problem is one of popular practical problem that appear nowadays. The challenge of solving the problem raise whenever the complexity related to preferences, the existence of real-world constraints and problem size increased. This study focuses on solving a chambering student-case assignment problem by using a simulated annealing algorithm where this problem is classified under project assignment problem. The project assignment problem is considered as hard combinatorial optimization problem and solving it using a metaheuristic approach is an advantage because it could return a good solution in a reasonable time. The problem of assigning chambering students to cases has never been addressed in the literature before. For the proposed problem, it is essential for law graduates to peruse in chambers before they are qualified to become legal counselor. Thus, assigning the chambering students to cases is a critically needed especially when involving many preferences. Hence, this study presents a preliminary study of the proposed project assignment problem. The objective of the study is to minimize the total completion time for all students in solving the given cases. This study employed a minimum cost greedy heuristic in order to construct a feasible initial solution. The search then is preceded with a simulated annealing algorithm for further improvement of solution quality. The analysis of the obtained result has shown that the proposed simulated annealing algorithm has greatly improved the solution constructed by the minimum cost greedy heuristic. Hence, this research has demonstrated the advantages of solving project assignment problem by using metaheuristic techniques.
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Air-gun signature modelling considering the influence of mechanical structure factors
NASA Astrophysics Data System (ADS)
Li, Guofa; Liu, Zhao; Wang, Jianhua; Cao, Mingqiang
2014-04-01
In marine seismic prospecting, as the air-gun array is usually composed of different types of air-guns, the signature modelling of different air-guns is particularly important to the array design. Different types of air-guns have different mechanical structures, which directly or indirectly affect the signatures. In order to simulate the influence of the mechanical structure, five parameters—the throttling constant, throttling power law exponent, mass release efficiency, fluid viscosity and heat transfer coefficient—are used in signature modelling. Through minimizing the energy relative error between the simulated and the measured signatures by the simulated annealing method, the five optimal parameters can be estimated. The method is tested in a field experiment, and the consistency between the simulated and the measured signatures is improved with the optimal parameters.
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Assignment Of Finite Elements To Parallel Processors
NASA Technical Reports Server (NTRS)
Salama, Moktar A.; Flower, Jon W.; Otto, Steve W.
1990-01-01
Elements assigned approximately optimally to subdomains. Mapping algorithm based on simulated-annealing concept used to minimize approximate time required to perform finite-element computation on hypercube computer or other network of parallel data processors. Mapping algorithm needed when shape of domain complicated or otherwise not obvious what allocation of elements to subdomains minimizes cost of computation.
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PID controller tuning using metaheuristic optimization algorithms for benchmark problems
NASA Astrophysics Data System (ADS)
Gholap, Vishal; Naik Dessai, Chaitali; Bagyaveereswaran, V.
2017-11-01
This paper contributes to find the optimal PID controller parameters using particle swarm optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The algorithms were developed through simulation of chemical process and electrical system and the PID controller is tuned. Here, two different fitness functions such as Integral Time Absolute Error and Time domain Specifications were chosen and applied on PSO, GA and SA while tuning the controller. The proposed Algorithms are implemented on two benchmark problems of coupled tank system and DC motor. Finally, comparative study has been done with different algorithms based on best cost, number of iterations and different objective functions. The closed loop process response for each set of tuned parameters is plotted for each system with each fitness function.
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An Evaluation of the Sniffer Global Optimization Algorithm Using Standard Test Functions
NASA Astrophysics Data System (ADS)
Butler, Roger A. R.; Slaminka, Edward E.
1992-03-01
The performance of Sniffer—a new global optimization algorithm—is compared with that of Simulated Annealing. Using the number of function evaluations as a measure of efficiency, the new algorithm is shown to be significantly better at finding the global minimum of seven standard test functions. Several of the test functions used have many local minima and very steep walls surrounding the global minimum. Such functions are intended to thwart global minimization algorithms.
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Lukashin, A V; Fuchs, R
2001-05-01
Cluster analysis of genome-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and samples. In the present paper, we focus on several important issues related to clustering algorithms that have not yet been fully studied. We describe a simple and robust algorithm for the clustering of temporal gene expression profiles that is based on the simulated annealing procedure. In general, this algorithm guarantees to eventually find the globally optimal distribution of genes over clusters. We introduce an iterative scheme that serves to evaluate quantitatively the optimal number of clusters for each specific data set. The scheme is based on standard approaches used in regular statistical tests. The basic idea is to organize the search of the optimal number of clusters simultaneously with the optimization of the distribution of genes over clusters. The efficiency of the proposed algorithm has been evaluated by means of a reverse engineering experiment, that is, a situation in which the correct distribution of genes over clusters is known a priori. The employment of this statistically rigorous test has shown that our algorithm places greater than 90% genes into correct clusters. Finally, the algorithm has been tested on real gene expression data (expression changes during yeast cell cycle) for which the fundamental patterns of gene expression and the assignment of genes to clusters are well understood from numerous previous studies.
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Annealing Ant Colony Optimization with Mutation Operator for Solving TSP
2016-01-01
Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality. PMID:27999590
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Chi, Miaofang; Wang, Chao; Lei, Yinkai; Wang, Guofeng; Li, Dongguo; More, Karren L.; Lupini, Andrew; Allard, Lawrence F.; Markovic, Nenad M.; Stamenkovic, Vojislav R.
2015-01-01
The catalytic performance of nanoparticles is primarily determined by the precise nature of the surface and near-surface atomic configurations, which can be tailored by post-synthesis annealing effectively and straightforwardly. Understanding the complete dynamic response of surface structure and chemistry to thermal treatments at the atomic scale is imperative for the rational design of catalyst nanoparticles. Here, by tracking the same individual Pt3Co nanoparticles during in situ annealing in a scanning transmission electron microscope, we directly discern five distinct stages of surface elemental rearrangements in Pt3Co nanoparticles at the atomic scale: initial random (alloy) elemental distribution; surface platinum-skin-layer formation; nucleation of structurally ordered domains; ordered framework development and, finally, initiation of amorphization. Furthermore, a comprehensive interplay among phase evolution, surface faceting and elemental inter-diffusion is revealed, and supported by atomistic simulations. This work may pave the way towards designing catalysts through post-synthesis annealing for optimized catalytic performance. PMID:26576477
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Chi, Miaofang; Wang, Chao; Lei, Yinkai; ...
2015-11-18
The catalytic performance of nanoparticles is primarily determined by the precise nature of the surface and near-surface atomic configurations, which can be tailored by post-synthesis annealing effectively and straightforwardly. Understanding the complete dynamic response of surface structure and chemistry to thermal treatments at the atomic scale is imperative for the rational design of catalyst nanoparticles. Here, by tracking the same individual Pt 3Co nanoparticles during in situ annealing in a scanning transmission electron microscope, we directly discern five distinct stages of surface elemental rearrangements in Pt 3Co nanoparticles at the atomic scale: initial random (alloy) elemental distribution; surface platinum-skin-layer formation;more » nucleation of structurally ordered domains; ordered framework development and, finally, initiation of amorphization. Furthermore, a comprehensive interplay among phase evolution, surface faceting and elemental inter-diffusion is revealed, and supported by atomistic simulations. In conlcusion, this work may pave the way towards designing catalysts through post-synthesis annealing for optimized catalytic performance.« less
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Li, Yanhui; Guo, Hao; Wang, Lin; Fu, Jing
2013-01-01
Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment.
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A simulated annealing approach for redesigning a warehouse network problem
NASA Astrophysics Data System (ADS)
Khairuddin, Rozieana; Marlizawati Zainuddin, Zaitul; Jiun, Gan Jia
2017-09-01
Now a day, several companies consider downsizing their distribution networks in ways that involve consolidation or phase-out of some of their current warehousing facilities due to the increasing competition, mounting cost pressure and taking advantage on the economies of scale. Consequently, the changes on economic situation after a certain period of time require an adjustment on the network model in order to get the optimal cost under the current economic conditions. This paper aimed to develop a mixed-integer linear programming model for a two-echelon warehouse network redesign problem with capacitated plant and uncapacitated warehouses. The main contribution of this study is considering capacity constraint for existing warehouses. A Simulated Annealing algorithm is proposed to tackle with the proposed model. The numerical solution showed the model and method of solution proposed was practical.
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Czaplicki, Jerzy; Cornélissen, Germaine; Halberg, Franz
2009-01-01
Summary Transyears in biology have been documented thus far by the extended cosinor approach, including linear-nonlinear rhythmometry. We here confirm the existence of transyears by simulated annealing, a method originally developed for a much broader use, but described and introduced herein for validating its application to time series. The method is illustrated both on an artificial test case with known components and on biological data. We provide a table comparing results by the two methods and trust that the procedure will serve the budding sciences of chronobiology (the study of mechanisms underlying biological time structure), chronomics (the mapping of time structures in and around us), and chronobioethics, using the foregoing disciplines to add to concern for illnesses of individuals, and to budding focus on diseases of nations and civilizations. PMID:20414480
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Parameter optimization for transitions between memory states in small arrays of Josephson junctions
DOE Office of Scientific and Technical Information (OSTI.GOV)
Rezac, Jacob D.; Imam, Neena; Braiman, Yehuda
Coupled arrays of Josephson junctions possess multiple stable zero voltage states. Such states can store information and consequently can be utilized for cryogenic memory applications. Basic memory operations can be implemented by sending a pulse to one of the junctions and studying transitions between the states. In order to be suitable for memory operations, such transitions between the states have to be fast and energy efficient. Here in this article we employed simulated annealing, a stochastic optimization algorithm, to study parameter optimization of array parameters which minimizes times and energies of transitions between specifically chosen states that can be utilizedmore » for memory operations (Read, Write, and Reset). Simulation results show that such transitions occur with access times on the order of 10–100 ps and access energies on the order of 10 -19–5×10 -18 J. Numerical simulations are validated with approximate analytical results.« less
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Model for Bi-objective emergency rescue vehicle routing optimization
NASA Astrophysics Data System (ADS)
Yang, Yuhang
2017-03-01
Vehicle routing problem is an important research topic in management science. In this paper, one vehicle can rescue multiple disaster points and two optimization objectives are rescue time and rescue effect. Rescue effect is expressed as the ratio of unloaded material to arrival time when rescue vehicles participate in rescue every time. In this paper, the corresponding emergency rescue model is established and the effectiveness of the model is verified by simulated annealing algorithm. It can provide the basis for practical decision-making.
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NASA Astrophysics Data System (ADS)
Utama, D. N.; Ani, N.; Iqbal, M. M.
2018-03-01
Optimization is a process for finding parameter (parameters) that is (are) able to deliver an optimal value for an objective function. Seeking an optimal generic model for optimizing is a computer science study that has been being practically conducted by numerous researchers. Generic model is a model that can be technically operated to solve any varieties of optimization problem. By using an object-oriented method, the generic model for optimizing was constructed. Moreover, two types of optimization method, simulated-annealing and hill-climbing, were functioned in constructing the model and compared to find the most optimal one then. The result said that both methods gave the same result for a value of objective function and the hill-climbing based model consumed the shortest running time.
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NASA Astrophysics Data System (ADS)
Torabi, Amir; Kolahan, Farhad
2018-07-01
Pulsed laser welding is a powerful technique especially suitable for joining thin sheet metals. In this study, based on experimental data, pulsed laser welding of thin AISI316L austenitic stainless steel sheet has been modeled and optimized. The experimental data required for modeling are gathered as per Central Composite Design matrix in Response Surface Methodology (RSM) with full replication of 31 runs. Ultimate Tensile Strength (UTS) is considered as the main quality measure in laser welding. Furthermore, the important process parameters including peak power, pulse duration, pulse frequency and welding speed are selected as input process parameters. The relation between input parameters and the output response is established via full quadratic response surface regression with confidence level of 95%. The adequacy of the regression model was verified using Analysis of Variance technique results. The main effects of each factor and the interactions effects with other factors were analyzed graphically in contour and surface plot. Next, to maximum joint UTS, the best combinations of parameters levels were specified using RSM. Moreover, the mathematical model is implanted into a Simulated Annealing (SA) optimization algorithm to determine the optimal values of process parameters. The results obtained by both SA and RSM optimization techniques are in good agreement. The optimal parameters settings for peak power of 1800 W, pulse duration of 4.5 ms, frequency of 4.2 Hz and welding speed of 0.5 mm/s would result in a welded joint with 96% of the base metal UTS. Computational results clearly demonstrate that the proposed modeling and optimization procedures perform quite well for pulsed laser welding process.
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Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems
2017-01-01
Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data. PMID:28806754
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Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems.
Almaraashi, Majid
2017-01-01
Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data.
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NASA Astrophysics Data System (ADS)
Branicio, Paulo S.; Bai, Kewu; Ramanarayan, H.; Wu, David T.; Sullivan, Michael B.; Srolovitz, David J.
2018-04-01
The complete process of amorphization and crystallization of the phase-change material G e2S b2T e5 is investigated using nanosecond ab initio molecular dynamics simulations. Varying the quench rate during the amorphization phase of the cycle results in the generation of a variety of structures from entirely crystallized (-0.45 K/ps) to entirely amorphized (-16 K/ps). The 1.5-ns annealing simulations indicate that the crystallization process depends strongly on both the annealing temperature and the initial amorphous structure. The presence of crystal precursors (square rings) in the amorphous matrix enhances nucleation/crystallization kinetics. The simulation data are used to construct a combined continuous-cooling-transformation (CCT) and temperature-time-transformation (TTT) diagram. The nose of the CCT-TTT diagram corresponds to the minimum time for the onset of homogenous crystallization and is located at 600 K and 70 ps. That corresponds to a critical cooling rate for amorphization of -4.5 K/ps. The results, in excellent agreement with experimental observations, suggest that a strategy that utilizes multiple quench rates and annealing temperatures may be used to effectively optimize the reversible switching speed and enable fast and energy-efficient phase-change memories.
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Prediction of Flood Warning in Taiwan Using Nonlinear SVM with Simulated Annealing Algorithm
NASA Astrophysics Data System (ADS)
Lee, C.
2013-12-01
The issue of the floods is important in Taiwan. It is because the narrow and high topography of the island make lots of rivers steep in Taiwan. The tropical depression likes typhoon always causes rivers to flood. Prediction of river flow under the extreme rainfall circumstances is important for government to announce the warning of flood. Every time typhoon passed through Taiwan, there were always floods along some rivers. The warning is classified to three levels according to the warning water levels in Taiwan. The propose of this study is to predict the level of floods warning from the information of precipitation, rainfall duration and slope of riverbed. To classify the level of floods warning by the above-mentioned information and modeling the problems, a machine learning model, nonlinear Support vector machine (SVM), is formulated to classify the level of floods warning. In addition, simulated annealing (SA), a probabilistic heuristic algorithm, is used to determine the optimal parameter of the SVM model. A case study of flooding-trend rivers of different gradients in Taiwan is conducted. The contribution of this SVM model with simulated annealing is capable of making efficient announcement for flood warning and keeping the danger of flood from residents along the rivers.
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Angland, P.; Haberberger, D.; Ivancic, S. T.; ...
2017-10-30
Here, a new method of analysis for angular filter refractometry images was developed to characterize laser-produced, long-scale-length plasmas using an annealing algorithm to iterative converge upon a solution. Angular filter refractometry (AFR) is a novel technique used to characterize the density pro files of laser-produced, long-scale-length plasmas. A synthetic AFR image is constructed by a user-defined density profile described by eight parameters, and the algorithm systematically alters the parameters until the comparison is optimized. The optimization and statistical uncertainty calculation is based on a minimization of themore » $$\\chi$$2 test statistic. The algorithm was successfully applied to experimental data of plasma expanding from a flat, laser-irradiated target, resulting in average uncertainty in the density profile of 5-10% in the region of interest.« less
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Angland, P.; Haberberger, D.; Ivancic, S. T.
Here, a new method of analysis for angular filter refractometry images was developed to characterize laser-produced, long-scale-length plasmas using an annealing algorithm to iterative converge upon a solution. Angular filter refractometry (AFR) is a novel technique used to characterize the density pro files of laser-produced, long-scale-length plasmas. A synthetic AFR image is constructed by a user-defined density profile described by eight parameters, and the algorithm systematically alters the parameters until the comparison is optimized. The optimization and statistical uncertainty calculation is based on a minimization of themore » $$\\chi$$2 test statistic. The algorithm was successfully applied to experimental data of plasma expanding from a flat, laser-irradiated target, resulting in average uncertainty in the density profile of 5-10% in the region of interest.« less
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Optimal atomic structure of amorphous silicon obtained from density functional theory calculations
NASA Astrophysics Data System (ADS)
Pedersen, Andreas; Pizzagalli, Laurent; Jónsson, Hannes
2017-06-01
Atomic structure of amorphous silicon consistent with several reported experimental measurements has been obtained from annealing simulations using electron density functional theory calculations and a systematic removal of weakly bound atoms. The excess energy and density with respect to the crystal are well reproduced in addition to radial distribution function, angular distribution functions, and vibrational density of states. No atom in the optimal configuration is locally in a crystalline environment as deduced by ring analysis and common neighbor analysis, but coordination defects are present at a level of 1%-2%. The simulated samples provide structural models of this archetypal disordered covalent material without preconceived notion of the atomic ordering or fitting to experimental data.
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A novel method for energy harvesting simulation based on scenario generation
NASA Astrophysics Data System (ADS)
Wang, Zhe; Li, Taoshen; Xiao, Nan; Ye, Jin; Wu, Min
2018-06-01
Energy harvesting network (EHN) is a new form of computer networks. It converts ambient energy into usable electric energy and supply the electrical energy as a primary or secondary power source to the communication devices. However, most of the EHN uses the analytical probability distribution function to describe the energy harvesting process, which cannot accurately identify the actual situation for the lack of authenticity. We propose an EHN simulation method based on scenario generation in this paper. Firstly, instead of setting a probability distribution in advance, it uses optimal scenario reduction technology to generate representative scenarios in single period based on the historical data of the harvested energy. Secondly, it uses homogeneous simulated annealing algorithm to generate optimal daily energy harvesting scenario sequences to get a more accurate simulation of the random characteristics of the energy harvesting network. Then taking the actual wind power data as an example, the accuracy and stability of the method are verified by comparing with the real data. Finally, we cite an instance to optimize the network throughput, which indicate the feasibility and effectiveness of the method we proposed from the optimal solution and data analysis in energy harvesting simulation.
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NASA Astrophysics Data System (ADS)
Dharmaseelan, Anoop; Adistambha, Keyne D.
2015-05-01
Fuel cost accounts for 40 percent of the operating cost of an airline. Fuel cost can be minimized by planning a flight on optimized routes. The routes can be optimized by searching best connections based on the cost function defined by the airline. The most common algorithm that used to optimize route search is Dijkstra's. Dijkstra's algorithm produces a static result and the time taken for the search is relatively long. This paper experiments a new algorithm to optimize route search which combines the principle of simulated annealing and genetic algorithm. The experimental results of route search, presented are shown to be computationally fast and accurate compared with timings from generic algorithm. The new algorithm is optimal for random routing feature that is highly sought by many regional operators.
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NASA Astrophysics Data System (ADS)
Bagheri Tolabi, Hajar; Hosseini, Rahil; Shakarami, Mahmoud Reza
2016-06-01
This article presents a novel hybrid optimization approach for a nonlinear controller of a distribution static compensator (DSTATCOM). The DSTATCOM is connected to a distribution system with the distributed generation units. The nonlinear control is based on partial feedback linearization. Two proportional-integral-derivative (PID) controllers regulate the voltage and track the output in this control system. In the conventional scheme, the trial-and-error method is used to determine the PID controller coefficients. This article uses a combination of a fuzzy system, simulated annealing (SA) and intelligent water drops (IWD) algorithms to optimize the parameters of the controllers. The obtained results reveal that the response of the optimized controlled system is effectively improved by finding a high-quality solution. The results confirm that using the tuning method based on the fuzzy-SA-IWD can significantly decrease the settling and rising times, the maximum overshoot and the steady-state error of the voltage step response of the DSTATCOM. The proposed hybrid tuning method for the partial feedback linearizing (PFL) controller achieved better regulation of the direct current voltage for the capacitor within the DSTATCOM. Furthermore, in the event of a fault the proposed controller tuned by the fuzzy-SA-IWD method showed better performance than the conventional controller or the PFL controller without optimization by the fuzzy-SA-IWD method with regard to both fault duration and clearing times.
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Characterization of the probabilistic traveling salesman problem.
Bowler, Neill E; Fink, Thomas M A; Ball, Robin C
2003-09-01
We show that stochastic annealing can be successfully applied to gain new results on the probabilistic traveling salesman problem. The probabilistic "traveling salesman" must decide on an a priori order in which to visit n cities (randomly distributed over a unit square) before learning that some cities can be omitted. We find the optimized average length of the pruned tour follows E(L(pruned))=sqrt[np](0.872-0.105p)f(np), where p is the probability of a city needing to be visited, and f(np)-->1 as np--> infinity. The average length of the a priori tour (before omitting any cities) is found to follow E(L(a priori))=sqrt[n/p]beta(p), where beta(p)=1/[1.25-0.82 ln(p)] is measured for 0.05< or =p< or =0.6. Scaling arguments and indirect measurements suggest that beta(p) tends towards a constant for p<0.03. Our stochastic annealing algorithm is based on limited sampling of the pruned tour lengths, exploiting the sampling error to provide the analog of thermal fluctuations in simulated (thermal) annealing. The method has general application to the optimization of functions whose cost to evaluate rises with the precision required.
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NASA Astrophysics Data System (ADS)
Luo, Ya-Zhong; Zhang, Jin; Li, Hai-yang; Tang, Guo-Jin
2010-08-01
In this paper, a new optimization approach combining primer vector theory and evolutionary algorithms for fuel-optimal non-linear impulsive rendezvous is proposed. The optimization approach is designed to seek the optimal number of impulses as well as the optimal impulse vectors. In this optimization approach, adding a midcourse impulse is determined by an interactive method, i.e. observing the primer-magnitude time history. An improved version of simulated annealing is employed to optimize the rendezvous trajectory with the fixed-number of impulses. This interactive approach is evaluated by three test cases: coplanar circle-to-circle rendezvous, same-circle rendezvous and non-coplanar rendezvous. The results show that the interactive approach is effective and efficient in fuel-optimal non-linear rendezvous design. It can guarantee solutions, which satisfy the Lawden's necessary optimality conditions.
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A Comparison of Techniques for Scheduling Fleets of Earth-Observing Satellites
NASA Technical Reports Server (NTRS)
Globus, Al; Crawford, James; Lohn, Jason; Pryor, Anna
2003-01-01
Earth observing satellite (EOS) scheduling is a complex real-world domain representative of a broad class of over-subscription scheduling problems. Over-subscription problems are those where requests for a facility exceed its capacity. These problems arise in a wide variety of NASA and terrestrial domains and are .XI important class of scheduling problems because such facilities often represent large capital investments. We have run experiments comparing multiple variants of the genetic algorithm, hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on two variants of a realistically-sized model of the EOS scheduling problem. These are implemented as permutation-based methods; methods that search in the space of priority orderings of observation requests and evaluate each permutation by using it to drive a greedy scheduler. Simulated annealing performs best and random mutation operators outperform our squeaky (more intelligent) operator. Furthermore, taking smaller steps towards the end of the search improves performance.
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Guo, Hao; Fu, Jing
2013-01-01
Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment. PMID:24489489
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NASA Astrophysics Data System (ADS)
Kadowaki, Tadashi
2018-02-01
We propose a method to interpolate dynamics of von Neumann and classical master equations with an arbitrary mixing parameter to investigate the thermal effects in quantum dynamics. The two dynamics are mixed by intervening to continuously modify their solutions, thus coupling them indirectly instead of directly introducing a coupling term. This maintains the quantum system in a pure state even after the introduction of thermal effects and obtains not only a density matrix but also a state vector representation. Further, we demonstrate that the dynamics of a two-level system can be rewritten as a set of standard differential equations, resulting in quantum dynamics that includes thermal relaxation. These equations are equivalent to the optical Bloch equations at the weak coupling and asymptotic limits, implying that the dynamics cause thermal effects naturally. Numerical simulations of ferromagnetic and frustrated systems support this idea. Finally, we use this method to study thermal effects in quantum annealing, revealing nontrivial performance improvements for a spin glass model over a certain range of annealing time. This result may enable us to optimize the annealing time of real annealing machines.
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Shandilya, Bhavesh K; Sen, Shrabani; Sahoo, Tapas; Talukder, Srijeeta; Chaudhury, Pinaki; Adhikari, Satrajit
2013-07-21
The selective control of O-H/O-D bond dissociation in reduced dimensionality model of HOD molecule has been explored through IR+UV femtosecond pulses. The IR pulse has been optimized using simulated annealing stochastic approach to maximize population of a desired low quanta vibrational state. Since those vibrational wavefunctions of the ground electronic states are preferentially localized either along the O-H or O-D mode, the femtosecond UV pulse is used only to transfer vibrationally excited molecule to the repulsive upper surface to cleave specific bond, O-H or O-D. While transferring from the ground electronic state to the repulsive one, the optimization of the UV pulse is not necessarily required except specific case. The results so obtained are analyzed with respect to time integrated flux along with contours of time evolution of probability density on excited potential energy surface. After preferential excitation from [line]0, 0> ([line]m, n> stands for the state having m and n quanta of excitations in O-H and O-D mode, respectively) vibrational level of the ground electronic state to its specific low quanta vibrational state ([line]1, 0> or [line]0, 1> or [line]2, 0> or [line]0, 2>) by using optimized IR pulse, the dissociation of O-D or O-H bond through the excited potential energy surface by UV laser pulse appears quite high namely, 88% (O-H ; [line]1, 0>) or 58% (O-D ; [line]0, 1>) or 85% (O-H ; [line]2, 0>) or 59% (O-D ; [line]0, 2>). Such selectivity of the bond breaking by UV pulse (if required, optimized) together with optimized IR one is encouraging compared to the normal pulses.
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Guaranteed Discrete Energy Optimization on Large Protein Design Problems.
Simoncini, David; Allouche, David; de Givry, Simon; Delmas, Céline; Barbe, Sophie; Schiex, Thomas
2015-12-08
In Computational Protein Design (CPD), assuming a rigid backbone and amino-acid rotamer library, the problem of finding a sequence with an optimal conformation is NP-hard. In this paper, using Dunbrack's rotamer library and Talaris2014 decomposable energy function, we use an exact deterministic method combining branch and bound, arc consistency, and tree-decomposition to provenly identify the global minimum energy sequence-conformation on full-redesign problems, defining search spaces of size up to 10(234). This is achieved on a single core of a standard computing server, requiring a maximum of 66GB RAM. A variant of the algorithm is able to exhaustively enumerate all sequence-conformations within an energy threshold of the optimum. These proven optimal solutions are then used to evaluate the frequencies and amplitudes, in energy and sequence, at which an existing CPD-dedicated simulated annealing implementation may miss the optimum on these full redesign problems. The probability of finding an optimum drops close to 0 very quickly. In the worst case, despite 1,000 repeats, the annealing algorithm remained more than 1 Rosetta unit away from the optimum, leading to design sequences that could differ from the optimal sequence by more than 30% of their amino acids.
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Global optimization of multimode interference structure for ratiometric wavelength measurement
NASA Astrophysics Data System (ADS)
Wang, Qian; Farrell, Gerald; Hatta, Agus Muhamad
2007-07-01
The multimode interference structure is conventionally used as a splitter/combiner. In this paper, it is optimised as an edge filter for ratiometric wavelength measurement, which can be used in demodulation of fiber Bragg grating sensing. The global optimization algorithm-adaptive simulated annealing is introduced in the design of multimode interference structure including the length and width of the multimode waveguide section, and positions of the input and output waveguides. The designed structure shows a suitable spectral response for wavelength measurement and a good fabrication tolerance.
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A stochastic framework for spot-scanning particle therapy.
Robini, Marc; Yuemin Zhu; Wanyu Liu; Magnin, Isabelle
2016-08-01
In spot-scanning particle therapy, inverse treatment planning is usually limited to finding the optimal beam fluences given the beam trajectories and energies. We address the much more challenging problem of jointly optimizing the beam fluences, trajectories and energies. For this purpose, we design a simulated annealing algorithm with an exploration mechanism that balances the conflicting demands of a small mixing time at high temperatures and a reasonable acceptance rate at low temperatures. Numerical experiments substantiate the relevance of our approach and open new horizons to spot-scanning particle therapy.
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A Simulated Annealing based Optimization Algorithm for Automatic Variogram Model Fitting
NASA Astrophysics Data System (ADS)
Soltani-Mohammadi, Saeed; Safa, Mohammad
2016-09-01
Fitting a theoretical model to an experimental variogram is an important issue in geostatistical studies because if the variogram model parameters are tainted with uncertainty, the latter will spread in the results of estimations and simulations. Although the most popular fitting method is fitting by eye, in some cases use is made of the automatic fitting method on the basis of putting together the geostatistical principles and optimization techniques to: 1) provide a basic model to improve fitting by eye, 2) fit a model to a large number of experimental variograms in a short time, and 3) incorporate the variogram related uncertainty in the model fitting. Effort has been made in this paper to improve the quality of the fitted model by improving the popular objective function (weighted least squares) in the automatic fitting. Also, since the variogram model function (£) and number of structures (m) too affect the model quality, a program has been provided in the MATLAB software that can present optimum nested variogram models using the simulated annealing method. Finally, to select the most desirable model from among the single/multi-structured fitted models, use has been made of the cross-validation method, and the best model has been introduced to the user as the output. In order to check the capability of the proposed objective function and the procedure, 3 case studies have been presented.
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A Comparison of Trajectory Optimization Methods for the Impulsive Minimum Fuel Rendezvous Problem
NASA Technical Reports Server (NTRS)
Hughes, Steven P.; Mailhe, Laurie M.; Guzman, Jose J.
2002-01-01
In this paper we present a comparison of optimization approaches to the minimum fuel rendezvous problem. Both indirect and direct methods are compared for a variety of test cases. The indirect approach is based on primer vector theory. The direct approaches are implemented numerically and include Sequential Quadratic Programming (SQP), Quasi-Newton, Simplex, Genetic Algorithms, and Simulated Annealing. Each method is applied to a variety of test cases including, circular to circular coplanar orbits, LEO to GEO, and orbit phasing in highly elliptic orbits. We also compare different constrained optimization routines on complex orbit rendezvous problems with complicated, highly nonlinear constraints.
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OPTIMIZING THROUGH CO-EVOLUTIONARY AVALANCHES
DOE Office of Scientific and Technical Information (OSTI.GOV)
S. BOETTCHER; A. PERCUS
2000-08-01
We explore a new general-purpose heuristic for finding high-quality solutions to hard optimization problems. The method, called extremal optimization, is inspired by ''self-organized critically,'' a concept introduced to describe emergent complexity in many physical systems. In contrast to Genetic Algorithms which operate on an entire ''gene-pool'' of possible solutions, extremal optimization successively replaces extremely undesirable elements of a sub-optimal solution with new, random ones. Large fluctuations, called ''avalanches,'' ensue that efficiently explore many local optima. Drawing upon models used to simulate far-from-equilibrium dynamics, extremal optimization complements approximation methods inspired by equilibrium statistical physics, such as simulated annealing. With only onemore » adjustable parameter, its performance has proved competitive with more elaborate methods, especially near phase transitions. Those phase transitions are found in the parameter space of most optimization problems, and have recently been conjectured to be the origin of some of the hardest instances in computational complexity. We will demonstrate how extremal optimization can be implemented for a variety of combinatorial optimization problems. We believe that extremal optimization will be a useful tool in the investigation of phase transitions in combinatorial optimization problems, hence valuable in elucidating the origin of computational complexity.« less
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High-Level Connectionist Models
1993-10-01
subject of intense research since Axelrod (1984) showed that two agents engaged in a prisoner’s dilemma ( Poundstone , 1992) can evolve into mutually...The various parameter values for the program are set as described above unless otherwise noted. 4.1 Williams ’ Trigger Problem As an initial test...M. P. Vecchi. Optimization by simulated annealing. Sci- ence, 220:671-680, 1983. [39] R. J. Williams . Adaptive State Representation and Estimation
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Bernal, Javier; Torres-Jimenez, Jose
2015-01-01
SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the implementation of the training process in SAGRAD are discussed, such as the efficient computation of gradients and multiplication of vectors by Hessian matrices that are required by Møller's algorithm; the (re)initialization of weights with simulated annealing required to (re)start Møller's algorithm the first time and each time thereafter that it shows insufficient progress in reaching a possibly local minimum; and the use of simulated annealing when Møller's algorithm, after possibly making considerable progress, becomes stuck at a local minimum or flat area of weight space. Outlines of the scaled conjugate gradient algorithm, the simulated annealing procedure and the training process used in SAGRAD are presented together with results from running SAGRAD on two examples of training data.
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Solving a Higgs optimization problem with quantum annealing for machine learning.
Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria
2017-10-18
The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.
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Solving a Higgs optimization problem with quantum annealing for machine learning
NASA Astrophysics Data System (ADS)
Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria
2017-10-01
The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.
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Stochastic Simulation of Actin Dynamics Reveals the Role of Annealing and Fragmentation
Fass, Joseph; Pak, Chi; Bamburg, James; Mogilner, Alex
2008-01-01
Recent observations of F-actin dynamics call for theoretical models to interpret and understand the quantitative data. A number of existing models rely on simplifications and do not take into account F-actin fragmentation and annealing. We use Gillespie’s algorithm for stochastic simulations of the F-actin dynamics including fragmentation and annealing. The simulations vividly illustrate that fragmentation and annealing have little influence on the shape of the polymerization curve and on nucleotide profiles within filaments but drastically affect the F-actin length distribution, making it exponential. We find that recent surprising measurements of high length diffusivity at the critical concentration cannot be explained by fragmentation and annealing events unless both fragmentation rates and frequency of undetected fragmentation and annealing events are greater than previously thought. The simulations compare well with experimentally measured actin polymerization data and lend additional support to a number of existing theoretical models. PMID:18279896
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Optimization Via Open System Quantum Annealing
2016-01-07
Daniel A. Lidar. Experimental signature of programmable quantum annealing, Nature Communications , (06 2013): 0. doi: 10.1038/ncomms3067 T. F...Demonstrated error correction effectiveness. • Demonstrated quantum annealing correction on antiferromagnetic chains, with substantial fidelity gains...Rev. A 91, 022309 (2015). 3. A. Kalev and I. Hen, “ Fidelity -optimized quantum state estimation”, New Journal of Physics 17 092008 (2015). 4. I
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Optimization of a hydrometric network extension using specific flow, kriging and simulated annealing
NASA Astrophysics Data System (ADS)
Chebbi, Afef; Kebaili Bargaoui, Zoubeida; Abid, Nesrine; da Conceição Cunha, Maria
2017-12-01
In hydrometric stations, water levels are continuously observed and discharge rating curves are constantly updated to achieve accurate river levels and discharge observations. An adequate spatial distribution of hydrological gauging stations presents a lot of interest in linkage with the river regime characterization, water infrastructures design, water resources management and ecological survey. Due to the increase of riverside population and the associated flood risk, hydrological networks constantly need to be developed. This paper suggests taking advantage of kriging approaches to improve the design of a hydrometric network. The context deals with the application of an optimization approach using ordinary kriging and simulated annealing (SA) in order to identify the best locations to install new hydrometric gauges. The task at hand is to extend an existing hydrometric network in order to estimate, at ungauged sites, the average specific annual discharge which is a key basin descriptor. This methodology is developed for the hydrometric network of the transboundary Medjerda River in the North of Tunisia. A Geographic Information System (GIS) is adopted to delineate basin limits and centroids. The latter are adopted to assign the location of basins in kriging development. Scenarios where the size of an existing 12 stations network is alternatively increased by 1, 2, 3, 4 and 5 new station(s) are investigated using geo-regression and minimization of the variance of kriging errors. The analysis of the optimized locations from a scenario to another shows a perfect conformity with respect to the location of the new sites. The new locations insure a better spatial coverage of the study area as seen with the increase of both the average and the maximum of inter-station distances after optimization. The optimization procedure selects the basins that insure the shifting of the mean drainage area towards higher specific discharges.
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Extremal Optimization: Methods Derived from Co-Evolution
DOE Office of Scientific and Technical Information (OSTI.GOV)
Boettcher, S.; Percus, A.G.
1999-07-13
We describe a general-purpose method for finding high-quality solutions to hard optimization problems, inspired by self-organized critical models of co-evolution such as the Bak-Sneppen model. The method, called Extremal Optimization, successively eliminates extremely undesirable components of sub-optimal solutions, rather than ''breeding'' better components. In contrast to Genetic Algorithms which operate on an entire ''gene-pool'' of possible solutions, Extremal Optimization improves on a single candidate solution by treating each of its components as species co-evolving according to Darwinian principles. Unlike Simulated Annealing, its non-equilibrium approach effects an algorithm requiring few parameters to tune. With only one adjustable parameter, its performance provesmore » competitive with, and often superior to, more elaborate stochastic optimization procedures. We demonstrate it here on two classic hard optimization problems: graph partitioning and the traveling salesman problem.« less
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Protein structure refinement using a quantum mechanics-based chemical shielding predictor.
Bratholm, Lars A; Jensen, Jan H
2017-03-01
The accurate prediction of protein chemical shifts using a quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of a protein backbone and CB chemical shifts (ProCS15, PeerJ , 2016, 3, e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1-0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If an amino acid specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiments that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differ in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift.
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Combining local search with co-evolution in a remarkably simple way
DOE Office of Scientific and Technical Information (OSTI.GOV)
Boettcher, S.; Percus, A.
2000-05-01
The authors explore a new general-purpose heuristic for finding high-quality solutions to hard optimization problem. The method, called extremal optimization, is inspired by self-organized criticality, a concept introduced to describe emergent complexity in physical systems. In contrast to genetic algorithms, which operate on an entire gene-pool of possible solutions, extremal optimization successively replaces extremely undesirable elements of a single sub-optimal solution with new, random ones. Large fluctuations, or avalanches, ensue that efficiently explore many local optima. Drawing upon models used to simulate far-from-equilibrium dynamics, extremal optimization complements heuristics inspired by equilibrium statistical physics, such as simulated annealing. With only onemore » adjustable parameter, its performance has proved competitive with more elaborate methods, especially near phase transitions. Phase transitions are found in many combinatorial optimization problems, and have been conjectured to occur in the region of parameter space containing the hardest instances. We demonstrate how extremal optimization can be implemented for a variety of hard optimization problems. We believe that this will be a useful tool in the investigation of phase transitions in combinatorial optimization, thereby helping to elucidate the origin of computational complexity.« less
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Unraveling Quantum Annealers using Classical Hardness
Martin-Mayor, Victor; Hen, Itay
2015-01-01
Recent advances in quantum technology have led to the development and manufacturing of experimental programmable quantum annealing optimizers that contain hundreds of quantum bits. These optimizers, commonly referred to as ‘D-Wave’ chips, promise to solve practical optimization problems potentially faster than conventional ‘classical’ computers. Attempts to quantify the quantum nature of these chips have been met with both excitement and skepticism but have also brought up numerous fundamental questions pertaining to the distinguishability of experimental quantum annealers from their classical thermal counterparts. Inspired by recent results in spin-glass theory that recognize ‘temperature chaos’ as the underlying mechanism responsible for the computational intractability of hard optimization problems, we devise a general method to quantify the performance of quantum annealers on optimization problems suffering from varying degrees of temperature chaos: A superior performance of quantum annealers over classical algorithms on these may allude to the role that quantum effects play in providing speedup. We utilize our method to experimentally study the D-Wave Two chip on different temperature-chaotic problems and find, surprisingly, that its performance scales unfavorably as compared to several analogous classical algorithms. We detect, quantify and discuss several purely classical effects that possibly mask the quantum behavior of the chip. PMID:26483257
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Samaan, Michael A; Weinhandl, Joshua T; Bawab, Sebastian Y; Ringleb, Stacie I
2016-12-01
Musculoskeletal modeling allows for the determination of various parameters during dynamic maneuvers by using in vivo kinematic and ground reaction force (GRF) data as inputs. Differences between experimental and model marker data and inconsistencies in the GRFs applied to these musculoskeletal models may not produce accurate simulations. Therefore, residual forces and moments are applied to these models in order to reduce these differences. Numerical optimization techniques can be used to determine optimal tracking weights of each degree of freedom of a musculoskeletal model in order to reduce differences between the experimental and model marker data as well as residual forces and moments. In this study, the particle swarm optimization (PSO) and simplex simulated annealing (SIMPSA) algorithms were used to determine optimal tracking weights for the simulation of a sidestep cut. The PSO and SIMPSA algorithms were able to produce model kinematics that were within 1.4° of experimental kinematics with residual forces and moments of less than 10 N and 18 Nm, respectively. The PSO algorithm was able to replicate the experimental kinematic data more closely and produce more dynamically consistent kinematic data for a sidestep cut compared to the SIMPSA algorithm. Future studies should use external optimization routines to determine dynamically consistent kinematic data and report the differences between experimental and model data for these musculoskeletal simulations.
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Code Samples Used for Complexity and Control
NASA Astrophysics Data System (ADS)
Ivancevic, Vladimir G.; Reid, Darryn J.
2015-11-01
The following sections are included: * MathematicaⓇ Code * Generic Chaotic Simulator * Vector Differential Operators * NLS Explorer * 2C++ Code * C++ Lambda Functions for Real Calculus * Accelerometer Data Processor * Simple Predictor-Corrector Integrator * Solving the BVP with the Shooting Method * Linear Hyperbolic PDE Solver * Linear Elliptic PDE Solver * Method of Lines for a Set of the NLS Equations * C# Code * Iterative Equation Solver * Simulated Annealing: A Function Minimum * Simple Nonlinear Dynamics * Nonlinear Pendulum Simulator * Lagrangian Dynamics Simulator * Complex-Valued Crowd Attractor Dynamics * Freeform Fortran Code * Lorenz Attractor Simulator * Complex Lorenz Attractor * Simple SGE Soliton * Complex Signal Presentation * Gaussian Wave Packet * Hermitian Matrices * Euclidean L2-Norm * Vector/Matrix Operations * Plain C-Code: Levenberg-Marquardt Optimizer * Free Basic Code: 2D Crowd Dynamics with 3000 Agents
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Kwok, T; Smith, K A
2000-09-01
The aim of this paper is to study both the theoretical and experimental properties of chaotic neural network (CNN) models for solving combinatorial optimization problems. Previously we have proposed a unifying framework which encompasses the three main model types, namely, Chen and Aihara's chaotic simulated annealing (CSA) with decaying self-coupling, Wang and Smith's CSA with decaying timestep, and the Hopfield network with chaotic noise. Each of these models can be represented as a special case under the framework for certain conditions. This paper combines the framework with experimental results to provide new insights into the effect of the chaotic neurodynamics of each model. By solving the N-queen problem of various sizes with computer simulations, the CNN models are compared in different parameter spaces, with optimization performance measured in terms of feasibility, efficiency, robustness and scalability. Furthermore, characteristic chaotic neurodynamics crucial to effective optimization are identified, together with a guide to choosing the corresponding model parameters.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Freitez, Juan A.; Sanchez, Morella; Ruette, Fernando
Application of simulated annealing (SA) and simplified GSA (SGSA) techniques for parameter optimization of parametric quantum chemistry method (CATIVIC) was performed. A set of organic molecules were selected for test these techniques. Comparison of the algorithms was carried out for error function minimization with respect to experimental values. Results show that SGSA is more efficient than SA with respect to computer time. Accuracy is similar in both methods; however, there are important differences in the final set of parameters.
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NASA Astrophysics Data System (ADS)
Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.
2016-07-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards.
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A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns
NASA Astrophysics Data System (ADS)
Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng
2009-11-01
Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.
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NASA Astrophysics Data System (ADS)
Singh, Puja; Prakash, Shashi
2017-07-01
Hybrid wireless-optical broadband access network (WOBAN) or Fiber-Wireless (FiWi) is the integration of wireless access network and optical network. This hybrid multi-domain network adopts the advantages of wireless and optical domains and serves the demand of technology savvy users. FiWi exhibits the properties of cost effectiveness, robustness, flexibility, high capacity, reliability and is self organized. Optical Network Unit (ONU) placement problem in FiWi contributes in simplifying the network design and enhances the performance in terms of cost efficiency and increased throughput. Several individual-based algorithms, such as Simulated Annealing (SA), Tabu Search, etc. have been suggested for ONU placement, but these algorithms suffer from premature convergence (trapping in a local optima). The present research work undertakes the deployment of FiWi and proposes a novel nature-inspired heuristic paradigm called Moth-Flame optimization (MFO) algorithm for multiple optical network units' placement. MFO is a population based algorithm. Population-based algorithms are better in handling local optima avoidance. The simulation results are compared with the existing Greedy and Simulated Annealing algorithms to optimize the position of ONUs. To the best of our knowledge, MFO algorithm has been used for the first time in this domain, moreover it has been able to provide very promising and competitive results. The performance of MFO algorithm has been analyzed by varying the 'b' parameter. MFO algorithm results in faster convergence than the existing strategies of Greedy and SA and returns a lower value of overall cost function. The results exhibit the dependence of the objective function on the distribution of wireless users also.
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Bernal, Javier; Torres-Jimenez, Jose
2015-01-01
SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller’s scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the implementation of the training process in SAGRAD are discussed, such as the efficient computation of gradients and multiplication of vectors by Hessian matrices that are required by Møller’s algorithm; the (re)initialization of weights with simulated annealing required to (re)start Møller’s algorithm the first time and each time thereafter that it shows insufficient progress in reaching a possibly local minimum; and the use of simulated annealing when Møller’s algorithm, after possibly making considerable progress, becomes stuck at a local minimum or flat area of weight space. Outlines of the scaled conjugate gradient algorithm, the simulated annealing procedure and the training process used in SAGRAD are presented together with results from running SAGRAD on two examples of training data. PMID:26958442
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NASA Astrophysics Data System (ADS)
Kumar, S.; Singh, A.; Dhar, A.
2017-08-01
The accurate estimation of the photovoltaic parameters is fundamental to gain an insight of the physical processes occurring inside a photovoltaic device and thereby to optimize its design, fabrication processes, and quality. A simulative approach of accurately determining the device parameters is crucial for cell array and module simulation when applied in practical on-field applications. In this work, we have developed a global particle swarm optimization (GPSO) approach to estimate the different solar cell parameters viz., ideality factor (η), short circuit current (Isc), open circuit voltage (Voc), shunt resistant (Rsh), and series resistance (Rs) with wide a search range of over ±100 % for each model parameter. After validating the accurateness and global search power of the proposed approach with synthetic and noisy data, we applied the technique to the extract the PV parameters of ZnO/PCDTBT based hybrid solar cells (HSCs) prepared under different annealing conditions. Further, we examine the variation of extracted model parameters to unveil the physical processes occurring when different annealing temperatures are employed during the device fabrication and establish the role of improved charge transport in polymer films from independent FET measurements. The evolution of surface morphology, optical absorption, and chemical compositional behaviour of PCDTBT co-polymer films as a function of processing temperature has also been captured in the study and correlated with the findings from the PV parameters extracted using GPSO approach.
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Optimization of the computational load of a hypercube supercomputer onboard a mobile robot.
Barhen, J; Toomarian, N; Protopopescu, V
1987-12-01
A combinatorial optimization methodology is developed, which enables the efficient use of hypercube multiprocessors onboard mobile intelligent robots dedicated to time-critical missions. The methodology is implemented in terms of large-scale concurrent algorithms based either on fast simulated annealing, or on nonlinear asynchronous neural networks. In particular, analytic expressions are given for the effect of singleneuron perturbations on the systems' configuration energy. Compact neuromorphic data structures are used to model effects such as prec xdence constraints, processor idling times, and task-schedule overlaps. Results for a typical robot-dynamics benchmark are presented.
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Optimization of the computational load of a hypercube supercomputer onboard a mobile robot
NASA Technical Reports Server (NTRS)
Barhen, Jacob; Toomarian, N.; Protopopescu, V.
1987-01-01
A combinatorial optimization methodology is developed, which enables the efficient use of hypercube multiprocessors onboard mobile intelligent robots dedicated to time-critical missions. The methodology is implemented in terms of large-scale concurrent algorithms based either on fast simulated annealing, or on nonlinear asynchronous neural networks. In particular, analytic expressions are given for the effect of single-neuron perturbations on the systems' configuration energy. Compact neuromorphic data structures are used to model effects such as precedence constraints, processor idling times, and task-schedule overlaps. Results for a typical robot-dynamics benchmark are presented.
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Two hybrid compaction algorithms for the layout optimization problem.
Xiao, Ren-Bin; Xu, Yi-Chun; Amos, Martyn
2007-01-01
In this paper we present two new algorithms for the layout optimization problem: this concerns the placement of circular, weighted objects inside a circular container, the two objectives being to minimize imbalance of mass and to minimize the radius of the container. This problem carries real practical significance in industrial applications (such as the design of satellites), as well as being of significant theoretical interest. We present two nature-inspired algorithms for this problem, the first based on simulated annealing, and the second on particle swarm optimization. We compare our algorithms with the existing best-known algorithm, and show that our approaches out-perform it in terms of both solution quality and execution time.
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Nonlinear optimization simplified by hypersurface deformation
DOE Office of Scientific and Technical Information (OSTI.GOV)
Stillinger, F.H.; Weber, T.A.
1988-09-01
A general strategy is advanced for simplifying nonlinear optimization problems, the ant-lion method. This approach exploits shape modifications of the cost-function hypersurface which distend basins surrounding low-lying minima (including global minima). By intertwining hypersurface deformations with steepest-descent displacements, the search is concentrated on a small relevant subset of all minima. Specific calculations demonstrating the value of this method are reported for the partitioning of two classes of irregular but nonrandom graphs, the prime-factor graphs and the pi graphs. We also indicate how this approach can be applied to the traveling salesman problem and to design layout optimization, and that itmore » may be useful in combination with simulated annealing strategies.« less
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Predictive process simulation of cryogenic implants for leading edge transistor design
DOE Office of Scientific and Technical Information (OSTI.GOV)
Gossmann, Hans-Joachim; Zographos, Nikolas; Park, Hugh
2012-11-06
Two cryogenic implant TCAD-modules have been developed: (i) A continuum-based compact model targeted towards a TCAD production environment calibrated against an extensive data-set for all common dopants. Ion-specific calibration parameters related to damage generation and dynamic annealing were used and resulted in excellent fits to the calibration data-set. (ii) A Kinetic Monte Carlo (kMC) model including the full time dependence of ion-exposure that a particular spot on the wafer experiences, as well as the resulting temperature vs. time profile of this spot. It was calibrated by adjusting damage generation and dynamic annealing parameters. The kMC simulations clearly demonstrate the importancemore » of the time-structure of the beam for the amorphization process: Assuming an average dose-rate does not capture all of the physics and may lead to incorrect conclusions. The model enables optimization of the amorphization process through tool parameters such as scan speed or beam height.« less
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A Comparison of Techniques for Scheduling Earth-Observing Satellites
NASA Technical Reports Server (NTRS)
Globus, Al; Crawford, James; Lohn, Jason; Pryor, Anna
2004-01-01
Scheduling observations by coordinated fleets of Earth Observing Satellites (EOS) involves large search spaces, complex constraints and poorly understood bottlenecks, conditions where evolutionary and related algorithms are often effective. However, there are many such algorithms and the best one to use is not clear. Here we compare multiple variants of the genetic algorithm: stochastic hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on ten realistically-sized EOS scheduling problems. Schedules are represented by a permutation (non-temperal ordering) of the observation requests. A simple deterministic scheduler assigns times and resources to each observation request in the order indicated by the permutation, discarding those that violate the constraints created by previously scheduled observations. Simulated annealing performs best. Random mutation outperform a more 'intelligent' mutator. Furthermore, the best mutator, by a small margin, was a novel approach we call temperature dependent random sampling that makes large changes in the early stages of evolution and smaller changes towards the end of search.
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A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes.
Lee, I; Sikora, R; Shaw, M J
1997-01-01
Genetic algorithms (GAs) have been used widely for such combinatorial optimization problems as the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and job shop scheduling. In all of these problems there is usually a well defined representation which GA's use to solve the problem. We present a novel approach for solving two related problems-lot sizing and sequencing-concurrently using GAs. The essence of our approach lies in the concept of using a unified representation for the information about both the lot sizes and the sequence and enabling GAs to evolve the chromosome by replacing primitive genes with good building blocks. In addition, a simulated annealing procedure is incorporated to further improve the performance. We evaluate the performance of applying the above approach to flexible flow line scheduling with variable lot sizes for an actual manufacturing facility, comparing it to such alternative approaches as pair wise exchange improvement, tabu search, and simulated annealing procedures. The results show the efficacy of this approach for flexible flow line scheduling.
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NASA Astrophysics Data System (ADS)
Li, Zixiang; Janardhanan, Mukund Nilakantan; Tang, Qiuhua; Nielsen, Peter
2018-05-01
This article presents the first method to simultaneously balance and sequence robotic mixed-model assembly lines (RMALB/S), which involves three sub-problems: task assignment, model sequencing and robot allocation. A new mixed-integer programming model is developed to minimize makespan and, using CPLEX solver, small-size problems are solved for optimality. Two metaheuristics, the restarted simulated annealing algorithm and co-evolutionary algorithm, are developed and improved to address this NP-hard problem. The restarted simulated annealing method replaces the current temperature with a new temperature to restart the search process. The co-evolutionary method uses a restart mechanism to generate a new population by modifying several vectors simultaneously. The proposed algorithms are tested on a set of benchmark problems and compared with five other high-performing metaheuristics. The proposed algorithms outperform their original editions and the benchmarked methods. The proposed algorithms are able to solve the balancing and sequencing problem of a robotic mixed-model assembly line effectively and efficiently.
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Recent progress of quantum annealing
DOE Office of Scientific and Technical Information (OSTI.GOV)
Suzuki, Sei
2015-03-10
We review the recent progress of quantum annealing. Quantum annealing was proposed as a method to solve generic optimization problems. Recently a Canadian company has drawn a great deal of attention, as it has commercialized a quantum computer based on quantum annealing. Although the performance of quantum annealing is not sufficiently understood, it is likely that quantum annealing will be a practical method both on a conventional computer and on a quantum computer.
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Finding Maximum Cliques on the D-Wave Quantum Annealer
Chapuis, Guillaume; Djidjev, Hristo; Hahn, Georg; ...
2018-05-03
This work assesses the performance of the D-Wave 2X (DW) quantum annealer for finding a maximum clique in a graph, one of the most fundamental and important NP-hard problems. Because the size of the largest graphs DW can directly solve is quite small (usually around 45 vertices), we also consider decomposition algorithms intended for larger graphs and analyze their performance. For smaller graphs that fit DW, we provide formulations of the maximum clique problem as a quadratic unconstrained binary optimization (QUBO) problem, which is one of the two input types (together with the Ising model) acceptable by the machine, andmore » compare several quantum implementations to current classical algorithms such as simulated annealing, Gurobi, and third-party clique finding heuristics. We further estimate the contributions of the quantum phase of the quantum annealer and the classical post-processing phase typically used to enhance each solution returned by DW. We demonstrate that on random graphs that fit DW, no quantum speedup can be observed compared with the classical algorithms. On the other hand, for instances specifically designed to fit well the DW qubit interconnection network, we observe substantial speed-ups in computing time over classical approaches.« less
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Finding Maximum Cliques on the D-Wave Quantum Annealer
DOE Office of Scientific and Technical Information (OSTI.GOV)
Chapuis, Guillaume; Djidjev, Hristo; Hahn, Georg
This work assesses the performance of the D-Wave 2X (DW) quantum annealer for finding a maximum clique in a graph, one of the most fundamental and important NP-hard problems. Because the size of the largest graphs DW can directly solve is quite small (usually around 45 vertices), we also consider decomposition algorithms intended for larger graphs and analyze their performance. For smaller graphs that fit DW, we provide formulations of the maximum clique problem as a quadratic unconstrained binary optimization (QUBO) problem, which is one of the two input types (together with the Ising model) acceptable by the machine, andmore » compare several quantum implementations to current classical algorithms such as simulated annealing, Gurobi, and third-party clique finding heuristics. We further estimate the contributions of the quantum phase of the quantum annealer and the classical post-processing phase typically used to enhance each solution returned by DW. We demonstrate that on random graphs that fit DW, no quantum speedup can be observed compared with the classical algorithms. On the other hand, for instances specifically designed to fit well the DW qubit interconnection network, we observe substantial speed-ups in computing time over classical approaches.« less
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Quantum annealing for combinatorial clustering
NASA Astrophysics Data System (ADS)
Kumar, Vaibhaw; Bass, Gideon; Tomlin, Casey; Dulny, Joseph
2018-02-01
Clustering is a powerful machine learning technique that groups "similar" data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum; however, the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local search-based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global search-based techniques, such as simulated annealing, tabu search, and genetic algorithms, may offer better quality results but can be too time-consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization problem and discuss two clustering algorithms which are then implemented on commercially available quantum annealing hardware, as well as on a purely classical solver "qbsolv." The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques.
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Stochastic optimization of broadband reflecting photonic structures.
Estrada-Wiese, D; Del Río-Chanona, E A; Del Río, J A
2018-01-19
Photonic crystals (PCs) are built to control the propagation of light within their structure. These can be used for an assortment of applications where custom designed devices are of interest. Among them, one-dimensional PCs can be produced to achieve the reflection of specific and broad wavelength ranges. However, their design and fabrication are challenging due to the diversity of periodic arrangement and layer configuration that each different PC needs. In this study, we present a framework to design high reflecting PCs for any desired wavelength range. Our method combines three stochastic optimization algorithms (Random Search, Particle Swarm Optimization and Simulated Annealing) along with a reduced space-search methodology to obtain a custom and optimized PC configuration. The optimization procedure is evaluated through theoretical reflectance spectra calculated by using the Equispaced Thickness Method, which improves the simulations due to the consideration of incoherent light transmission. We prove the viability of our procedure by fabricating different reflecting PCs made of porous silicon and obtain good agreement between experiment and theory using a merit function. With this methodology, diverse reflecting PCs can be designed for any applications and fabricated with different materials.
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Power and Efficiency Optimized in Traveling-Wave Tubes Over a Broad Frequency Bandwidth
NASA Technical Reports Server (NTRS)
Wilson, Jeffrey D.
2001-01-01
A traveling-wave tube (TWT) is an electron beam device that is used to amplify electromagnetic communication waves at radio and microwave frequencies. TWT's are critical components in deep space probes, communication satellites, and high-power radar systems. Power conversion efficiency is of paramount importance for TWT's employed in deep space probes and communication satellites. A previous effort was very successful in increasing efficiency and power at a single frequency (ref. 1). Such an algorithm is sufficient for narrow bandwidth designs, but for optimal designs in applications that require high radiofrequency power over a wide bandwidth, such as high-density communications or high-resolution radar, the variation of the circuit response with respect to frequency must be considered. This work at the NASA Glenn Research Center is the first to develop techniques for optimizing TWT efficiency and output power over a broad frequency bandwidth (ref. 2). The techniques are based on simulated annealing, which has the advantage over conventional optimization techniques in that it enables the best possible solution to be obtained (ref. 3). Two new broadband simulated annealing algorithms were developed that optimize (1) minimum saturated power efficiency over a frequency bandwidth and (2) simultaneous bandwidth and minimum power efficiency over the frequency band with constant input power. The algorithms were incorporated into the NASA coupled-cavity TWT computer model (ref. 4) and used to design optimal phase velocity tapers using the 59- to 64-GHz Hughes 961HA coupled-cavity TWT as a baseline model. In comparison to the baseline design, the computational results of the first broad-band design algorithm show an improvement of 73.9 percent in minimum saturated efficiency (see the top graph). The second broadband design algorithm (see the bottom graph) improves minimum radiofrequency efficiency with constant input power drive by a factor of 2.7 at the high band edge (64 GHz) and increases simultaneous bandwidth by 500 MHz.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Nandipati, Giridhar; Setyawan, Wahyu; Heinisch, Howard L.
2015-07-01
The results of object kinetic Monte Carlo (OKMC) simulations of the annealing of primary cascade damage in bulk tungsten using a comprehensive database of cascades obtained from molecular dynamics (Setyawan et al.) are described as a function of primary knock-on atom (PKA) energy at temperatures of 300, 1025 and 2050 K. An increase in SIA clustering coupled with a decrease in vacancy clustering with increasing temperature, in addition to the disparate mobilities of SIAs versus vacancies, causes an interesting effect of temperature on cascade annealing. The annealing efficiency (the ratio of the number of defects after and before annealing) exhibitsmore » an inverse U-shape curve as a function of temperature. The capabilities of the newly developed OKMC code KSOME (kinetic simulations of microstructure evolution) used to carry out these simulations are described.« less
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Data Analysis for Rotationally Resolved Spectra: A Simulated Annealing Approach
1992-05-29
Table V. Expe rimental Data PSA was used to analyze high resolution infrared spectra of 2- fluoroethanol (2FE) and difluoroethane (DFE). Although the...does not inhere in the model used to calculate spectra, so the match to the experimental spectrum is not ideal. 8. PSA optimization of Difluoroethane ...A) The experimental spectrum of Difluoroethane . B) The spectrum generated from the starting state given to PSA. C) The spectrum generated from the
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Traveling salesman problem with a center.
Lipowski, Adam; Lipowska, Dorota
2005-06-01
We study a traveling salesman problem where the path is optimized with a cost function that includes its length L as well as a certain measure C of its distance from the geometrical center of the graph. Using simulated annealing (SA) we show that such a problem has a transition point that separates two phases differing in the scaling behavior of L and C, in efficiency of SA, and in the shape of minimal paths.
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Product Mix Selection Using AN Evolutionary Technique
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Vasant, Pandian
2009-08-01
This paper proposes an evolutionary technique for the solution of a real—life industrial problem and particular for the product mix selection problem. The evolutionary technique is a combination of a genetic algorithm that preserves the feasibility of the trial solutions with penalties and some local optimization method. The goal of this paper has been achieved in finding the best near optimal solution for the profit fitness function respect to vagueness factor and level of satisfaction. The findings of the profit values will be very useful for the decision makers in the industrial engineering sector for the implementation purpose. It's possible to improve the solutions obtained in this study by employing other meta-heuristic methods such as simulated annealing, tabu Search, ant colony optimization, particle swarm optimization and artificial immune systems.
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Boosting quantum annealer performance via sample persistence
NASA Astrophysics Data System (ADS)
Karimi, Hamed; Rosenberg, Gili
2017-07-01
We propose a novel method for reducing the number of variables in quadratic unconstrained binary optimization problems, using a quantum annealer (or any sampler) to fix the value of a large portion of the variables to values that have a high probability of being optimal. The resulting problems are usually much easier for the quantum annealer to solve, due to their being smaller and consisting of disconnected components. This approach significantly increases the success rate and number of observations of the best known energy value in samples obtained from the quantum annealer, when compared with calling the quantum annealer without using it, even when using fewer annealing cycles. Use of the method results in a considerable improvement in success metrics even for problems with high-precision couplers and biases, which are more challenging for the quantum annealer to solve. The results are further enhanced by applying the method iteratively and combining it with classical pre-processing. We present results for both Chimera graph-structured problems and embedded problems from a real-world application.
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2017-01-01
The accurate prediction of protein chemical shifts using a quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of a protein backbone and CB chemical shifts (ProCS15, PeerJ, 2016, 3, e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1–0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If an amino acid specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiments that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differ in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift. PMID:28451325
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Effects of annealing temperature on the H2-sensing properties of Pd-decorated WO3 nanorods
NASA Astrophysics Data System (ADS)
Lee, Sangmin; Lee, Woo Seok; Lee, Jae Kyung; Hyun, Soong Keun; Lee, Chongmu; Choi, Seungbok
2018-03-01
The temperature of the post-annealing treatment carried out after noble metal deposition onto semiconducting metal oxides (SMOs) must be carefully optimized to maximize the sensing performance of the metal-decorated SMO sensors. WO3 nanorods were synthesized by thermal evaporation of WO3 powders and decorated with Pd nanoparticles using a sol-gel method, followed by an annealing process. The effects of the annealing temperature on the hydrogen gas-sensing properties of the Pd-decorated WO3 nanorods were then examined; the optimal annealing temperature, leading to the highest response of the WO3 nanorod sensor to H2, was determined to be 600 °C. Post-annealing at 600 °C resulted in nanorods with the highest surface area-to-volume ratio, as well as in the optimal size and the largest number of deposited Pd nanoparticles, leading to the highest response and the shortest response/recovery times toward H2. The improved H2-sensing performance of the Pd-decorated WO3 nanorod sensor, compared to a sensor based on pristine WO3 nanorods, is attributed to the enhanced catalytic activity, increased surface area-to-volume ratio, and higher amounts of surface defects.
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NASA Astrophysics Data System (ADS)
Huang, Yin; Chen, Jianhua; Xiong, Shaojun
2009-07-01
Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.
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Optimization of the resources management in fighting wildfires.
Martin-Fernández, Susana; Martínez-Falero, Eugenio; Pérez-González, J Manuel
2002-09-01
Wildfires lead to important economic, social, and environmental losses, especially in areas of Mediterranean climate where they are of a high intensity and frequency. Over the past 30 years there has been a dramatic surge in the development and use of fire spread models. However, given the chaotic nature of environmental systems, it is very difficult to develop real-time fire-extinguishing models. This article proposes a method of optimizing the performance of wildfire fighting resources such that losses are kept to a minimum. The optimization procedure includes discrete simulation algorithms and Bayesian optimization methods for discrete and continuous problems (simulated annealing and Bayesian global optimization). Fast calculus algorithms are applied to provide optimization outcomes in short periods of time such that the predictions of the model and the real behavior of the fire, combat resources, and meteorological conditions are similar. In addition, adaptive algorithms take into account the chaotic behavior of wildfire so that the system can be updated with data corresponding to the real situation to obtain a new optimum solution. The application of this method to the Northwest Forest of Madrid (Spain) is also described. This application allowed us to check that it is a helpful tool in the decision-making process.
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Optimization of the Resources Management in Fighting Wildfires
NASA Astrophysics Data System (ADS)
Martin-Fernández, Susana; Martínez-Falero, Eugenio; Pérez-González, J. Manuel
2002-09-01
Wildfires lead to important economic, social, and environmental losses, especially in areas of Mediterranean climate where they are of a high intensity and frequency. Over the past 30 years there has been a dramatic surge in the development and use of fire spread models. However, given the chaotic nature of environmental systems, it is very difficult to develop real-time fire-extinguishing models. This article proposes a method of optimizing the performance of wildfire fighting resources such that losses are kept to a minimum. The optimization procedure includes discrete simulation algorithms and Bayesian optimization methods for discrete and continuous problems (simulated annealing and Bayesian global optimization). Fast calculus algorithms are applied to provide optimization outcomes in short periods of time such that the predictions of the model and the real behavior of the fire, combat resources, and meteorological conditions are similar. In addition, adaptive algorithms take into account the chaotic behavior of wildfire so that the system can be updated with data corresponding to the real situation to obtain a new optimum solution. The application of this method to the Northwest Forest of Madrid (Spain) is also described. This application allowed us to check that it is a helpful tool in the decision-making process.
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Wang, Jun; Zhou, Bihua; Zhou, Shudao
2016-01-01
This paper proposes an improved cuckoo search (ICS) algorithm to establish the parameters of chaotic systems. In order to improve the optimization capability of the basic cuckoo search (CS) algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability. Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the algorithm can estimate parameters with high accuracy and reliability. Finally, the results are compared with the CS algorithm, genetic algorithm, and particle swarm optimization algorithm, and the compared results demonstrate the method is energy-efficient and superior. PMID:26880874
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Simulated Annealing in the Variable Landscape
NASA Astrophysics Data System (ADS)
Hasegawa, Manabu; Kim, Chang Ju
An experimental analysis is conducted to test whether the appropriate introduction of the smoothness-temperature schedule enhances the optimizing ability of the MASSS method, the combination of the Metropolis algorithm (MA) and the search-space smoothing (SSS) method. The test is performed on two types of random traveling salesman problems. The results show that the optimization performance of the MA is substantially improved by a single smoothing alone and slightly more by a single smoothing with cooling and by a de-smoothing process with heating. The performance is compared to that of the parallel tempering method and a clear advantage of the idea of smoothing is observed depending on the problem.
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Simulated annealing with probabilistic analysis for solving traveling salesman problems
NASA Astrophysics Data System (ADS)
Hong, Pei-Yee; Lim, Yai-Fung; Ramli, Razamin; Khalid, Ruzelan
2013-09-01
Simulated Annealing (SA) is a widely used meta-heuristic that was inspired from the annealing process of recrystallization of metals. Therefore, the efficiency of SA is highly affected by the annealing schedule. As a result, in this paper, we presented an empirical work to provide a comparable annealing schedule to solve symmetric traveling salesman problems (TSP). Randomized complete block design is also used in this study. The results show that different parameters do affect the efficiency of SA and thus, we propose the best found annealing schedule based on the Post Hoc test. SA was tested on seven selected benchmarked problems of symmetric TSP with the proposed annealing schedule. The performance of SA was evaluated empirically alongside with benchmark solutions and simple analysis to validate the quality of solutions. Computational results show that the proposed annealing schedule provides a good quality of solution.
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NASA Astrophysics Data System (ADS)
Ghosh, K.; Naresh, Y.; Srichakradhar Reddy, N.
2012-07-01
In this paper, we present theoretical analysis and computation for tuning the ground state (GS) photoluminescence (PL) emission of InAs/GaAs quantum dots (QDs) at telecommunication window of 1.3-1.55 μm by optimizing its height and base dimensions through quantum mechanical concepts. For this purpose, numerical modelling is carried out to calculate the quantized energy states of finite dimensional QDs so as to obtain the GS PL emission at or beyond 1.3 μm. Here, we also explored strain field altering the QD size distribution in multilayer heterostructure along with the changes in the PL spectra, simulation on post growth thermal annealing process which blueshifts the operating wavelength away from the vicinity of 1.3 μm and improvement of optical properties by varying the thickness of GaAs spacing. The results are discussed in detail which will serve as an important information tool for device scientist fabricating high quality semiconductor quantum structures with reduced defects at telecommunication wavelengths.
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Population annealing simulations of a binary hard-sphere mixture
NASA Astrophysics Data System (ADS)
Callaham, Jared; Machta, Jonathan
2017-06-01
Population annealing is a sequential Monte Carlo scheme well suited to simulating equilibrium states of systems with rough free energy landscapes. Here we use population annealing to study a binary mixture of hard spheres. Population annealing is a parallel version of simulated annealing with an extra resampling step that ensures that a population of replicas of the system represents the equilibrium ensemble at every packing fraction in an annealing schedule. The algorithm and its equilibration properties are described, and results are presented for a glass-forming fluid composed of a 50/50 mixture of hard spheres with diameter ratio of 1.4:1. For this system, we obtain precise results for the equation of state in the glassy regime up to packing fractions φ ≈0.60 and study deviations from the Boublik-Mansoori-Carnahan-Starling-Leland equation of state. For higher packing fractions, the algorithm falls out of equilibrium and a free volume fit predicts jamming at packing fraction φ ≈0.667 . We conclude that population annealing is an effective tool for studying equilibrium glassy fluids and the jamming transition.
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Computational Multiqubit Tunnelling in Programmable Quantum Annealers
2016-08-25
ARTICLE Received 3 Jun 2015 | Accepted 26 Nov 2015 | Published 7 Jan 2016 Computational multiqubit tunnelling in programmable quantum annealers...state itself. Quantum tunnelling has been hypothesized as an advantageous physical resource for optimization in quantum annealing. However, computational ...qubit tunnelling plays a computational role in a currently available programmable quantum annealer. We devise a probe for tunnelling, a computational
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An Evaluation of a Modified Simulated Annealing Algorithm for Various Formulations
1990-08-01
trials of the K"h Markov chain, is sufficiently close to q(c, ), the stationary distribution at ck la (Lk,c,,) - q(c.) < epsilon Requiring the final...Wiley and Sons . Aarts, E. H. L., & Van Laarhoven, P. J. M. (1985). Statistical cooling: A general approach to combinatorial optimization problems...Birkhoff, G. (1946). Tres observaciones sobre el algebra lineal, Rev. Univ. Nac. TucumanSer. A, 5, 147-151. Bohr, Niels (1913). Old quantum theory
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Dai, Jianrong; Que, William
2004-12-07
This paper introduces a method to simultaneously minimize the leaf travel distance and the tongue-and-groove effect for IMRT leaf sequences to be delivered in segmental mode. The basic idea is to add a large enough number of openings through cutting or splitting existing openings for those leaf pairs with openings fewer than the number of segments so that all leaf pairs have the same number of openings. The cutting positions are optimally determined with a simulated annealing technique called adaptive simulated annealing. The optimization goal is set to minimize the weighted summation of the leaf travel distance and tongue-and-groove effect. Its performance was evaluated with 19 beams from three clinical cases; one brain, one head-and-neck and one prostate case. The results show that it can reduce the leaf travel distance and (or) tongue-and-groove effect; the reduction of the leaf travel distance reaches its maximum of about 50% when minimized alone; the reduction of the tongue-and-groove reaches its maximum of about 70% when minimized alone. The maximum reduction in the leaf travel distance translates to a 1 to 2 min reduction in treatment delivery time per fraction, depending on leaf speed. If the method is implemented clinically, it could result in significant savings in treatment delivery time, and also result in significant reduction in the wear-and-tear of MLC mechanics.
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Automated design evolution of stereochemically randomized protein foldamers
NASA Astrophysics Data System (ADS)
Ranbhor, Ranjit; Kumar, Anil; Patel, Kirti; Ramakrishnan, Vibin; Durani, Susheel
2018-05-01
Diversification of chain stereochemistry opens up the possibilities of an ‘in principle’ increase in the design space of proteins. This huge increase in the sequence and consequent structural variation is aimed at the generation of smart materials. To diversify protein structure stereochemically, we introduced L- and D-α-amino acids as the design alphabet. With a sequence design algorithm, we explored the usage of specific variables such as chirality and the sequence of this alphabet in independent steps. With molecular dynamics, we folded stereochemically diverse homopolypeptides and evaluated their ‘fitness’ for possible design as protein-like foldamers. We propose a fitness function to prune the most optimal fold among 1000 structures simulated with an automated repetitive simulated annealing molecular dynamics (AR-SAMD) approach. The highly scored poly-leucine fold with sequence lengths of 24 and 30 amino acids were later sequence-optimized using a Dead End Elimination cum Monte Carlo based optimization tool. This paper demonstrates a novel approach for the de novo design of protein-like foldamers.
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Optimal design of wide-view-angle waveplate used for polarimetric diagnosis of lithography system
NASA Astrophysics Data System (ADS)
Gu, Honggang; Jiang, Hao; Zhang, Chuanwei; Chen, Xiuguo; Liu, Shiyuan
2016-03-01
The diagnosis and control of the polarization aberrations is one of the main concerns in a hyper numerical aperture (NA) lithography system. Waveplates are basic and indispensable optical components in the polarimetric diagnosis tools for the immersion lithography system. The retardance of a birefringent waveplate is highly sensitive to the incident angle of the light, which makes the conventional waveplate not suitable to be applied in the polarimetric diagnosis for the immersion lithography system with a hyper NA. In this paper, we propose a method for the optimal design of a wideview- angle waveplate by combining two positive waveplates made from magnesium fluoride (MgF2) and two negative waveplates made from sapphire using the simulated annealing algorithm. Theoretical derivations and numerical simulations are performed and the results demonstrate that the maximum variation in the retardance of the optimally designed wide-view-angle waveplate is less than +/- 0.35° for a wide-view-angle range of +/- 20°.
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Tunneling and speedup in quantum optimization for permutation-symmetric problems
Muthukrishnan, Siddharth; Albash, Tameem; Lidar, Daniel A.
2016-07-21
Tunneling is often claimed to be the key mechanism underlying possible speedups in quantum optimization via quantum annealing (QA), especially for problems featuring a cost function with tall and thin barriers. We present and analyze several counterexamples from the class of perturbed Hamming weight optimization problems with qubit permutation symmetry. We first show that, for these problems, the adiabatic dynamics that make tunneling possible should be understood not in terms of the cost function but rather the semiclassical potential arising from the spin-coherent path-integral formalism. We then provide an example where the shape of the barrier in the final costmore » function is short and wide, which might suggest no quantum advantage for QA, yet where tunneling renders QA superior to simulated annealing in the adiabatic regime. However, the adiabatic dynamics turn out not be optimal. Instead, an evolution involving a sequence of diabatic transitions through many avoided-level crossings, involving no tunneling, is optimal and outperforms adiabatic QA. We show that this phenomenon of speedup by diabatic transitions is not unique to this example, and we provide an example where it provides an exponential speedup over adiabatic QA. In yet another twist, we show that a classical algorithm, spin-vector dynamics, is at least as efficient as diabatic QA. Lastly, in a different example with a convex cost function, the diabatic transitions result in a speedup relative to both adiabatic QA with tunneling and classical spin-vector dynamics.« less
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Tunneling and speedup in quantum optimization for permutation-symmetric problems
DOE Office of Scientific and Technical Information (OSTI.GOV)
Muthukrishnan, Siddharth; Albash, Tameem; Lidar, Daniel A.
Tunneling is often claimed to be the key mechanism underlying possible speedups in quantum optimization via quantum annealing (QA), especially for problems featuring a cost function with tall and thin barriers. We present and analyze several counterexamples from the class of perturbed Hamming weight optimization problems with qubit permutation symmetry. We first show that, for these problems, the adiabatic dynamics that make tunneling possible should be understood not in terms of the cost function but rather the semiclassical potential arising from the spin-coherent path-integral formalism. We then provide an example where the shape of the barrier in the final costmore » function is short and wide, which might suggest no quantum advantage for QA, yet where tunneling renders QA superior to simulated annealing in the adiabatic regime. However, the adiabatic dynamics turn out not be optimal. Instead, an evolution involving a sequence of diabatic transitions through many avoided-level crossings, involving no tunneling, is optimal and outperforms adiabatic QA. We show that this phenomenon of speedup by diabatic transitions is not unique to this example, and we provide an example where it provides an exponential speedup over adiabatic QA. In yet another twist, we show that a classical algorithm, spin-vector dynamics, is at least as efficient as diabatic QA. Lastly, in a different example with a convex cost function, the diabatic transitions result in a speedup relative to both adiabatic QA with tunneling and classical spin-vector dynamics.« less
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Separating figure from ground with a parallel network.
Kienker, P K; Sejnowski, T J; Hinton, G E; Schumacher, L E
1986-01-01
The differentiation of figure from ground plays an important role in the perceptual organization of visual stimuli. The rapidity with which we can discriminate the inside from the outside of a figure suggests that at least this step in the process may be performed in visual cortex by a large number of neurons in several different areas working together in parallel. We have attempted to simulate this collective computation by designing a network of simple processing units that receives two types of information: bottom-up input from the image containing the outlines of a figure, which may be incomplete, and a top-down attentional input that biases one part of the image to be the inside of the figure. No presegmentation of the image was assumed. Two methods for performing the computation were explored: gradient descent, which seeks locally optimal states, and simulated annealing, which attempts to find globally optimal states by introducing noise into the computation. For complete outlines, gradient descent was faster, but the range of input parameters leading to successful performance was very narrow. In contrast, simulated annealing was more robust: it worked over a wider range of attention parameters and a wider range of outlines, including incomplete ones. Our network model is too simplified to serve as a model of human performance, but it does demonstrate that one global property of outlines can be computed through local interactions in a parallel network. Some features of the model, such as the role of noise in escaping from nonglobal optima, may generalize to more realistic models.
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Budinich, M
1996-02-15
Unsupervised learning applied to an unstructured neural network can give approximate solutions to the traveling salesman problem. For 50 cities in the plane this algorithm performs like the elastic net of Durbin and Willshaw (1987) and it improves when increasing the number of cities to get better than simulated annealing for problems with more than 500 cities. In all the tests this algorithm requires a fraction of the time taken by simulated annealing.
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NASA Astrophysics Data System (ADS)
Gaillot, P.; Bardaine, T.; Lyon-Caen, H.
2004-12-01
Since recent years, various automatic phase pickers based on the wavelet transform have been developed. The main motivation for using wavelet transform is that they are excellent at finding the characteristics of transient signals, they have good time resolution at all periods, and they are easy to program for fast execution. Thus, the time-scale properties and flexibility of the wavelets allow detection of P and S phases in a broad frequency range making their utilization possible in various context. However, the direct application of an automatic picking program in a different context/network than the one for which it has been initially developed is quickly tedious. In fact, independently of the strategy involved in automatic picking algorithms (window average, autoregressive, beamforming, optimization filtering, neuronal network), all developed algorithms use different parameters that depend on the objective of the seismological study, the region and the seismological network. Classically, these parameters are manually defined by trial-error or calibrated learning stage. In order to facilitate this laborious process, we have developed an automated method that provide optimal parameters for the picking programs. The set of parameters can be explored using simulated annealing which is a generic name for a family of optimization algorithms based on the principle of stochastic relaxation. The optimization process amounts to systematically modifying an initial realization so as to decrease the value of the objective function, getting the realization acceptably close to the target statistics. Different formulations of the optimization problem (objective function) are discussed using (1) world seismicity data recorded by the French national seismic monitoring network (ReNass), (2) regional seismicity data recorded in the framework of the Corinth Rift Laboratory (CRL) experiment, (3) induced seismicity data from the gas field of Lacq (Western Pyrenees), and (4) micro-seismicity data from glacier monitoring. The developed method is discussed and tested using our wavelet version of the standard STA-LTA algorithm.
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Improving the magnetoelectric performance of Metglas/PZT laminates by annealing in a magnetic field.
Freeman, E; Harper, J; Goel, N; Gilbert, I; Unguris, J; Schiff, S J; Tadigadapa, S
2017-01-01
A comprehensive investigation of magnetostriction optimization in Metglas 2605SA1 ribbons is performed to enhance magnetoelectric performance. We explore a range of annealing conditions to relieve remnant stress and align the magnetic domains in the Metglas, while minimizing unwanted crystallization. The magnetostriction coefficient, magnetoelectric coefficient, and magnetic domain alignment are correlated to optimize magnetoelectric performance. We report on direct magnetostriction observed by in-plane Doppler vibrometer and domain imagining using scanning electron microscopy with polarization analysis for a range of annealing conditions. We find that annealing in an oxygen-free environment at 400 °C for 30 min yields an optimal magnetoelectric coefficient, magnetostriction and magnetostriction coefficient. The optimized ribbons had a magnetostriction of 50.6 ± 0.2 μ m m -1 and magnetoelectric coefficient of 79.3 ± 1.5 μ m m -1 mT -1 . The optimized Metglas 2605SA1 ribbons and PZT-5A (d 31 mode) sensor achieves a magnetic noise floor of approximately 600 pT Hz -1/2 at 100 Hz and a magnetoelectric coefficient of 6.1 ± 0.03 MV m -1 T -1 .
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Improving the magnetoelectric performance of Metglas/PZT laminates by annealing in a magnetic field
NASA Astrophysics Data System (ADS)
Freeman, E.; Harper, J.; Goel, N.; Gilbert, I.; Unguris, J.; Schiff, S. J.; Tadigadapa, S.
2017-08-01
A comprehensive investigation of magnetostriction optimization in Metglas 2605SA1 ribbons is performed to enhance magnetoelectric performance. We explore a range of annealing conditions to relieve remnant stress and align the magnetic domains in the Metglas, while minimizing unwanted crystallization. The magnetostriction coefficient, magnetoelectric coefficient, and magnetic domain alignment are correlated to optimize magnetoelectric performance. We report on direct magnetostriction observed by in-plane Doppler vibrometer and domain imagining using scanning electron microscopy with polarization analysis for a range of annealing conditions. We find that annealing in an oxygen-free environment at 400 {}\\circ {{C}} for 30 min yields an optimal magnetoelectric coefficient, magnetostriction and magnetostriction coefficient. The optimized ribbons had a magnetostriction of 50.6 ± 0.2 μ {{m}} {{{m}}}-1 and magnetoelectric coefficient of 79.3 ± 1.5 µm m-1 mT-1. The optimized Metglas 2605SA1 ribbons and PZT-5A (d31 mode) sensor achieves a magnetic noise floor of approximately 600 pT {{{H}}{{z}}}-1/2 at 100 Hz and a magnetoelectric coefficient of 6.1 ± 0.03 MV m-1 T-1.
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Combined Simulated Annealing and Genetic Algorithm Approach to Bus Network Design
NASA Astrophysics Data System (ADS)
Liu, Li; Olszewski, Piotr; Goh, Pong-Chai
A new method - combined simulated annealing (SA) and genetic algorithm (GA) approach is proposed to solve the problem of bus route design and frequency setting for a given road network with fixed bus stop locations and fixed travel demand. The method involves two steps: a set of candidate routes is generated first and then the best subset of these routes is selected by the combined SA and GA procedure. SA is the main process to search for a better solution to minimize the total system cost, comprising user and operator costs. GA is used as a sub-process to generate new solutions. Bus demand assignment on two alternative paths is performed at the solution evaluation stage. The method was implemented on four theoretical grid networks of different size and a benchmark network. Several GA operators (crossover and mutation) were utilized and tested for their effectiveness. The results show that the proposed method can efficiently converge to the optimal solution on a small network but computation time increases significantly with network size. The method can also be used for other transport operation management problems.
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Elimination of Bimodal Size in InAs/GaAs Quantum Dots for Preparation of 1.3-μm Quantum Dot Lasers
NASA Astrophysics Data System (ADS)
Su, Xiang-Bin; Ding, Ying; Ma, Ben; Zhang, Ke-Lu; Chen, Ze-Sheng; Li, Jing-Lun; Cui, Xiao-Ran; Xu, Ying-Qiang; Ni, Hai-Qiao; Niu, Zhi-Chuan
2018-02-01
The device characteristics of semiconductor quantum dot lasers have been improved with progress in active layer structures. Self-assembly formed InAs quantum dots grown on GaAs had been intensively promoted in order to achieve quantum dot lasers with superior device performances. In the process of growing high-density InAs/GaAs quantum dots, bimodal size occurs due to large mismatch and other factors. The bimodal size in the InAs/GaAs quantum dot system is eliminated by the method of high-temperature annealing and optimized the in situ annealing temperature. The annealing temperature is taken as the key optimization parameters, and the optimal annealing temperature of 680 °C was obtained. In this process, quantum dot growth temperature, InAs deposition, and arsenic (As) pressure are optimized to improve quantum dot quality and emission wavelength. A 1.3-μm high-performance F-P quantum dot laser with a threshold current density of 110 A/cm2 was demonstrated.
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Elimination of Bimodal Size in InAs/GaAs Quantum Dots for Preparation of 1.3-μm Quantum Dot Lasers.
Su, Xiang-Bin; Ding, Ying; Ma, Ben; Zhang, Ke-Lu; Chen, Ze-Sheng; Li, Jing-Lun; Cui, Xiao-Ran; Xu, Ying-Qiang; Ni, Hai-Qiao; Niu, Zhi-Chuan
2018-02-21
The device characteristics of semiconductor quantum dot lasers have been improved with progress in active layer structures. Self-assembly formed InAs quantum dots grown on GaAs had been intensively promoted in order to achieve quantum dot lasers with superior device performances. In the process of growing high-density InAs/GaAs quantum dots, bimodal size occurs due to large mismatch and other factors. The bimodal size in the InAs/GaAs quantum dot system is eliminated by the method of high-temperature annealing and optimized the in situ annealing temperature. The annealing temperature is taken as the key optimization parameters, and the optimal annealing temperature of 680 °C was obtained. In this process, quantum dot growth temperature, InAs deposition, and arsenic (As) pressure are optimized to improve quantum dot quality and emission wavelength. A 1.3-μm high-performance F-P quantum dot laser with a threshold current density of 110 A/cm 2 was demonstrated.
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NASA Technical Reports Server (NTRS)
Sohn, Andrew; Biswas, Rupak
1996-01-01
Solving the hard Satisfiability Problem is time consuming even for modest-sized problem instances. Solving the Random L-SAT Problem is especially difficult due to the ratio of clauses to variables. This report presents a parallel synchronous simulated annealing method for solving the Random L-SAT Problem on a large-scale distributed-memory multiprocessor. In particular, we use a parallel synchronous simulated annealing procedure, called Generalized Speculative Computation, which guarantees the same decision sequence as sequential simulated annealing. To demonstrate the performance of the parallel method, we have selected problem instances varying in size from 100-variables/425-clauses to 5000-variables/21,250-clauses. Experimental results on the AP1000 multiprocessor indicate that our approach can satisfy 99.9 percent of the clauses while giving almost a 70-fold speedup on 500 processors.
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Simulation of radiation damage in minerals by sequential ion irradiations
NASA Astrophysics Data System (ADS)
Nakasuga, W. M.; Li, W.; Ewing, R. C.
2015-12-01
Radiation effects due to α-decay of U and Th and spontaneous fission of 238U control the production and recovery of the radiation-induced structure of minerals, as well as the diffusion of elements through the mineral host. However, details of how the damage microstructure is produced and annealed remain unknown. Our recent ion beam experiments demonstrate that ionizing radiation from the α-particle recovers the damage structure. Thus, the damage structure is not only the result of the thermal hisotry of the sample, but also of the complex interaction between ionizing and ballistic damage mechanisms. By combining ion irradiations with transmission electron microscopy (TEM), we have simulated the damage produced by α-decay and fission. The α-particle induced annealing has been simulated by in situ TEM observation of consecutive ion-irradiations: i.) 1 MeV Kr2+ (simulating 70 keV α-recoils induced damage), ii.) followed by 400 keV He+ (simulating 4.5 MeV α-particle induced annealing). Thus, in addition to the well-established effects of thermal annealing, the α-particle annealing effects, as evidenced by partical recrystallization of the originally, fully-amorphous apatite upon the α-particle irriadations, should also be considered when evaluating diffusion and release of elements, such as He. In addition, the fission track annealing has been simulated by a new sample preparation method that allows for direct observation of radiation damage recovery at each point along the length of latent tracks created by 80 MeV Xe ions (a typical fission fragment). The initial, rapid reduction in etched track length during isothermal annealing is explained by the rapid annealing of those sections of the track with smaller diameters, as observed directly by in situ TEM. In summary, the atomic-scale investigation of radiation damage in minerals is critical to understanding of the influence of raidation damage on diffusion and kinetics that are fundamental to geochronology.
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A novel metaheuristic for continuous optimization problems: Virus optimization algorithm
NASA Astrophysics Data System (ADS)
Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue
2016-01-01
A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.
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Metaheuristic Optimization and its Applications in Earth Sciences
NASA Astrophysics Data System (ADS)
Yang, Xin-She
2010-05-01
A common but challenging task in modelling geophysical and geological processes is to handle massive data and to minimize certain objectives. This can essentially be considered as an optimization problem, and thus many new efficient metaheuristic optimization algorithms can be used. In this paper, we will introduce some modern metaheuristic optimization algorithms such as genetic algorithms, harmony search, firefly algorithm, particle swarm optimization and simulated annealing. We will also discuss how these algorithms can be applied to various applications in earth sciences, including nonlinear least-squares, support vector machine, Kriging, inverse finite element analysis, and data-mining. We will present a few examples to show how different problems can be reformulated as optimization. Finally, we will make some recommendations for choosing various algorithms to suit various problems. References 1) D. H. Wolpert and W. G. Macready, No free lunch theorems for optimization, IEEE Trans. Evolutionary Computation, Vol. 1, 67-82 (1997). 2) X. S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, (2008). 3) X. S. Yang, Mathematical Modelling for Earth Sciences, Dunedin Academic Press, (2008).
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Performance of quantum annealing on random Ising problems implemented using the D-Wave Two
NASA Astrophysics Data System (ADS)
Wang, Zhihui; Job, Joshua; Rønnow, Troels F.; Troyer, Matthias; Lidar, Daniel A.; USC Collaboration; ETH Collaboration
2014-03-01
Detecting a possible speedup of quantum annealing compared to classical algorithms is a pressing task in experimental adiabatic quantum computing. In this talk, we discuss the performance of the D-Wave Two quantum annealing device on Ising spin glass problems. The expected time to solution for the device to solve random instances with up to 503 spins and with specified coupling ranges is evaluated while carefully addressing the issue of statistical errors. We perform a systematic comparison of the expected time to solution between the D-Wave Two and classical stochastic solvers, specifically simulated annealing, and simulated quantum annealing based on quantum Monte Carlo, and discuss the question of speedup.
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A coherent Ising machine for 2000-node optimization problems
NASA Astrophysics Data System (ADS)
Inagaki, Takahiro; Haribara, Yoshitaka; Igarashi, Koji; Sonobe, Tomohiro; Tamate, Shuhei; Honjo, Toshimori; Marandi, Alireza; McMahon, Peter L.; Umeki, Takeshi; Enbutsu, Koji; Tadanaga, Osamu; Takenouchi, Hirokazu; Aihara, Kazuyuki; Kawarabayashi, Ken-ichi; Inoue, Kyo; Utsunomiya, Shoko; Takesue, Hiroki
2016-11-01
The analysis and optimization of complex systems can be reduced to mathematical problems collectively known as combinatorial optimization. Many such problems can be mapped onto ground-state search problems of the Ising model, and various artificial spin systems are now emerging as promising approaches. However, physical Ising machines have suffered from limited numbers of spin-spin couplings because of implementations based on localized spins, resulting in severe scalability problems. We report a 2000-spin network with all-to-all spin-spin couplings. Using a measurement and feedback scheme, we coupled time-multiplexed degenerate optical parametric oscillators to implement maximum cut problems on arbitrary graph topologies with up to 2000 nodes. Our coherent Ising machine outperformed simulated annealing in terms of accuracy and computation time for a 2000-node complete graph.
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NASA Astrophysics Data System (ADS)
Biswas, Katja
2017-09-01
A computational method is presented which is capable to obtain low lying energy structures of topological amorphous systems. The method merges a differential mutation genetic algorithm with simulated annealing. This is done by incorporating a thermal selection criterion, which makes it possible to reliably obtain low lying minima with just a small population size and is suitable for multimodal structural optimization. The method is tested on the structural optimization of amorphous graphene from unbiased atomic starting configurations. With just a population size of six systems, energetically very low structures are obtained. While each of the structures represents a distinctly different arrangement of the atoms, their properties, such as energy, distribution of rings, radial distribution function, coordination number, and distribution of bond angles, are very similar.
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Adaptive multiple super fast simulated annealing for stochastic microstructure reconstruction
DOE Office of Scientific and Technical Information (OSTI.GOV)
Ryu, Seun; Lin, Guang; Sun, Xin
2013-01-01
Fast image reconstruction from statistical information is critical in image fusion from multimodality chemical imaging instrumentation to create high resolution image with large domain. Stochastic methods have been used widely in image reconstruction from two point correlation function. The main challenge is to increase the efficiency of reconstruction. A novel simulated annealing method is proposed for fast solution of image reconstruction. Combining the advantage of very fast cooling schedules, dynamic adaption and parallelization, the new simulation annealing algorithm increases the efficiencies by several orders of magnitude, making the large domain image fusion feasible.
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A Novel Coupled Resonator Photonic Crystal Design in Lithium Niobate for Electrooptic Applications
Ozturk, Birol; Yavuzcetin, Ozgur; Sridhar, Srinivas
2015-01-01
High-aspect-ratio photonic crystal air-hole fabrication on bulk Lithium Niobate (LN) substrates is extremely difficult due to its inherent resistance to etching, resulting in conical structures and high insertion losses. Here, we propose a novel coupled resonator photonic crystal (CRPC) design, combining a coupled resonator approach with that of Bragg gratings. CRPC design parameters were optimized by analytical calculations and FDTD simulations. CRPC structures with optimized parameters were fabricated and electrooptically tested on bulk LN annealed proton exchange waveguides. Low insertion loss and large electrooptic effect were observed with the fabricated devices, making the CRPC design a promising structure for electroopticmore » device applications.« less
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Study of the temperature configuration of parallel tempering for the traveling salesman problem
NASA Astrophysics Data System (ADS)
Hasegawa, Manabu
The effective temperature configuration of parallel tempering (PT) in finite-time optimization is studied for the solution of the traveling salesman problem. An experimental analysis is conducted to decide the relative importance of the two characteristic temperatures, the specific-heat-peak temperature referred to in the general guidelines and the effective intermediate temperature identified in the recent study on simulated annealing (SA). The results show that the operation near the former has no notable significance contrary to the conventional belief but that the operation near the latter plays a crucial role in fulfilling the optimization function of PT. The method shares the same origin of effectiveness with the SA and SA-related algorithms.
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Molecular dynamics simulations and photoluminescence measurements of annealed ZnO surfaces
NASA Astrophysics Data System (ADS)
Min, Tjun Kit; Yoon, Tiem Leong; Ling, Chuo Ann; Mahmud, Shahrom; Lim, Thong Leng; Saw, Kim Guan
2017-06-01
The effect of thermal annealing on wurtzite ZnO, terminated by two surfaces, (000 1 bar) (which is oxygen-terminated) and (0 0 0 1) (which is Zn-terminated), is investigated via molecular dynamics simulation using reactive force field (ReaxFF). As a result of annealing at a threshold temperature range of 700 K
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Frausto-Solis, Juan; Liñán-García, Ernesto; Sánchez-Hernández, Juan Paulo; González-Barbosa, J Javier; González-Flores, Carlos; Castilla-Valdez, Guadalupe
2016-01-01
A new hybrid Multiphase Simulated Annealing Algorithm using Boltzmann and Bose-Einstein distributions (MPSABBE) is proposed. MPSABBE was designed for solving the Protein Folding Problem (PFP) instances. This new approach has four phases: (i) Multiquenching Phase (MQP), (ii) Boltzmann Annealing Phase (BAP), (iii) Bose-Einstein Annealing Phase (BEAP), and (iv) Dynamical Equilibrium Phase (DEP). BAP and BEAP are simulated annealing searching procedures based on Boltzmann and Bose-Einstein distributions, respectively. DEP is also a simulated annealing search procedure, which is applied at the final temperature of the fourth phase, which can be seen as a second Bose-Einstein phase. MQP is a search process that ranges from extremely high to high temperatures, applying a very fast cooling process, and is not very restrictive to accept new solutions. However, BAP and BEAP range from high to low and from low to very low temperatures, respectively. They are more restrictive for accepting new solutions. DEP uses a particular heuristic to detect the stochastic equilibrium by applying a least squares method during its execution. MPSABBE parameters are tuned with an analytical method, which considers the maximal and minimal deterioration of problem instances. MPSABBE was tested with several instances of PFP, showing that the use of both distributions is better than using only the Boltzmann distribution on the classical SA.
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NASA Astrophysics Data System (ADS)
Totz, Sonja; Eliseev, Alexey V.; Petri, Stefan; Flechsig, Michael; Caesar, Levke; Petoukhov, Vladimir; Coumou, Dim
2018-02-01
We present and validate a set of equations for representing the atmosphere's large-scale general circulation in an Earth system model of intermediate complexity (EMIC). These dynamical equations have been implemented in Aeolus 1.0, which is a statistical-dynamical atmosphere model (SDAM) and includes radiative transfer and cloud modules (Coumou et al., 2011; Eliseev et al., 2013). The statistical dynamical approach is computationally efficient and thus enables us to perform climate simulations at multimillennia timescales, which is a prime aim of our model development. Further, this computational efficiency enables us to scan large and high-dimensional parameter space to tune the model parameters, e.g., for sensitivity studies.Here, we present novel equations for the large-scale zonal-mean wind as well as those for planetary waves. Together with synoptic parameterization (as presented by Coumou et al., 2011), these form the mathematical description of the dynamical core of Aeolus 1.0.We optimize the dynamical core parameter values by tuning all relevant dynamical fields to ERA-Interim reanalysis data (1983-2009) forcing the dynamical core with prescribed surface temperature, surface humidity and cumulus cloud fraction. We test the model's performance in reproducing the seasonal cycle and the influence of the El Niño-Southern Oscillation (ENSO). We use a simulated annealing optimization algorithm, which approximates the global minimum of a high-dimensional function.With non-tuned parameter values, the model performs reasonably in terms of its representation of zonal-mean circulation, planetary waves and storm tracks. The simulated annealing optimization improves in particular the model's representation of the Northern Hemisphere jet stream and storm tracks as well as the Hadley circulation.The regions of high azonal wind velocities (planetary waves) are accurately captured for all validation experiments. The zonal-mean zonal wind and the integrated lower troposphere mass flux show good results in particular in the Northern Hemisphere. In the Southern Hemisphere, the model tends to produce too-weak zonal-mean zonal winds and a too-narrow Hadley circulation. We discuss possible reasons for these model biases as well as planned future model improvements and applications.
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Hasegawa, M
2011-03-01
The aim of the present study is to elucidate how simulated annealing (SA) works in its finite-time implementation by starting from the verification of its conventional optimization scenario based on equilibrium statistical mechanics. Two and one supplementary experiments, the design of which is inspired by concepts and methods developed for studies on liquid and glass, are performed on two types of random traveling salesman problems. In the first experiment, a newly parameterized temperature schedule is introduced to simulate a quasistatic process along the scenario and a parametric study is conducted to investigate the optimization characteristics of this adaptive cooling. In the second experiment, the search trajectory of the Metropolis algorithm (constant-temperature SA) is analyzed in the landscape paradigm in the hope of drawing a precise physical analogy by comparison with the corresponding dynamics of glass-forming molecular systems. These two experiments indicate that the effectiveness of finite-time SA comes not from equilibrium sampling at low temperature but from downward interbasin dynamics occurring before equilibrium. These dynamics work most effectively at an intermediate temperature varying with the total search time and thus this effective temperature is identified using the Deborah number. To test directly the role of these relaxation dynamics in the process of cooling, a supplementary experiment is performed using another parameterized temperature schedule with a piecewise variable cooling rate and the effect of this biased cooling is examined systematically. The results show that the optimization performance is not only dependent on but also sensitive to cooling in the vicinity of the above effec-tive temperature and that this feature is interpreted as a consequence of the presence or absence of the workable interbasin dynamics. It is confirmed for the present instances that the effectiveness of finite-time SA derives from the glassy relaxation dynamics occurring in the "landscape-influenced" temperature regime and that its naive optimization scenario should be rectified by considering the analogy with vitrification phenomena. A comprehensive guideline for the design of finite-time SA and SA-related algorithms is discussed on the basis of this rectified analogy.
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Hamzehpour, Hossein; Rasaei, M Reza; Sahimi, Muhammad
2007-05-01
We describe a method for the development of the optimal spatial distributions of the porosity phi and permeability k of a large-scale porous medium. The optimal distributions are constrained by static and dynamic data. The static data that we utilize are limited data for phi and k, which the method honors in the optimal model and utilizes their correlation functions in the optimization process. The dynamic data include the first-arrival (FA) times, at a number of receivers, of seismic waves that have propagated in the porous medium, and the time-dependent production rates of a fluid that flows in the medium. The method combines the simulated-annealing method with a simulator that solves numerically the three-dimensional (3D) acoustic wave equation and computes the FA times, and a second simulator that solves the 3D governing equation for the fluid's pressure as a function of time. To our knowledge, this is the first time that an optimization method has been developed to determine simultaneously the global minima of two distinct total energy functions. As a stringent test of the method's accuracy, we solve for flow of two immiscible fluids in the same porous medium, without using any data for the two-phase flow problem in the optimization process. We show that the optimal model, in addition to honoring the data, also yields accurate spatial distributions of phi and k, as well as providing accurate quantitative predictions for the single- and two-phase flow problems. The efficiency of the computations is discussed in detail.
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Preparation, optimization and property of PVA-HA/PAA composite hydrogel.
Chen, Kai; Liu, Jinlong; Yang, Xuehui; Zhang, Dekun
2017-09-01
PVA-HA/PAA composite hydrogel is prepared by freezing-thawing, PEG dehydration and annealing method. Orthogonal design method is used to choose the optimization combination. Results showed that HA and PVA have the maximum effect on water content. PVA and freezing-thawing cycles have the maximum effect on creep resistance and stress relaxation rate of hydrogel. Annealing temperature and freezing-thawing cycles have the maximum effect on compressive elastic modulus of hydrogel. Comparing with the water content and mechanical properties of 16 kinds of combination, PVA-HA/PAA composite hydrogel with freezing-thawing cycles of 3, annealing temperature of 120°C, PVA of 16%, HA of 2%, PAA of 4% has the optimization comprehensive properties. PVA-HA/PAA composite hydrogel has a porous network structure. There are some interactions between PVA, HA and PAA in hydrogel and the properties of hydrogel are strengthened. The annealing treatment improves the crystalline and crosslinking of hydrogel. Therefore, the annealing PVA-HA/PAA composite hydrogel has good thermostability, strength and mechanical properties. It also has good lubrication property and its friction coefficient is relative low. Copyright © 2017 Elsevier B.V. All rights reserved.
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Annealed importance sampling with constant cooling rate
NASA Astrophysics Data System (ADS)
Giovannelli, Edoardo; Cardini, Gianni; Gellini, Cristina; Pietraperzia, Giangaetano; Chelli, Riccardo
2015-02-01
Annealed importance sampling is a simulation method devised by Neal [Stat. Comput. 11, 125 (2001)] to assign weights to configurations generated by simulated annealing trajectories. In particular, the equilibrium average of a generic physical quantity can be computed by a weighted average exploiting weights and estimates of this quantity associated to the final configurations of the annealed trajectories. Here, we review annealed importance sampling from the perspective of nonequilibrium path-ensemble averages [G. E. Crooks, Phys. Rev. E 61, 2361 (2000)]. The equivalence of Neal's and Crooks' treatments highlights the generality of the method, which goes beyond the mere thermal-based protocols. Furthermore, we show that a temperature schedule based on a constant cooling rate outperforms stepwise cooling schedules and that, for a given elapsed computer time, performances of annealed importance sampling are, in general, improved by increasing the number of intermediate temperatures.
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Graph drawing using tabu search coupled with path relinking.
Dib, Fadi K; Rodgers, Peter
2018-01-01
Graph drawing, or the automatic layout of graphs, is a challenging problem. There are several search based methods for graph drawing which are based on optimizing an objective function which is formed from a weighted sum of multiple criteria. In this paper, we propose a new neighbourhood search method which uses a tabu search coupled with path relinking to optimize such objective functions for general graph layouts with undirected straight lines. To our knowledge, before our work, neither of these methods have been previously used in general multi-criteria graph drawing. Tabu search uses a memory list to speed up searching by avoiding previously tested solutions, while the path relinking method generates new solutions by exploring paths that connect high quality solutions. We use path relinking periodically within the tabu search procedure to speed up the identification of good solutions. We have evaluated our new method against the commonly used neighbourhood search optimization techniques: hill climbing and simulated annealing. Our evaluation examines the quality of the graph layout (objective function's value) and the speed of layout in terms of the number of evaluated solutions required to draw a graph. We also examine the relative scalability of each method. Our experimental results were applied to both random graphs and a real-world dataset. We show that our method outperforms both hill climbing and simulated annealing by producing a better layout in a lower number of evaluated solutions. In addition, we demonstrate that our method has greater scalability as it can layout larger graphs than the state-of-the-art neighbourhood search methods. Finally, we show that similar results can be produced in a real world setting by testing our method against a standard public graph dataset.
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Graph drawing using tabu search coupled with path relinking
Rodgers, Peter
2018-01-01
Graph drawing, or the automatic layout of graphs, is a challenging problem. There are several search based methods for graph drawing which are based on optimizing an objective function which is formed from a weighted sum of multiple criteria. In this paper, we propose a new neighbourhood search method which uses a tabu search coupled with path relinking to optimize such objective functions for general graph layouts with undirected straight lines. To our knowledge, before our work, neither of these methods have been previously used in general multi-criteria graph drawing. Tabu search uses a memory list to speed up searching by avoiding previously tested solutions, while the path relinking method generates new solutions by exploring paths that connect high quality solutions. We use path relinking periodically within the tabu search procedure to speed up the identification of good solutions. We have evaluated our new method against the commonly used neighbourhood search optimization techniques: hill climbing and simulated annealing. Our evaluation examines the quality of the graph layout (objective function’s value) and the speed of layout in terms of the number of evaluated solutions required to draw a graph. We also examine the relative scalability of each method. Our experimental results were applied to both random graphs and a real-world dataset. We show that our method outperforms both hill climbing and simulated annealing by producing a better layout in a lower number of evaluated solutions. In addition, we demonstrate that our method has greater scalability as it can layout larger graphs than the state-of-the-art neighbourhood search methods. Finally, we show that similar results can be produced in a real world setting by testing our method against a standard public graph dataset. PMID:29746576
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A quantum annealing architecture with all-to-all connectivity from local interactions.
Lechner, Wolfgang; Hauke, Philipp; Zoller, Peter
2015-10-01
Quantum annealers are physical devices that aim at solving NP-complete optimization problems by exploiting quantum mechanics. The basic principle of quantum annealing is to encode the optimization problem in Ising interactions between quantum bits (qubits). A fundamental challenge in building a fully programmable quantum annealer is the competing requirements of full controllable all-to-all connectivity and the quasi-locality of the interactions between physical qubits. We present a scalable architecture with full connectivity, which can be implemented with local interactions only. The input of the optimization problem is encoded in local fields acting on an extended set of physical qubits. The output is-in the spirit of topological quantum memories-redundantly encoded in the physical qubits, resulting in an intrinsic fault tolerance. Our model can be understood as a lattice gauge theory, where long-range interactions are mediated by gauge constraints. The architecture can be realized on various platforms with local controllability, including superconducting qubits, NV-centers, quantum dots, and atomic systems.
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A quantum annealing architecture with all-to-all connectivity from local interactions
Lechner, Wolfgang; Hauke, Philipp; Zoller, Peter
2015-01-01
Quantum annealers are physical devices that aim at solving NP-complete optimization problems by exploiting quantum mechanics. The basic principle of quantum annealing is to encode the optimization problem in Ising interactions between quantum bits (qubits). A fundamental challenge in building a fully programmable quantum annealer is the competing requirements of full controllable all-to-all connectivity and the quasi-locality of the interactions between physical qubits. We present a scalable architecture with full connectivity, which can be implemented with local interactions only. The input of the optimization problem is encoded in local fields acting on an extended set of physical qubits. The output is—in the spirit of topological quantum memories—redundantly encoded in the physical qubits, resulting in an intrinsic fault tolerance. Our model can be understood as a lattice gauge theory, where long-range interactions are mediated by gauge constraints. The architecture can be realized on various platforms with local controllability, including superconducting qubits, NV-centers, quantum dots, and atomic systems. PMID:26601316
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A Geographical Heuristic Routing Protocol for VANETs
Urquiza-Aguiar, Luis; Tripp-Barba, Carolina; Aguilar Igartua, Mónica
2016-01-01
Vehicular ad hoc networks (VANETs) leverage the communication system of Intelligent Transportation Systems (ITS). Recently, Delay-Tolerant Network (DTN) routing protocols have increased their popularity among the research community for being used in non-safety VANET applications and services like traffic reporting. Vehicular DTN protocols use geographical and local information to make forwarding decisions. However, current proposals only consider the selection of the best candidate based on a local-search. In this paper, we propose a generic Geographical Heuristic Routing (GHR) protocol that can be applied to any DTN geographical routing protocol that makes forwarding decisions hop by hop. GHR includes in its operation adaptations simulated annealing and Tabu-search meta-heuristics, which have largely been used to improve local-search results in discrete optimization. We include a complete performance evaluation of GHR in a multi-hop VANET simulation scenario for a reporting service. Our study analyzes all of the meaningful configurations of GHR and offers a statistical analysis of our findings by means of MANOVA tests. Our results indicate that the use of a Tabu list contributes to improving the packet delivery ratio by around 5% to 10%. Moreover, if Tabu is used, then the simulated annealing routing strategy gets a better performance than the selection of the best node used with carry and forwarding (default operation). PMID:27669254
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A Geographical Heuristic Routing Protocol for VANETs.
Urquiza-Aguiar, Luis; Tripp-Barba, Carolina; Aguilar Igartua, Mónica
2016-09-23
Vehicular ad hoc networks (VANETs) leverage the communication system of Intelligent Transportation Systems (ITS). Recently, Delay-Tolerant Network (DTN) routing protocols have increased their popularity among the research community for being used in non-safety VANET applications and services like traffic reporting. Vehicular DTN protocols use geographical and local information to make forwarding decisions. However, current proposals only consider the selection of the best candidate based on a local-search. In this paper, we propose a generic Geographical Heuristic Routing (GHR) protocol that can be applied to any DTN geographical routing protocol that makes forwarding decisions hop by hop. GHR includes in its operation adaptations simulated annealing and Tabu-search meta-heuristics, which have largely been used to improve local-search results in discrete optimization. We include a complete performance evaluation of GHR in a multi-hop VANET simulation scenario for a reporting service. Our study analyzes all of the meaningful configurations of GHR and offers a statistical analysis of our findings by means of MANOVA tests. Our results indicate that the use of a Tabu list contributes to improving the packet delivery ratio by around 5% to 10%. Moreover, if Tabu is used, then the simulated annealing routing strategy gets a better performance than the selection of the best node used with carry and forwarding (default operation).
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Global optimization method based on ray tracing to achieve optimum figure error compensation
NASA Astrophysics Data System (ADS)
Liu, Xiaolin; Guo, Xuejia; Tang, Tianjin
2017-02-01
Figure error would degrade the performance of optical system. When predicting the performance and performing system assembly, compensation by clocking of optical components around the optical axis is a conventional but user-dependent method. Commercial optical software cannot optimize this clocking. Meanwhile existing automatic figure-error balancing methods can introduce approximate calculation error and the build process of optimization model is complex and time-consuming. To overcome these limitations, an accurate and automatic global optimization method of figure error balancing is proposed. This method is based on precise ray tracing to calculate the wavefront error, not approximate calculation, under a given elements' rotation angles combination. The composite wavefront error root-mean-square (RMS) acts as the cost function. Simulated annealing algorithm is used to seek the optimal combination of rotation angles of each optical element. This method can be applied to all rotational symmetric optics. Optimization results show that this method is 49% better than previous approximate analytical method.
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Stochastic Methods for Aircraft Design
NASA Technical Reports Server (NTRS)
Pelz, Richard B.; Ogot, Madara
1998-01-01
The global stochastic optimization method, simulated annealing (SA), was adapted and applied to various problems in aircraft design. The research was aimed at overcoming the problem of finding an optimal design in a space with multiple minima and roughness ubiquitous to numerically generated nonlinear objective functions. SA was modified to reduce the number of objective function evaluations for an optimal design, historically the main criticism of stochastic methods. SA was applied to many CFD/MDO problems including: low sonic-boom bodies, minimum drag on supersonic fore-bodies, minimum drag on supersonic aeroelastic fore-bodies, minimum drag on HSCT aeroelastic wings, FLOPS preliminary design code, another preliminary aircraft design study with vortex lattice aerodynamics, HSR complete aircraft aerodynamics. In every case, SA provided a simple, robust and reliable optimization method which found optimal designs in order 100 objective function evaluations. Perhaps most importantly, from this academic/industrial project, technology has been successfully transferred; this method is the method of choice for optimization problems at Northrop Grumman.
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Determining the optimal number of Kanban in multi-products supply chain system
NASA Astrophysics Data System (ADS)
Widyadana, G. A.; Wee, H. M.; Chang, Jer-Yuan
2010-02-01
Kanban, a key element of just-in-time system, is a re-order card or signboard giving instruction or triggering the pull system to manufacture or supply a component based on actual usage of material. There are two types of Kanban: production Kanban and withdrawal Kanban. This study uses optimal and meta-heuristic methods to determine the Kanban quantity and withdrawal lot sizes in a supply chain system. Although the mix integer programming method gives an optimal solution, it is not time efficient. For this reason, the meta-heuristic methods are suggested. In this study, a genetic algorithm (GA) and a hybrid of genetic algorithm and simulated annealing (GASA) are used. The study compares the performance of GA and GASA with that of the optimal method using MIP. The given problems show that both GA and GASA result in a near optimal solution, and they outdo the optimal method in term of run time. In addition, the GASA heuristic method gives a better performance than the GA heuristic method.
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A case study in programming a quantum annealer for hard operational planning problems
NASA Astrophysics Data System (ADS)
Rieffel, Eleanor G.; Venturelli, Davide; O'Gorman, Bryan; Do, Minh B.; Prystay, Elicia M.; Smelyanskiy, Vadim N.
2015-01-01
We report on a case study in programming an early quantum annealer to attack optimization problems related to operational planning. While a number of studies have looked at the performance of quantum annealers on problems native to their architecture, and others have examined performance of select problems stemming from an application area, ours is one of the first studies of a quantum annealer's performance on parametrized families of hard problems from a practical domain. We explore two different general mappings of planning problems to quadratic unconstrained binary optimization (QUBO) problems, and apply them to two parametrized families of planning problems, navigation-type and scheduling-type. We also examine two more compact, but problem-type specific, mappings to QUBO, one for the navigation-type planning problems and one for the scheduling-type planning problems. We study embedding properties and parameter setting and examine their effect on the efficiency with which the quantum annealer solves these problems. From these results, we derive insights useful for the programming and design of future quantum annealers: problem choice, the mapping used, the properties of the embedding, and the annealing profile all matter, each significantly affecting the performance.
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Design and Optimization of Low-thrust Orbit Transfers Using Q-law and Evolutionary Algorithms
NASA Technical Reports Server (NTRS)
Lee, Seungwon; vonAllmen, Paul; Fink, Wolfgang; Petropoulos, Anastassios; Terrile, Richard
2005-01-01
Future space missions will depend more on low-thrust propulsion (such as ion engines) thanks to its high specific impulse. Yet, the design of low-thrust trajectories is complex and challenging. Third-body perturbations often dominate the thrust, and a significant change to the orbit requires a long duration of thrust. In order to guide the early design phases, we have developed an efficient and efficacious method to obtain approximate propellant and flight-time requirements (i.e., the Pareto front) for orbit transfers. A search for the Pareto-optimal trajectories is done in two levels: optimal thrust angles and locations are determined by Q-law, while the Q-law is optimized with two evolutionary algorithms: a genetic algorithm and a simulated-annealing-related algorithm. The examples considered are several types of orbit transfers around the Earth and the asteroid Vesta.
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NASA Astrophysics Data System (ADS)
Alegria Mira, Lara; Thrall, Ashley P.; De Temmerman, Niels
2016-02-01
Deployable scissor structures are well equipped for temporary and mobile applications since they are able to change their form and functionality. They are structural mechanisms that transform from a compact state to an expanded, fully deployed configuration. A barrier to the current design and reuse of scissor structures, however, is that they are traditionally designed for a single purpose. Alternatively, a universal scissor component (USC)-a generalized element which can achieve all traditional scissor types-introduces an opportunity for reuse in which the same component can be utilized for different configurations and spans. In this article, the USC is optimized for structural performance. First, an optimized length for the USC is determined based on a trade-off between component weight and structural performance (measured by deflections). Then, topology optimization, using the simulated annealing algorithm, is implemented to determine a minimum weight layout of beams within a single USC component.
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Genetic Adaptive Control for PZT Actuators
NASA Technical Reports Server (NTRS)
Kim, Jeongwook; Stover, Shelley K.; Madisetti, Vijay K.
1995-01-01
A piezoelectric transducer (PZT) is capable of providing linear motion if controlled correctly and could provide a replacement for traditional heavy and large servo systems using motors. This paper focuses on a genetic model reference adaptive control technique (GMRAC) for a PZT which is moving a mirror where the goal is to keep the mirror velocity constant. Genetic Algorithms (GAs) are an integral part of the GMRAC technique acting as the search engine for an optimal PID controller. Two methods are suggested to control the actuator in this research. The first one is to change the PID parameters and the other is to add an additional reference input in the system. The simulation results of these two methods are compared. Simulated Annealing (SA) is also used to solve the problem. Simulation results of GAs and SA are compared after simulation. GAs show the best result according to the simulation results. The entire model is designed using the Mathworks' Simulink tool.
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NASA Astrophysics Data System (ADS)
Biswas, A.; Sharma, S. P.
2012-12-01
Self-Potential anomaly is an important geophysical technique that measures the electrical potential due natural source of current in the Earth's subsurface. An inclined sheet type model is a very familiar structure associated with mineralization, fault plane, groundwater flow and many other geological features which exhibits self potential anomaly. A number of linearized and global inversion approaches have been developed for the interpretation of SP anomaly over different structures for various purposes. Mathematical expression to compute the forward response over a two-dimensional dipping sheet type structures can be described in three different ways using five variables in each case. Complexities in the inversion using three different forward approaches are different. Interpretation of self-potential anomaly using very fast simulated annealing global optimization has been developed in the present study which yielded a new insight about the uncertainty and equivalence in model parameters. Interpretation of the measured data yields the location of the causative body, depth to the top, extension, dip and quality of the causative body. In the present study, a comparative performance of three different forward approaches in the interpretation of self-potential anomaly is performed to assess the efficacy of the each approach in resolving the possible ambiguity. Even though each forward formulation yields the same forward response but optimization of different sets of variable using different forward problems poses different kinds of ambiguity in the interpretation. Performance of the three approaches in optimization has been compared and it is observed that out of three methods, one approach is best and suitable for this kind of study. Our VFSA approach has been tested on synthetic, noisy and field data for three different methods to show the efficacy and suitability of the best method. It is important to use the forward problem in the optimization that yields the best result without any ambiguity and smaller uncertainty. Keywords: SP anomaly, inclined sheet, 2D structure, forward problems, VFSA Optimization,
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NASA Astrophysics Data System (ADS)
Deepu, M. J.; Farivar, H.; Prahl, U.; Phanikumar, G.
2017-04-01
Dual phase steels are versatile advanced high strength steels that are being used for sheet metal applications in automotive industry. It also has the potential for application in bulk components like gear. The inter-critical annealing in dual phase steels is one of the crucial steps that determine the mechanical properties of the material. Selection of the process parameters for inter-critical annealing, in particular, the inter-critical annealing temperature and time is important as it plays a major role in determining the volume fractions of ferrite and martensite, which in turn determines the mechanical properties. Selection of these process parameters to obtain a particular required mechanical property requires large number of experimental trials. Simulation of microstructure evolution and virtual compression/tensile testing can help in reducing the number of such experimental trials. In the present work, phase field modeling implemented in the commercial software Micress® is used to predict the microstructure evolution during inter-critical annealing. Virtual compression tests are performed on the simulated microstructure using finite element method implemented in the commercial software, to obtain the effective flow curve of the macroscopic material. The flow curves obtained by simulation are experimentally validated with physical simulation in Gleeble® and compared with that obtained using linear rule of mixture. The methodology could be used in determining the inter-critical annealing process parameters required for achieving a particular flow curve.
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NASA Astrophysics Data System (ADS)
Ghaderi, F.; Pahlavani, P.
2015-12-01
A multimodal multi-criteria route planning (MMRP) system provides an optimal multimodal route from an origin point to a destination point considering two or more criteria in a way this route can be a combination of public and private transportation modes. In this paper, the simulate annealing (SA) and the fuzzy analytical hierarchy process (fuzzy AHP) were combined in order to find this route. In this regard, firstly, the effective criteria that are significant for users in their trip were determined. Then the weight of each criterion was calculated using the fuzzy AHP weighting method. The most important characteristic of this weighting method is the use of fuzzy numbers that aids the users to consider their uncertainty in pairwise comparison of criteria. After determining the criteria weights, the proposed SA algorithm were used for determining an optimal route from an origin to a destination. One of the most important problems in a meta-heuristic algorithm is trapping in local minima. In this study, five transportation modes, including subway, bus rapid transit (BRT), taxi, walking, and bus were considered for moving between nodes. Also, the fare, the time, the user's bother, and the length of the path were considered as effective criteria for solving the problem. The proposed model was implemented in an area in centre of Tehran in a GUI MATLAB programming language. The results showed a high efficiency and speed of the proposed algorithm that support our analyses.
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Attenuation of harmonic noise in vibroseis data using Simulated Annealing
NASA Astrophysics Data System (ADS)
Sharma, S. P.; Tildy, Peter; Iranpour, Kambiz; Scholtz, Peter
2009-04-01
Processing of high productivity vibroseis seismic data (such as slip-sweep acquisition records) suffers from the well known disadvantage of harmonic distortion. Harmonic distortions are observed after cross-correlation of the recorded seismic signal with the pilot sweep and affect the signals in negative time (before the actual strong reflection event). Weak reflection events of the earlier sweeps falling in the negative time window of the cross-correlation sequence are being masked by harmonic distortions. Though the amplitude of the harmonic distortion is small (up to 10-20 %) compared to the fundamental amplitude of the reflection events, but it is significant enough to mask weak reflected signals. Elimination of harmonic noise due to source signal distortion from the cross-correlated seismic trace is a challenging task since the application of vibratory sources started and it still needs improvement. An approach has been worked out that minimizes the level of harmonic distortion by designing the signal similar to the harmonic distortion. An arbitrary length filter is optimized using the Simulated Annealing global optimization approach to design a harmonic signal. The approach deals with the convolution of a ratio trace (ratio of the harmonics with respect to the fundamental sweep) with the correlated "positive time" recorded signal and an arbitrary filter. Synthetic data study has revealed that this procedure of designing a signal similar to the desired harmonics using convolution of a suitable filter with theoretical ratio of harmonics with fundamental sweep helps in reducing the problem of harmonic distortion. Once we generate a similar signal for a vibroseis source using an optimized filter, then, this filter could be used to generate harmonics, which can be subtracted from the main cross-correlated trace to get the better, undistorted image of the subsurface. Designing the predicted harmonics to reduce the energy in the trace by considering weak reflection and observed harmonics together yields the desired result (resolution of weak reflected signal from the harmonic distortion). As optimization steps proceeds forward it is possible to observe from the difference plots of desired and predicted harmonics how weak reflections evolved from the harmonic distortion gradually during later iterations of global optimization. The procedure is applied in resolving weak reflections from a number of traces considered together. For a more precise design of harmonics SA procedure needs longer computation time which is impractical to deal with voluminous seismic data. However, the objective of resolving weak reflection signal in the strong harmonic noise can be achieved with fast computation using faster cooling schedule and less number of iterations and number of moves in simulated annealing procedure. This process could help in reducing the harmonics distortion and achieving the objective of resolving the lost weak reflection events in the cross-correlated seismic traces. Acknowledgements: The research was supported under the European Marie Curie Host Fellowships for Transfer of Knowledge (TOK) Development Host Scheme (contract no. MTKD-CT-2006-042537).
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Enhanced magnetic refrigeration properties in Mn-rich Ni-Mn-Sn ribbons by optimal annealing
Zhang, Yu; Zhang, Linlin; Zheng, Qiang; Zheng, Xinqi; Li, Ming; Du, Juan; Yan, Aru
2015-01-01
The influence of annealing time on temperature range of martensitic phase transition (ΔTA-M), thermal hysteresis (ΔThys), magnetic hysteresis loss (ΔMhys), magnetic entropy change (ΔSM) and relative refrigeration capacity (RC) of the Mn-rich Ni43Mn46Sn11 melt spun ribbons have been systematically studied. By optimal annealing, an extremely large ΔSM of 43.2 J.kg−1K−1 and a maximum RC of 221.0 J.kg−1 could be obtained respectively in a field change of 5 T. Both ΔTA-M and ΔThys decreases after annealing, while ΔMhys and ΔSM first dramatically increase to a maximum then degenerates as increase of annealing time. A large effective cooling capacity (RCeff) of 115.4 J.kg−1 was achieved in 60 min annealed ribbons, which increased 75% compared with that unannealed ribbons. The evolution of magnetic properties and magnetocaloric effect has been discussed and proved by atomic ordering degree, microstructure and composition analysis. PMID:26055884
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Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.
2018-01-01
Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems. PMID:29652405
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Effect of Annealing Treatment on Mechanical Properties of Nanocrystalline α-iron: an Atomistic Study
Tong, Xuhang; Zhang, Hao; Li, D. Y.
2015-01-01
Claims are often found in the literature that metallic materials can be nanocrystallized by severe plastic deformation (SPD). However, SPD does not generate a well-defined nanocrystalline (NC) material, which can be achieved by subsequent annealing/recovery treatment. In this study, molecular dynamics (MD) simulation is employed to study the effect of annealing on structure and mechanical properties of cyclic deformed NC α-iron, which simulates SPD-processed α-iron. It is demonstrated that grain boundaries in the deformed NC α-iron evolve to a more equilibrium state during annealing, eliminating or minimizing the residual stress. The annealing treatment increases the system's strength by reducing dislocation emission sources, and improves material ductility through strengthening grain boundaries' resistance to intergranular cracks. The results indicate that the annealing treatment is an essential process for obtaining a well-defined NC structure with superior mechanical properties. PMID:25675978
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A Global Optimization Method to Calculate Water Retention Curves
NASA Astrophysics Data System (ADS)
Maggi, S.; Caputo, M. C.; Turturro, A. C.
2013-12-01
Water retention curves (WRC) have a key role for the hydraulic characterization of soils and rocks. The behaviour of the medium is defined by relating the unsaturated water content to the matric potential. The experimental determination of WRCs requires an accurate and detailed measurement of the dependence of matric potential on water content, a time-consuming and error-prone process, in particular for rocky media. A complete experimental WRC needs at least a few tens of data points, distributed more or less uniformly from full saturation to oven dryness. Since each measurement requires to wait to reach steady state conditions (i.e., between a few tens of minutes for soils and up to several hours or days for rocks or clays), the whole process can even take a few months. The experimental data are fitted to the most appropriate parametric model, such as the widely used models of Van Genuchten, Brooks and Corey and Rossi-Nimmo, to obtain the analytic WRC. We present here a new method for the determination of the parameters that best fit the models to the available experimental data. The method is based on differential evolution, an evolutionary computation algorithm particularly useful for multidimensional real-valued global optimization problems. With this method it is possible to strongly reduce the number of measurements necessary to optimize the model parameters that accurately describe the WRC of the samples, allowing to decrease the time needed to adequately characterize the medium. In the present work, we have applied our method to calculate the WRCs of sedimentary carbonatic rocks of marine origin, belonging to 'Calcarenite di Gravina' Formation (Middle Pliocene - Early Pleistocene) and coming from two different quarry districts in Southern Italy. WRC curves calculated using the Van Genuchten model by simulated annealing (dashed curve) and differential evolution (solid curve). The curves are calculated using 10 experimental data points randomly extracted from the full experimental dataset. Simulated annealing is not able to find the optimal solution with this reduced data set.
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Quantum Spin Glasses, Annealing and Computation
NASA Astrophysics Data System (ADS)
Chakrabarti, Bikas K.; Inoue, Jun-ichi; Tamura, Ryo; Tanaka, Shu
2017-05-01
List of tables; List of figures, Preface; 1. Introduction; Part I. Quantum Spin Glass, Annealing and Computation: 2. Classical spin models from ferromagnetic spin systems to spin glasses; 3. Simulated annealing; 4. Quantum spin glass; 5. Quantum dynamics; 6. Quantum annealing; Part II. Additional Notes: 7. Notes on adiabatic quantum computers; 8. Quantum information and quenching dynamics; 9. A brief historical note on the studies of quantum glass, annealing and computation.
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Assessing and optimising flood control options along the Arachthos river floodplain (Epirus, Greece)
NASA Astrophysics Data System (ADS)
Drosou, Athina; Dimitriadis, Panayiotis; Lykou, Archontia; Kossieris, Panagiotis; Tsoukalas, Ioannis; Efstratiadis, Andreas; Mamassis, Nikos
2015-04-01
We present a multi-criteria simulation-optimization framework for the optimal design and setting of flood protection structures along river banks. The methodology is tested in the lower course of the Arachthos River (Epirus, Greece), downstream of the hydroelectric dam of Pournari. The entire study area is very sensitive, particularly because the river crosses the urban area of Arta, which is located just after the dam. Moreover, extended agricultural areas that are crucial for the local economy are prone to floods. In the proposed methodology we investigate two conflicting criteria, i.e. the minimization of flood hazards (due to damages to urban infrastructures, crops, etc.) and the minimization of construction costs of the essential hydraulic structures (e.g. dikes). For the hydraulic simulation we examine two flood routing models, named 1D HEC-RAS and quasi-2D LISFLOOD, whereas the optimization is carried out through the Surrogate-Enhanced Evolutionary Annealing-Simplex (SE-EAS) algorithm that couples the strengths of surrogate modeling with the effectiveness and efficiency of the EAS method.
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Ant system: optimization by a colony of cooperating agents.
Dorigo, M; Maniezzo, V; Colorni, A
1996-01-01
An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
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Cyclical Annealing Technique To Enhance Reliability of Amorphous Metal Oxide Thin Film Transistors.
Chen, Hong-Chih; Chang, Ting-Chang; Lai, Wei-Chih; Chen, Guan-Fu; Chen, Bo-Wei; Hung, Yu-Ju; Chang, Kuo-Jui; Cheng, Kai-Chung; Huang, Chen-Shuo; Chen, Kuo-Kuang; Lu, Hsueh-Hsing; Lin, Yu-Hsin
2018-02-26
This study introduces a cyclical annealing technique that enhances the reliability of amorphous indium-gallium-zinc-oxide (a-IGZO) via-type structure thin film transistors (TFTs). By utilizing this treatment, negative gate-bias illumination stress (NBIS)-induced instabilities can be effectively alleviated. The cyclical annealing provides several cooling steps, which are exothermic processes that can form stronger ionic bonds. An additional advantage is that the total annealing time is much shorter than when using conventional long-term annealing. With the use of cyclical annealing, the reliability of the a-IGZO can be effectively optimized, and the shorter process time can increase fabrication efficiency.
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Analysis and optimization of the active rigidity joint
NASA Astrophysics Data System (ADS)
Manzo, Justin; Garcia, Ephrahim
2009-12-01
The active rigidity joint is a composite mechanism using shape memory alloy and shape memory polymer to create a passively rigid joint with thermally activated deflection. A new model for the active rigidity joint relaxes constraints of earlier methods and allows for more accurate deflection predictions compared to finite element results. Using an iterative process to determine the strain distribution and deflection, the method demonstrates accurate results for both surface bonded and embedded actuators with and without external loading. Deflection capabilities are explored through simulated annealing heuristic optimization using a variety of cost functions to explore actuator performance. A family of responses presents actuator characteristics in terms of load bearing and deflection capabilities given material and thermal constraints. Optimization greatly expands the available workspace of the active rigidity joint from the initial configuration, demonstrating specific work capabilities comparable to those of muscle tissue.
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Direct Immersion Annealing of Thin Block Copolymer Films.
Modi, Arvind; Bhaway, Sarang M; Vogt, Bryan D; Douglas, Jack F; Al-Enizi, Abdullah; Elzatahry, Ahmed; Sharma, Ashutosh; Karim, Alamgir
2015-10-07
We demonstrate ordering of thin block copolymer (BCP) films via direct immersion annealing (DIA) at enhanced rate leading to stable morphologies. The BCP films are immersed in carefully selected mixtures of good and marginal solvents that can impart enhanced polymer mobility, while inhibiting film dissolution. DIA is compatible with roll-to-roll assembly manufacturing and has distinct advantages over conventional thermal annealing and batch processing solvent-vapor annealing methods. We identify three solvent composition-dependent BCP film ordering regimes in DIA for the weakly interacting polystyrene-poly(methyl methacrylate) (PS-PMMA) system: rapid short-range order, optimal long-range order, and a film instability regime. Kinetic studies in the "optimal long-range order" processing regime as a function of temperature indicate a significant reduction of activation energy for BCP grain growth compared to oven annealing at conventional temperatures. An attractive feature of DIA is its robustness to ordering other BCP (e.g. PS-P2VP) and PS-PMMA systems exhibiting spherical, lamellar and cylindrical ordering.
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Direct Immersion Annealing of Thin Block Copolymer Films
DOE Office of Scientific and Technical Information (OSTI.GOV)
Modi, Arvind; Bhaway, Sarang M.; Vogt, Bryan D.
2015-09-09
We demonstrate ordering of thin block copolymer (BCP) films via direct immersion annealing (DIA) at enhanced rate leading to stable morphologies. The BCP films are immersed in carefully selected mixtures of good and marginal solvents that can impart enhanced polymer mobility, while inhibiting film dissolution. DIA is compatible with roll-to-roll assembly manufacturing and has distinct advantages over conventional thermal annealing and batch processing solvent-vapor annealing methods. We identify three solvent composition-dependent BCP film ordering regimes in DIA for the weakly interacting polystyrene–poly(methyl methacrylate) (PS–PMMA) system: rapid short-range order, optimal long-range order, and a film instability regime. Kinetic studies in themore » “optimal long-range order” processing regime as a function of temperature indicate a significant reduction of activation energy for BCP grain growth compared to oven annealing at conventional temperatures. An attractive feature of DIA is its robustness to ordering other BCP (e.g. PS-P2VP) and PS-PMMA systems exhibiting spherical, lamellar and cylindrical ordering.« less
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Microstructure and Property Modifications of Cold Rolled IF Steel by Local Laser Annealing
NASA Astrophysics Data System (ADS)
Hallberg, Håkan; Adamski, Frédéric; Baïz, Sarah; Castelnau, Olivier
2017-10-01
Laser annealing experiments are performed on cold rolled IF steel whereby highly localized microstructure and property modification are achieved. The microstructure is seen to develop by strongly heterogeneous recrystallization to provide steep gradients, across the submillimeter scale, of grain size and crystallographic texture. Hardness mapping by microindentation is used to reveal the corresponding gradients in macroscopic properties. A 2D level set model of the microstructure development is established as a tool to further optimize the method and to investigate, for example, the development of grain size variations due to the strong and transient thermal gradient. Particular focus is given to the evolution of the beneficial γ-fiber texture during laser annealing. The simulations indicate that the influence of selective growth based on anisotropic grain boundary properties only has a minor effect on texture evolution compared to heterogeneous stored energy, temperature variations, and nucleation conditions. It is also shown that although the α-fiber has an initial frequency advantage, the higher probability of γ-nucleation, in combination with a higher stored energy driving force in this fiber, promotes a stronger presence of the γ-fiber as also observed in experiments.
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Lee, Juyong; Lee, Jinhyuk; Sasaki, Takeshi N; Sasai, Masaki; Seok, Chaok; Lee, Jooyoung
2011-08-01
Ab initio protein structure prediction is a challenging problem that requires both an accurate energetic representation of a protein structure and an efficient conformational sampling method for successful protein modeling. In this article, we present an ab initio structure prediction method which combines a recently suggested novel way of fragment assembly, dynamic fragment assembly (DFA) and conformational space annealing (CSA) algorithm. In DFA, model structures are scored by continuous functions constructed based on short- and long-range structural restraint information from a fragment library. Here, DFA is represented by the full-atom model by CHARMM with the addition of the empirical potential of DFIRE. The relative contributions between various energy terms are optimized using linear programming. The conformational sampling was carried out with CSA algorithm, which can find low energy conformations more efficiently than simulated annealing used in the existing DFA study. The newly introduced DFA energy function and CSA sampling algorithm are implemented into CHARMM. Test results on 30 small single-domain proteins and 13 template-free modeling targets of the 8th Critical Assessment of protein Structure Prediction show that the current method provides comparable and complementary prediction results to existing top methods. Copyright © 2011 Wiley-Liss, Inc.
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Efficient generation of low-energy folded states of a model protein
NASA Astrophysics Data System (ADS)
Gordon, Heather L.; Kwan, Wai Kei; Gong, Chunhang; Larrass, Stefan; Rothstein, Stuart M.
2003-01-01
A number of short simulated annealing runs are performed on a highly-frustrated 46-"residue" off-lattice model protein. We perform, in an iterative fashion, a principal component analysis of the 946 nonbonded interbead distances, followed by two varieties of cluster analyses: hierarchical and k-means clustering. We identify several distinct sets of conformations with reasonably consistent cluster membership. Nonbonded distance constraints are derived for each cluster and are employed within a distance geometry approach to generate many new conformations, previously unidentified by the simulated annealing experiments. Subsequent analyses suggest that these new conformations are members of the parent clusters from which they were generated. Furthermore, several novel, previously unobserved structures with low energy were uncovered, augmenting the ensemble of simulated annealing results, and providing a complete distribution of low-energy states. The computational cost of this approach to generating low-energy conformations is small when compared to the expense of further Monte Carlo simulated annealing runs.
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A parallel simulated annealing algorithm for standard cell placement on a hypercube computer
NASA Technical Reports Server (NTRS)
Jones, Mark Howard
1987-01-01
A parallel version of a simulated annealing algorithm is presented which is targeted to run on a hypercube computer. A strategy for mapping the cells in a two dimensional area of a chip onto processors in an n-dimensional hypercube is proposed such that both small and large distance moves can be applied. Two types of moves are allowed: cell exchanges and cell displacements. The computation of the cost function in parallel among all the processors in the hypercube is described along with a distributed data structure that needs to be stored in the hypercube to support parallel cost evaluation. A novel tree broadcasting strategy is used extensively in the algorithm for updating cell locations in the parallel environment. Studies on the performance of the algorithm on example industrial circuits show that it is faster and gives better final placement results than the uniprocessor simulated annealing algorithms. An improved uniprocessor algorithm is proposed which is based on the improved results obtained from parallelization of the simulated annealing algorithm.
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Wang, Fengyou; Zhang, Xiaodan; Wang, Liguo; Jiang, Yuanjian; Wei, Changchun; Xu, Shengzhi; Zhao, Ying
2014-10-07
In this study, hydrogenated amorphous silicon (a-Si:H) thin films are deposited using a radio-frequency plasma-enhanced chemical vapor deposition (RF-PECVD) system. The Si-H configuration of the a-Si:H/c-Si interface is regulated by optimizing the deposition temperature and post-annealing duration to improve the minority carrier lifetime (τeff) of a commercial Czochralski (Cz) silicon wafer. The mechanism of this improvement involves saturation of the microstructural defects with hydrogen evolved within the a-Si:H films due to the transformation from SiH2 into SiH during the annealing process. The post-annealing temperature is controlled to ∼180 °C so that silicon heterojunction solar cells (SHJ) could be prepared without an additional annealing step. To achieve better performance of the SHJ solar cells, we also optimize the thickness of the a-Si:H passivation layer. Finally, complete SHJ solar cells are fabricated using different temperatures for the a-Si:H film deposition to study the influence of the deposition temperature on the solar cell parameters. For the optimized a-Si:H deposition conditions, an efficiency of 18.41% is achieved on a textured Cz silicon wafer.
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Improvement of band gap profile in Cu(InGa)Se{sub 2} solar cells through rapid thermal annealing
DOE Office of Scientific and Technical Information (OSTI.GOV)
Chen, D.S.; College of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 200090; Yang, J.
Highlights: • Proper RTA treatment can effectively optimize band gap profile to more expected level. • Inter-diffusion of atoms account for the improvement of the graded band gap profile. • The variation of the band gap profile created an absolute gain in the efficiency by 1.22%. - Abstract: In the paper, the effect of rapid thermal annealing on non-optimal double-graded band gap profiles was investigated by using X-ray photoelectron spectroscopy and capacitance–voltage measurement techniques. Experimental results revealed that proper rapid thermal annealing treatment can effectively improve band gap profile to more optimal level. The annealing treatment could not only reducemore » the values of front band gap and minimum band gap, but also shift the position of the minimum band gap toward front electrode and enter into space charge region. In addition, the thickness of Cu(InGa)Se{sub 2} thin film decreased by 25 nm after rapid thermal annealing treatment. All of these modifications were attributed to the inter-diffusion of atoms during thermal treatment process. Simultaneously, the variation of the band gap profile created an absolute gain in the efficiency by 1.22%, short-circuit current density by 2.16 mA/cm{sup 2} and filled factor by 3.57%.« less
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An improved grey wolf optimizer algorithm for the inversion of geoelectrical data
NASA Astrophysics Data System (ADS)
Li, Si-Yu; Wang, Shu-Ming; Wang, Peng-Fei; Su, Xiao-Lu; Zhang, Xin-Song; Dong, Zhi-Hui
2018-05-01
The grey wolf optimizer (GWO) is a novel bionics algorithm inspired by the social rank and prey-seeking behaviors of grey wolves. The GWO algorithm is easy to implement because of its basic concept, simple formula, and small number of parameters. This paper develops a GWO algorithm with a nonlinear convergence factor and an adaptive location updating strategy and applies this improved grey wolf optimizer (improved grey wolf optimizer, IGWO) algorithm to geophysical inversion problems using magnetotelluric (MT), DC resistivity and induced polarization (IP) methods. Numerical tests in MATLAB 2010b for the forward modeling data and the observed data show that the IGWO algorithm can find the global minimum and rarely sinks to the local minima. For further study, inverted results using the IGWO are contrasted with particle swarm optimization (PSO) and the simulated annealing (SA) algorithm. The outcomes of the comparison reveal that the IGWO and PSO similarly perform better in counterpoising exploration and exploitation with a given number of iterations than the SA.
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NASA Astrophysics Data System (ADS)
Hasegawa, Manabu; Hiramatsu, Kotaro
2013-10-01
The effectiveness of the Metropolis algorithm (MA) (constant-temperature simulated annealing) in optimization by the method of search-space smoothing (SSS) (potential smoothing) is studied on two types of random traveling salesman problems. The optimization mechanism of this hybrid approach (MASSS) is investigated by analyzing the exploration dynamics observed in the rugged landscape of the cost function (energy surface). The results show that the MA can be successfully utilized as a local search algorithm in the SSS approach. It is also clarified that the optimization characteristics of these two constituent methods are improved in a mutually beneficial manner in the MASSS run. Specifically, the relaxation dynamics generated by employing the MA work effectively even in a smoothed landscape and more advantage is taken of the guiding function proposed in the idea of SSS; this mechanism operates in an adaptive manner in the de-smoothing process and therefore the MASSS method maintains its optimization function over a wider temperature range than the MA.
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NASA Astrophysics Data System (ADS)
Khadzhai, G. Ya.; Vovk, R. V.; Vovk, N. R.; Kamchatnaya, S. N.; Dobrovolskiy, O. V.
2018-02-01
We reveal that the temperature dependence of the basal-plane normal-state electrical resistance of optimally doped YBa2Cu3O7-δ single crystals can be with great accuracy approximated within the framework of the model of s-d electron-phonon scattering. This requires taking into account the fluctuation conductivity whose contribution exponentially increases with decreasing temperature and decreases with an increase of oxygen deficiency. Room-temperature annealing improves the sample and, thus, increases the superconducting transition temperature. The temperature of the 2D-3D crossover decreases during annealing.
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Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation
DOE Office of Scientific and Technical Information (OSTI.GOV)
Robertson, J.; Polly, B.; Collis, J.
2013-09-01
This simulation study adapts and applies the general framework described in BESTEST-EX (Judkoff et al 2010) for self-testing residential building energy model calibration methods. BEopt/DOE-2.2 is used to evaluate four mathematical calibration methods in the context of monthly, daily, and hourly synthetic utility data for a 1960's-era existing home in a cooling-dominated climate. The home's model inputs are assigned probability distributions representing uncertainty ranges, random selections are made from the uncertainty ranges to define 'explicit' input values, and synthetic utility billing data are generated using the explicit input values. The four calibration methods evaluated in this study are: an ASHRAEmore » 1051-RP-based approach (Reddy and Maor 2006), a simplified simulated annealing optimization approach, a regression metamodeling optimization approach, and a simple output ratio calibration approach. The calibration methods are evaluated for monthly, daily, and hourly cases; various retrofit measures are applied to the calibrated models and the methods are evaluated based on the accuracy of predicted savings, computational cost, repeatability, automation, and ease of implementation.« less
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Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation
DOE Office of Scientific and Technical Information (OSTI.GOV)
and Ben Polly, Joseph Robertson; Polly, Ben; Collis, Jon
2013-09-01
This simulation study adapts and applies the general framework described in BESTEST-EX (Judkoff et al 2010) for self-testing residential building energy model calibration methods. BEopt/DOE-2.2 is used to evaluate four mathematical calibration methods in the context of monthly, daily, and hourly synthetic utility data for a 1960's-era existing home in a cooling-dominated climate. The home's model inputs are assigned probability distributions representing uncertainty ranges, random selections are made from the uncertainty ranges to define "explicit" input values, and synthetic utility billing data are generated using the explicit input values. The four calibration methods evaluated in this study are: an ASHRAEmore » 1051-RP-based approach (Reddy and Maor 2006), a simplified simulated annealing optimization approach, a regression metamodeling optimization approach, and a simple output ratio calibration approach. The calibration methods are evaluated for monthly, daily, and hourly cases; various retrofit measures are applied to the calibrated models and the methods are evaluated based on the accuracy of predicted savings, computational cost, repeatability, automation, and ease of implementation.« less
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Adewumi, Aderemi Oluyinka; Chetty, Sivashan
2017-01-01
The Annual Crop Planning (ACP) problem was a recently introduced problem in the literature. This study further expounds on this problem by presenting a new mathematical formulation, which is based on market economic factors. To determine solutions, a new local search metaheuristic algorithm is investigated which is called the enhanced Best Performance Algorithm (eBPA). eBPA's results are compared against two well-known local search metaheuristic algorithms; these include Tabu Search and Simulated Annealing. The results show the potential of the eBPA for continuous optimization problems.
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1990-01-19
following theorem from the Perron - Frobenius theory of nonnegative matrices. Theorem 2.2 : [1] Consider an irreducible Markov chain with transition...us suppose to the contrary that both expressions are nonnegative . Then max ,01v,,= max /3,1v,,> max 3OV,,= max /3,0V max i,01v,,, -A.,. A’ ,, A.i. A...induction. For k 1, from (20) we see that (22) /3, 8 ,, _-30,A V(). Clearly, the left-hand side of (22) is nonnegative , implying that the right-hand
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NASA Astrophysics Data System (ADS)
Greenlee, Jordan D.; Feigelson, Boris N.; Anderson, Travis J.; Tadjer, Marko J.; Hite, Jennifer K.; Mastro, Michael A.; Eddy, Charles R.; Hobart, Karl D.; Kub, Francis J.
2014-08-01
The first step of a multi-cycle rapid thermal annealing process was systematically studied. The surface, structure, and optical properties of Mg implanted GaN thin films annealed at temperatures ranging from 900 to 1200 °C were investigated by Raman spectroscopy, photoluminescence, UV-visible spectroscopy, atomic force microscopy, and Nomarski microscopy. The GaN thin films are capped with two layers of in-situ metal organic chemical vapor deposition -grown AlN and annealed in 24 bar of N2 overpressure to avoid GaN decomposition. The crystal quality of the GaN improves with increasing annealing temperature as confirmed by UV-visible spectroscopy and the full widths at half maximums of the E2 and A1 (LO) Raman modes. The crystal quality of films annealed above 1100 °C exceeds the quality of the as-grown films. At 1200 °C, Mg is optically activated, which is determined by photoluminescence measurements. However, at 1200 °C, the GaN begins to decompose as evidenced by pit formation on the surface of the samples. Therefore, it was determined that the optimal temperature for the first step in a multi-cycle rapid thermal anneal process should be conducted at 1150 °C due to crystal quality and surface morphology considerations.
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NASA Astrophysics Data System (ADS)
Zhao, Ruitu; Wang, Mu; Chen, Baojie; Liu, Ke; Pun, Edwin Yue-Bun; Lin, Hai
2011-04-01
Bent waveguide structures (S-, U-, and F-bend) based on buried Er3+/Yb3+ codoped phosphate glass waveguide channel fabricated by field-assisted annealing have been designed to achieve high-gain C-band integrated amplification. Using a simulated-bend method, the optimal radius for the curved structure is derived to be 0.90 cm with loss coefficient of 0.02 dB/cm, as the substrate size is schemed to be 4×3 cm2. In the wavelength range of 1520 to 1575 nm, obvious gain enhancement for the bent structure waveguides is anticipated, and for the F-bend waveguide, the internal gain at 1534-nm wavelength is derived to be 41.61 dB, which is much higher than the value of 26.22 and 13.81 dB in the U- and S-bend waveguides, respectively, and over three times higher than that of the straight one. The simulation results indicate that the bent structure design is beneficial in obtaining high signal gain in buried Er3+/Yb3+ codoped phosphate glass waveguides, which lays the foundation for further design and fabrication of integrated devices.
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Instantons in Quantum Annealing: Thermally Assisted Tunneling Vs Quantum Monte Carlo Simulations
NASA Technical Reports Server (NTRS)
Jiang, Zhang; Smelyanskiy, Vadim N.; Boixo, Sergio; Isakov, Sergei V.; Neven, Hartmut; Mazzola, Guglielmo; Troyer, Matthias
2015-01-01
Recent numerical result (arXiv:1512.02206) from Google suggested that the D-Wave quantum annealer may have an asymptotic speed-up than simulated annealing, however, the asymptotic advantage disappears when it is compared to quantum Monte Carlo (a classical algorithm despite its name). We show analytically that the asymptotic scaling of quantum tunneling is exactly the same as the escape rate in quantum Monte Carlo for a class of problems. Thus, the Google result might be explained in our framework. We also found that the transition state in quantum Monte Carlo corresponds to the instanton solution in quantum tunneling problems, which is observed in numerical simulations.
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Spin systems and Political Districting Problem
NASA Astrophysics Data System (ADS)
Chou, Chung-I.; Li, Sai-Ping
2007-03-01
The aim of the Political Districting Problem is to partition a territory into electoral districts subject to some constraints such as contiguity, population equality, etc. In this paper, we apply statistical physics methods to Political Districting Problem. We will show how to transform the political problem to a spin system, and how to write down a q-state Potts model-like energy function in which the political constraints can be written as interactions between sites or external fields acting on the system. Districting into q voter districts is equivalent to finding the ground state of this q-state Potts model. Searching for the ground state becomes an optimization problem, where optimization algorithms such as the simulated annealing method and Genetic Algorithm can be employed here.
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Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance.
Alia, Shaun M; Pivovar, Bryan S
2018-04-27
Platinum-nickel (Pt-Ni) nanowires were developed as fuel cell electrocatalysts, and were optimized for the performance and durability in the oxygen reduction reaction. Spontaneous galvanic displacement was used to deposit Pt layers onto Ni nanowire substrates. The synthesis approach produced catalysts with high specific activities and high Pt surface areas. Hydrogen annealing improved Pt and Ni mixing and specific activity. Acid leaching was used to preferentially remove Ni near the nanowire surface, and oxygen annealing was used to stabilize near-surface Ni, improving durability and minimizing Ni dissolution. These protocols detail the optimization of each post-synthesis processing step, including hydrogen annealing to 250 °C, exposure to 0.1 M nitric acid, and oxygen annealing to 175 °C. Through these steps, Pt-Ni nanowires produced increased activities more than an order of magnitude than Pt nanoparticles, while offering significant durability improvements. The presented protocols are based on Pt-Ni systems in the development of fuel cell catalysts. These techniques have also been used for a variety of metal combinations, and can be applied to develop catalysts for a number of electrochemical processes.
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Thin-film designs by simulated annealing
NASA Astrophysics Data System (ADS)
Boudet, T.; Chaton, P.; Herault, L.; Gonon, G.; Jouanet, L.; Keller, P.
1996-11-01
With the increasing power of computers, new methods in synthesis of optical multilayer systems have appeared. Among these, the simulated-annealing algorithm has proved its efficiency in several fields of physics. We propose to show its performances in the field of optical multilayer systems through different filter designs.
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NASA Astrophysics Data System (ADS)
Ramana, E. Venkata; Ferreira, N. M.; Mahajan, A.; Ferro, Marta C.; Figueiras, F.; Graça, M. P. F.; Valente, M. A.
2018-02-01
In this work, we have fabricated lead-free piezoelectric Ba0.85Ca0.15Ti0.9Zr0.1O3 thick films by the electrophoretic deposition (EPD) followed by a continuous-wave CO2 laser annealing and demonstrated the effect of laser energy on the quality of the final product. Thick films annealed under optimized conditions, 50 W/15 min, show a controlled microstructure/density compared to those derived from higher laser power/annealing time/conventional sintering. The increase in laser power above this limit affects the grain growth kinetics and results in the compositional heterogeneities. From the results of Raman spectra, it was found that the film annealed under optimized conditions has a high degree of crystallinity and tetragonality, while the increase in laser fluence results in the growth of A1g mode. The controlled composition and microstructure, thus has resulted in the improved ferroelectricity with a remanent polarization 12 μC/cm2, on par with the bulk or larger than the films grown by the chemical solution deposition techniques. From the piezoresponse studies, we found that the film annealed at 75 W/5 min has weak ferroelectric nature with no switchable ferroelectric domains compared to those under optimized conditions. Subtle differences in phase transition temperatures and drop in ferroelectric polarization, for films annealed conventionally or at higher laser fluence, are related to porosity or site defects as well as compositional heterogeneities. Our study demonstrates that the combination of EPD and laser annealing is an effective way to achieve high quality piezoelectric thick films with a controlled composition, useful for energy harvesting applications.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Holland, Troy; Bhat, Sham; Marcy, Peter
Oxy-fired coal combustion is a promising potential carbon capture technology. Predictive computational fluid dynamics (CFD) simulations are valuable tools in evaluating and deploying oxyfuel and other carbon capture technologies, either as retrofit technologies or for new construction. However, accurate predictive combustor simulations require physically realistic submodels with low computational requirements. A recent sensitivity analysis of a detailed char conversion model (Char Conversion Kinetics (CCK)) found thermal annealing to be an extremely sensitive submodel. In the present work, further analysis of the previous annealing model revealed significant disagreement with numerous datasets from experiments performed after that annealing model was developed. Themore » annealing model was accordingly extended to reflect experimentally observed reactivity loss, because of the thermal annealing of a variety of coals under diverse char preparation conditions. The model extension was informed by a Bayesian calibration analysis. In addition, since oxyfuel conditions include extraordinarily high levels of CO 2, the development of a first-ever CO 2 reactivity loss model due to annealing is presented.« less
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Holland, Troy; Bhat, Sham; Marcy, Peter; ...
2017-08-25
Oxy-fired coal combustion is a promising potential carbon capture technology. Predictive computational fluid dynamics (CFD) simulations are valuable tools in evaluating and deploying oxyfuel and other carbon capture technologies, either as retrofit technologies or for new construction. However, accurate predictive combustor simulations require physically realistic submodels with low computational requirements. A recent sensitivity analysis of a detailed char conversion model (Char Conversion Kinetics (CCK)) found thermal annealing to be an extremely sensitive submodel. In the present work, further analysis of the previous annealing model revealed significant disagreement with numerous datasets from experiments performed after that annealing model was developed. Themore » annealing model was accordingly extended to reflect experimentally observed reactivity loss, because of the thermal annealing of a variety of coals under diverse char preparation conditions. The model extension was informed by a Bayesian calibration analysis. In addition, since oxyfuel conditions include extraordinarily high levels of CO 2, the development of a first-ever CO 2 reactivity loss model due to annealing is presented.« less
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Quantum Error Correction for Minor Embedded Quantum Annealing
NASA Astrophysics Data System (ADS)
Vinci, Walter; Paz Silva, Gerardo; Mishra, Anurag; Albash, Tameem; Lidar, Daniel
2015-03-01
While quantum annealing can take advantage of the intrinsic robustness of adiabatic dynamics, some form of quantum error correction (QEC) is necessary in order to preserve its advantages over classical computation. Moreover, realistic quantum annealers are subject to a restricted connectivity between qubits. Minor embedding techniques use several physical qubits to represent a single logical qubit with a larger set of interactions, but necessarily introduce new types of errors (whenever the physical qubits corresponding to the same logical qubit disagree). We present a QEC scheme where a minor embedding is used to generate a 8 × 8 × 2 cubic connectivity out of the native one and perform experiments on a D-Wave quantum annealer. Using a combination of optimized encoding and decoding techniques, our scheme enables the D-Wave device to solve minor embedded hard instances at least as well as it would on a native implementation. Our work is a proof-of-concept that minor embedding can be advantageously implemented in order to increase both the robustness and the connectivity of a programmable quantum annealer. Applied in conjunction with decoding techniques, this paves the way toward scalable quantum annealing with applications to hard optimization problems.
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Exploring first-order phase transitions with population annealing
NASA Astrophysics Data System (ADS)
Barash, Lev Yu.; Weigel, Martin; Shchur, Lev N.; Janke, Wolfhard
2017-03-01
Population annealing is a hybrid of sequential and Markov chain Monte Carlo methods geared towards the efficient parallel simulation of systems with complex free-energy landscapes. Systems with first-order phase transitions are among the problems in computational physics that are difficult to tackle with standard methods such as local-update simulations in the canonical ensemble, for example with the Metropolis algorithm. It is hence interesting to see whether such transitions can be more easily studied using population annealing. We report here our preliminary observations from population annealing runs for the two-dimensional Potts model with q > 4, where it undergoes a first-order transition.
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NASA Astrophysics Data System (ADS)
Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.
2018-03-01
Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to classify and rank binding affinities. Using simplified data sets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified data sets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems.
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Benwadih, M; Coppard, R; Bonrad, K; Klyszcz, A; Vuillaume, D
2016-12-21
Amorphous, sol-gel processed, indium gallium zinc oxide (IGZO) transistors on plastic substrate with a printable gate dielectric and an electron mobility of 4.5 cm 2 /(V s), as well as a mobility of 7 cm 2 /(V s) on solid substrate (Si/SiO 2 ) are reported. These performances are obtained using a low temperature pulsed light annealing technique. Ultraviolet (UV) pulsed light system is an innovative technique compared to conventional (furnace or hot-plate) annealing process that we successfully implemented on sol-gel IGZO thin film transistors (TFTs) made on plastic substrate. The photonic annealing treatment has been optimized to obtain IGZO TFTs with significant electrical properties. Organic gate dielectric layers deposited on this pulsed UV light annealed films have also been optimized. This technique is very promising for the development of amorphous IGZO TFTs on plastic substrates.
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NASA Astrophysics Data System (ADS)
Utama, D. N.; Triana, Y. S.; Iqbal, M. M.; Iksal, M.; Fikri, I.; Dharmawan, T.
2018-03-01
Mosque, for Muslim, is not only a place for daily worshipping, however as a center of culture as well. It is an important and valuable building to be well managed. For a responsible department or institution (such as Religion or Plan Department in Indonesia), to practically manage a lot of mosques is not simple task to handle. The challenge is in relation to data number and characteristic problems tackled. Specifically for renovating and rehabilitating the damaged mosques, a decision to determine the first damaged mosque priority to be renovated and rehabilitated is problematic. Through two types of optimization method, simulated-annealing and hill-climbing, a decision support model for mosque renovation and rehabilitation was systematically constructed. The method fuzzy-logic was also operated to establish the priority of eleven selected parameters. The constructed model is able to simulate an efficiency comparison between two optimization methods used and suggest the most objective decision coming from 196 generated alternatives.
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Optimizing Monitoring Designs under Alternative Objectives
Gastelum, Jason A.; USA, Richland Washington; Porter, Ellen A.; ...
2014-12-31
This paper describes an approach to identify monitoring designs that optimize detection of CO2 leakage from a carbon capture and sequestration (CCS) reservoir and compares the results generated under two alternative objective functions. The first objective function minimizes the expected time to first detection of CO2 leakage, the second more conservative objective function minimizes the maximum time to leakage detection across the set of realizations. The approach applies a simulated annealing algorithm that searches the solution space by iteratively mutating the incumbent monitoring design. The approach takes into account uncertainty by evaluating the performance of potential monitoring designs across amore » set of simulated leakage realizations. The approach relies on a flexible two-tiered signature to infer that CO2 leakage has occurred. This research is part of the National Risk Assessment Partnership, a U.S. Department of Energy (DOE) project tasked with conducting risk and uncertainty analysis in the areas of reservoir performance, natural leakage pathways, wellbore integrity, groundwater protection, monitoring, and systems level modeling.« less
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High-temperature annealing of graphite: A molecular dynamics study
NASA Astrophysics Data System (ADS)
Petersen, Andrew; Gillette, Victor
2018-05-01
A modified AIREBO potential was developed to simulate the effects of thermal annealing on the structure and physical properties of damaged graphite. AIREBO parameter modifications were made to reproduce Density Functional Theory interstitial results. These changes to the potential resulted in high-temperature annealing of the model, as measured by stored-energy reduction. These results show some resemblance to experimental high-temperature annealing results, and show promise that annealing effects in graphite are accessible with molecular dynamics and reactive potentials.
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Annealing Induced Re-crystallization in CH3NH3PbI3−xClx for High Performance Perovskite Solar Cells
Yang, Yingguo; Feng, Shanglei; Li, Meng; Xu, Weidong; Yin, Guangzhi; Wang, Zhaokui; Sun, Baoquan; Gao, Xingyu
2017-01-01
Using poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) as hole conductor, a series of inverted planar CH3NH3PbI3−xClx perovskite solar cells (PSCs) were fabricated based on perovskite annealed by an improved time-temperature dependent (TTD) procedure in a flowing nitrogen atmosphere for different time. Only after an optimum annealing time, an optimized power conversion efficiency of 14.36% could be achieved. To understand their performance dependence on annealing time, an in situ real-time synchrotron-based grazing incidence X-ray diffraction (GIXRD) was used to monitor a step-by-step gradual structure transformation from distinct mainly organic-inorganic hybrid materials into highly ordered CH3NH3PbI3 crystal during annealing. However, a re-crystallization process of perovskite crystal was observed for the first time during such an annealing procedure, which helps to enhance the perovskite crystallization and preferential orientations. The present GIXRD findings could well explain the drops of the open circuit voltage (Voc) and the fill factor (FF) during the ramping of temperature as well as the optimized power conversion efficiency achieved after an optimum annealing time. Thus, the present study not only illustrates clearly the decisive roles of post-annealing in the formation of solution-processed perovskite to better understand its formation mechanism, but also demonstrates the crucial dependences of device performance on the perovskite microstructure in PSCs. PMID:28429762
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GPU accelerated population annealing algorithm
NASA Astrophysics Data System (ADS)
Barash, Lev Yu.; Weigel, Martin; Borovský, Michal; Janke, Wolfhard; Shchur, Lev N.
2017-11-01
Population annealing is a promising recent approach for Monte Carlo simulations in statistical physics, in particular for the simulation of systems with complex free-energy landscapes. It is a hybrid method, combining importance sampling through Markov chains with elements of sequential Monte Carlo in the form of population control. While it appears to provide algorithmic capabilities for the simulation of such systems that are roughly comparable to those of more established approaches such as parallel tempering, it is intrinsically much more suitable for massively parallel computing. Here, we tap into this structural advantage and present a highly optimized implementation of the population annealing algorithm on GPUs that promises speed-ups of several orders of magnitude as compared to a serial implementation on CPUs. While the sample code is for simulations of the 2D ferromagnetic Ising model, it should be easily adapted for simulations of other spin models, including disordered systems. Our code includes implementations of some advanced algorithmic features that have only recently been suggested, namely the automatic adaptation of temperature steps and a multi-histogram analysis of the data at different temperatures. Program Files doi:http://dx.doi.org/10.17632/sgzt4b7b3m.1 Licensing provisions: Creative Commons Attribution license (CC BY 4.0) Programming language: C, CUDA External routines/libraries: NVIDIA CUDA Toolkit 6.5 or newer Nature of problem: The program calculates the internal energy, specific heat, several magnetization moments, entropy and free energy of the 2D Ising model on square lattices of edge length L with periodic boundary conditions as a function of inverse temperature β. Solution method: The code uses population annealing, a hybrid method combining Markov chain updates with population control. The code is implemented for NVIDIA GPUs using the CUDA language and employs advanced techniques such as multi-spin coding, adaptive temperature steps and multi-histogram reweighting. Additional comments: Code repository at https://github.com/LevBarash/PAising. The system size and size of the population of replicas are limited depending on the memory of the GPU device used. For the default parameter values used in the sample programs, L = 64, θ = 100, β0 = 0, βf = 1, Δβ = 0 . 005, R = 20 000, a typical run time on an NVIDIA Tesla K80 GPU is 151 seconds for the single spin coded (SSC) and 17 seconds for the multi-spin coded (MSC) program (see Section 2 for a description of these parameters).
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Su, Hongsheng
2017-12-18
Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration. In addition, diverse types of DGs are made equivalent to four types of nodes in flow calculation by the backward or forward sweep method, and reactive power sharing principles and allocation theory are applied to determine initial reactive power value and execute subsequent correction, thus providing the algorithm a better start to speed up the convergence. Finally, a mathematical model of the minimum economic cost is established for the siting and sizing of DGs under the location and capacity uncertainties of each single DG. Its objective function considers investment and operation cost of DGs, grid loss cost, annual purchase electricity cost, and environmental pollution cost, and the constraints include power flow, bus voltage, conductor current, and DG capacity. Through applications in an IEEE33-node distributed system, it is found that the proposed method can achieve desirable economic efficiency and safer voltage level relative to traditional PSO and SA-PSO algorithms, and is a more effective planning method for the siting and sizing of DGs in distributed power grids.
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Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater
Shabbir, Javid; M. AbdEl-Salam, Nasser; Hussain, Tajammal
2016-01-01
Sodium is an integral part of water, and its excessive amount in drinking water causes high blood pressure and hypertension. In the present paper, spatial distribution of sodium concentration in drinking water is modeled and optimized sampling designs for selecting sampling locations is calculated for three divisions in Punjab, Pakistan. Universal kriging and Bayesian universal kriging are used to predict the sodium concentrations. Spatial simulated annealing is used to generate optimized sampling designs. Different estimation methods (i.e., maximum likelihood, restricted maximum likelihood, ordinary least squares, and weighted least squares) are used to estimate the parameters of the variogram model (i.e, exponential, Gaussian, spherical and cubic). It is concluded that Bayesian universal kriging fits better than universal kriging. It is also observed that the universal kriging predictor provides minimum mean universal kriging variance for both adding and deleting locations during sampling design. PMID:27683016
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Bifurcation-based adiabatic quantum computation with a nonlinear oscillator network.
Goto, Hayato
2016-02-22
The dynamics of nonlinear systems qualitatively change depending on their parameters, which is called bifurcation. A quantum-mechanical nonlinear oscillator can yield a quantum superposition of two oscillation states, known as a Schrödinger cat state, via quantum adiabatic evolution through its bifurcation point. Here we propose a quantum computer comprising such quantum nonlinear oscillators, instead of quantum bits, to solve hard combinatorial optimization problems. The nonlinear oscillator network finds optimal solutions via quantum adiabatic evolution, where nonlinear terms are increased slowly, in contrast to conventional adiabatic quantum computation or quantum annealing, where quantum fluctuation terms are decreased slowly. As a result of numerical simulations, it is concluded that quantum superposition and quantum fluctuation work effectively to find optimal solutions. It is also notable that the present computer is analogous to neural computers, which are also networks of nonlinear components. Thus, the present scheme will open new possibilities for quantum computation, nonlinear science, and artificial intelligence.
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Traveling-Wave Tube Efficiency Enhancement
NASA Technical Reports Server (NTRS)
Dayton, James A., Jr.
2011-01-01
Traveling-wave tubes (TWT's) are used to amplify microwave communication signals on virtually all NASA and commercial spacecraft. Because TWT's are a primary power user, increasing their power efficiency is important for reducing spacecraft weight and cost. NASA Glenn Research Center has played a major role in increasing TWT efficiency over the last thirty years. In particular, two types of efficiency optimization algorithms have been developed for coupled-cavity TWT's. The first is the phase-adjusted taper which was used to increase the RF power from 420 to 1000 watts and the RF efficiency from 9.6% to 22.6% for a Ka-band (29.5 GHz) TWT. This was a record efficiency at this frequency level. The second is an optimization algorithm based on simulated annealing. This improved algorithm is more general and can be used to optimize efficiency over a frequency bandwidth and to provide a robust design for very high frequency TWT's in which dimensional tolerance variations are significant.
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Evolutionary computation applied to the reconstruction of 3-D surface topography in the SEM.
Kodama, Tetsuji; Li, Xiaoyuan; Nakahira, Kenji; Ito, Dai
2005-10-01
A genetic algorithm has been applied to the line profile reconstruction from the signals of the standard secondary electron (SE) and/or backscattered electron detectors in a scanning electron microscope. This method solves the topographical surface reconstruction problem as one of combinatorial optimization. To extend this optimization approach for three-dimensional (3-D) surface topography, this paper considers the use of a string coding where a 3-D surface topography is represented by a set of coordinates of vertices. We introduce the Delaunay triangulation, which attains the minimum roughness for any set of height data to capture the fundamental features of the surface being probed by an electron beam. With this coding, the strings are processed with a class of hybrid optimization algorithms that combine genetic algorithms and simulated annealing algorithms. Experimental results on SE images are presented.
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Bifurcation-based adiabatic quantum computation with a nonlinear oscillator network
NASA Astrophysics Data System (ADS)
Goto, Hayato
2016-02-01
The dynamics of nonlinear systems qualitatively change depending on their parameters, which is called bifurcation. A quantum-mechanical nonlinear oscillator can yield a quantum superposition of two oscillation states, known as a Schrödinger cat state, via quantum adiabatic evolution through its bifurcation point. Here we propose a quantum computer comprising such quantum nonlinear oscillators, instead of quantum bits, to solve hard combinatorial optimization problems. The nonlinear oscillator network finds optimal solutions via quantum adiabatic evolution, where nonlinear terms are increased slowly, in contrast to conventional adiabatic quantum computation or quantum annealing, where quantum fluctuation terms are decreased slowly. As a result of numerical simulations, it is concluded that quantum superposition and quantum fluctuation work effectively to find optimal solutions. It is also notable that the present computer is analogous to neural computers, which are also networks of nonlinear components. Thus, the present scheme will open new possibilities for quantum computation, nonlinear science, and artificial intelligence.
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Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods.
Gonzalez-Navarro, Felix F; Stilianova-Stoytcheva, Margarita; Renteria-Gutierrez, Livier; Belanche-Muñoz, Lluís A; Flores-Rios, Brenda L; Ibarra-Esquer, Jorge E
2016-10-26
Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization.
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Jihong, Qu
2014-01-01
Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision. PMID:24895663
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Ren, Kun; Jihong, Qu
2014-01-01
Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision.
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Relabeling exchange method (REM) for learning in neural networks
NASA Astrophysics Data System (ADS)
Wu, Wen; Mammone, Richard J.
1994-02-01
The supervised training of neural networks require the use of output labels which are usually arbitrarily assigned. In this paper it is shown that there is a significant difference in the rms error of learning when `optimal' label assignment schemes are used. We have investigated two efficient random search algorithms to solve the relabeling problem: the simulated annealing and the genetic algorithm. However, we found them to be computationally expensive. Therefore we shall introduce a new heuristic algorithm called the Relabeling Exchange Method (REM) which is computationally more attractive and produces optimal performance. REM has been used to organize the optimal structure for multi-layered perceptrons and neural tree networks. The method is a general one and can be implemented as a modification to standard training algorithms. The motivation of the new relabeling strategy is based on the present interpretation of dyslexia as an encoding problem.
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Capturing planar shapes by approximating their outlines
NASA Astrophysics Data System (ADS)
Sarfraz, M.; Riyazuddin, M.; Baig, M. H.
2006-05-01
A non-deterministic evolutionary approach for approximating the outlines of planar shapes has been developed. Non-uniform Rational B-splines (NURBS) have been utilized as an underlying approximation curve scheme. Simulated Annealing heuristic is used as an evolutionary methodology. In addition to independent studies of the optimization of weight and knot parameters of the NURBS, a separate scheme has also been developed for the optimization of weights and knots simultaneously. The optimized NURBS models have been fitted over the contour data of the planar shapes for the ultimate and automatic output. The output results are visually pleasing with respect to the threshold provided by the user. A web-based system has also been developed for the effective and worldwide utilization. The objective of this system is to provide the facility to visualize the output to the whole world through internet by providing the freedom to the user for various desired input parameters setting in the algorithm designed.
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Efficient Optimization of Low-Thrust Spacecraft Trajectories
NASA Technical Reports Server (NTRS)
Lee, Seungwon; Fink, Wolfgang; Russell, Ryan; Terrile, Richard; Petropoulos, Anastassios; vonAllmen, Paul
2007-01-01
A paper describes a computationally efficient method of optimizing trajectories of spacecraft driven by propulsion systems that generate low thrusts and, hence, must be operated for long times. A common goal in trajectory-optimization problems is to find minimum-time, minimum-fuel, or Pareto-optimal trajectories (here, Pareto-optimality signifies that no other solutions are superior with respect to both flight time and fuel consumption). The present method utilizes genetic and simulated-annealing algorithms to search for globally Pareto-optimal solutions. These algorithms are implemented in parallel form to reduce computation time. These algorithms are coupled with either of two traditional trajectory- design approaches called "direct" and "indirect." In the direct approach, thrust control is discretized in either arc time or arc length, and the resulting discrete thrust vectors are optimized. The indirect approach involves the primer-vector theory (introduced in 1963), in which the thrust control problem is transformed into a co-state control problem and the initial values of the co-state vector are optimized. In application to two example orbit-transfer problems, this method was found to generate solutions comparable to those of other state-of-the-art trajectory-optimization methods while requiring much less computation time.
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Performance of Quantum Annealers on Hard Scheduling Problems
NASA Astrophysics Data System (ADS)
Pokharel, Bibek; Venturelli, Davide; Rieffel, Eleanor
Quantum annealers have been employed to attack a variety of optimization problems. We compared the performance of the current D-Wave 2X quantum annealer to that of the previous generation D-Wave Two quantum annealer on scheduling-type planning problems. Further, we compared the effect of different anneal times, embeddings of the logical problem, and different settings of the ferromagnetic coupling JF across the logical vertex-model on the performance of the D-Wave 2X quantum annealer. Our results show that at the best settings, the scaling of expected anneal time to solution for D-WAVE 2X is better than that of the DWave Two, but still inferior to that of state of the art classical solvers on these problems. We discuss the implication of our results for the design and programming of future quantum annealers. Supported by NASA Ames Research Center.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Greenlee, Jordan D., E-mail: jordan.greenlee.ctr@nrl.navy.mil; Feigelson, Boris N.; Anderson, Travis J.
2014-08-14
The first step of a multi-cycle rapid thermal annealing process was systematically studied. The surface, structure, and optical properties of Mg implanted GaN thin films annealed at temperatures ranging from 900 to 1200 °C were investigated by Raman spectroscopy, photoluminescence, UV-visible spectroscopy, atomic force microscopy, and Nomarski microscopy. The GaN thin films are capped with two layers of in-situ metal organic chemical vapor deposition -grown AlN and annealed in 24 bar of N{sub 2} overpressure to avoid GaN decomposition. The crystal quality of the GaN improves with increasing annealing temperature as confirmed by UV-visible spectroscopy and the full widths at halfmore » maximums of the E{sub 2} and A{sub 1} (LO) Raman modes. The crystal quality of films annealed above 1100 °C exceeds the quality of the as-grown films. At 1200 °C, Mg is optically activated, which is determined by photoluminescence measurements. However, at 1200 °C, the GaN begins to decompose as evidenced by pit formation on the surface of the samples. Therefore, it was determined that the optimal temperature for the first step in a multi-cycle rapid thermal anneal process should be conducted at 1150 °C due to crystal quality and surface morphology considerations.« less
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Computer-Assisted Scheduling of Army Unit Training: An Application of Simulated Annealing.
ERIC Educational Resources Information Center
Hart, Roland J.; Goehring, Dwight J.
This report of an ongoing research project intended to provide computer assistance to Army units for the scheduling of training focuses on the feasibility of simulated annealing, a heuristic approach for solving scheduling problems. Following an executive summary and brief introduction, the document is divided into three sections. First, the Army…
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Re'class'ification of 'quant'ified classical simulated annealing
NASA Astrophysics Data System (ADS)
Tanaka, Toshiyuki
2009-12-01
We discuss a classical reinterpretation of quantum-mechanics-based analysis of classical Markov chains with detailed balance, that is based on the quantum-classical correspondence. The classical reinterpretation is then used to demonstrate that it successfully reproduces a sufficient condition for cooling schedule in classical simulated annealing, which has the inverse-logarithmic scaling.
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NASA Astrophysics Data System (ADS)
Lopatynskyi, Andrii M.; Lytvyn, Vitalii K.; Nazarenko, Volodymyr I.; Guo, L. Jay; Lucas, Brandon D.; Chegel, Volodymyr I.
2015-03-01
This paper attempts to compare the main features of random and highly ordered gold nanostructure arrays (NSA) prepared by thermally annealed island film and nanoimprint lithography (NIL) techniques, respectively. Each substrate possesses different morphology in terms of plasmonic enhancement. Both methods allow such important features as spectral tuning of plasmon resonance position depending on size and shape of nanostructures; however, the time and cost is quite different. The respective comparison was performed experimentally and theoretically for a number of samples with different geometrical parameters. Spectral characteristics of fabricated NSA exhibited an expressed plasmon peak in the range from 576 to 809 nm for thermally annealed samples and from 606 to 783 nm for samples prepared by NIL. Modelling of the optical response for nanostructures with typical shapes associated with these techniques (parallelepiped for NIL and semi-ellipsoid for annealed island films) was performed using finite-difference time-domain calculations. Mathematical simulations have indicated the dependence of electric field enhancement on the shape and size of the nanoparticles. As an important point, the distribution of electric field at so-called `hot spots' was considered. Parallelepiped-shaped nanoparticles were shown to yield maximal enhancement values by an order of magnitude greater than their semi-ellipsoid-shaped counterparts; however, both nanoparticle shapes have demonstrated comparable effective electrical field enhancement values. Optimized Au nanostructures with equivalent diameters ranging from 85 to 143 nm and height equal to 35 nm were obtained for both techniques, resulting in the largest electrical field enhancement. The application of island film thermal annealing method for nanochips fabrication can be considered as a possible cost-effective platform for various surface-enhanced spectroscopies; while the NIL-fabricated NSA looks like more effective for sensing of small-size objects.
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NASA Astrophysics Data System (ADS)
Bermundo, Juan Paolo; Ishikawa, Yasuaki; Fujii, Mami N.; Nonaka, Toshiaki; Ishihara, Ryoichi; Ikenoue, Hiroshi; Uraoka, Yukiharu
2016-01-01
We demonstrate the use of excimer laser annealing (ELA) as a low temperature annealing alternative to anneal amorphous InGaZnO (a-IGZO) thin-film transistors (TFTs) passivated by a solution-processed hybrid passivation layer. Usually, a-IGZO is annealed using thermal annealing at high temperatures of up to 400 °C. As an alternative to high temperature thermal annealing, two types of ELA, XeCl (308 nm) and KrF (248 nm) ELA, are introduced. Both ELA types enhanced the electrical characteristics of a-IGZO TFTs leading to a mobility improvement of ~13 cm2 V-1 s-1 and small threshold voltage which varied from ~0-3 V. Furthermore, two-dimensional heat simulation using COMSOL Multiphysics was used to identify possible degradation sites, analyse laser heat localization, and confirm that the substrate temperature is below 50 °C. The two-dimensional heat simulation showed that the substrate temperature remained at very low temperatures, less than 30 °C, during ELA. This implies that any flexible material can be used as the substrate. These results demonstrate the large potential of ELA as a low temperature annealing alternative for already-passivated a-IGZO TFTs.
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Metaheuristic Algorithms for Convolution Neural Network
Fanany, Mohamad Ivan; Arymurthy, Aniati Murni
2016-01-01
A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent). PMID:27375738
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Metaheuristic Algorithms for Convolution Neural Network.
Rere, L M Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni
2016-01-01
A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).
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Solving Fuzzy Optimization Problem Using Hybrid Ls-Sa Method
NASA Astrophysics Data System (ADS)
Vasant, Pandian
2011-06-01
Fuzzy optimization problem has been one of the most and prominent topics inside the broad area of computational intelligent. It's especially relevant in the filed of fuzzy non-linear programming. It's application as well as practical realization can been seen in all the real world problems. In this paper a large scale non-linear fuzzy programming problem has been solved by hybrid optimization techniques of Line Search (LS), Simulated Annealing (SA) and Pattern Search (PS). As industrial production planning problem with cubic objective function, 8 decision variables and 29 constraints has been solved successfully using LS-SA-PS hybrid optimization techniques. The computational results for the objective function respect to vagueness factor and level of satisfaction has been provided in the form of 2D and 3D plots. The outcome is very promising and strongly suggests that the hybrid LS-SA-PS algorithm is very efficient and productive in solving the large scale non-linear fuzzy programming problem.
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Groff, Shannon C.; Loftin, Cynthia S.; Drummond, Frank; Bushmann, Sara; McGill, Brian J.
2016-01-01
Non-native honeybees historically have been managed for crop pollination, however, recent population declines draw attention to pollination services provided by native bees. We applied the InVEST Crop Pollination model, developed to predict native bee abundance from habitat resources, in Maine's wild blueberry crop landscape. We evaluated model performance with parameters informed by four approaches: 1) expert opinion; 2) sensitivity analysis; 3) sensitivity analysis informed model optimization; and, 4) simulated annealing (uninformed) model optimization. Uninformed optimization improved model performance by 29% compared to expert opinion-informed model, while sensitivity-analysis informed optimization improved model performance by 54%. This suggests that expert opinion may not result in the best parameter values for the InVEST model. The proportion of deciduous/mixed forest within 2000 m of a blueberry field also reliably predicted native bee abundance in blueberry fields, however, the InVEST model provides an efficient tool to estimate bee abundance beyond the field perimeter.
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Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance
Alia, Shaun M.; Pivovar, Bryan S.
2018-01-01
Platinum-nickel (Pt-Ni) nanowires were developed as fuel cell electrocatalysts, and were optimized for the performance and durability in the oxygen reduction reaction. Spontaneous galvanic displacement was used to deposit Pt layers onto Ni nanowire substrates. The synthesis approach produced catalysts with high specific activities and high Pt surface areas. Hydrogen annealing improved Pt and Ni mixing and specific activity. Acid leaching was used to preferentially remove Ni near the nanowire surface, and oxygen annealing was used to stabilize near-surface Ni, improving durability and minimizing Ni dissolution. These protocols detail the optimization of each post-synthesis processing step, including hydrogen annealing tomore » 250 degrees C, exposure to 0.1 M nitric acid, and oxygen annealing to 175 degrees C. Through these steps, Pt-Ni nanowires produced increased activities more than an order of magnitude than Pt nanoparticles, while offering significant durability improvements. The presented protocols are based on Pt-Ni systems in the development of fuel cell catalysts. Furthermore, these techniques have also been used for a variety of metal combinations, and can be applied to develop catalysts for a number of electrochemical processes.« less
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Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance
DOE Office of Scientific and Technical Information (OSTI.GOV)
Alia, Shaun M.; Pivovar, Bryan S.
Platinum-nickel (Pt-Ni) nanowires were developed as fuel cell electrocatalysts, and were optimized for the performance and durability in the oxygen reduction reaction. Spontaneous galvanic displacement was used to deposit Pt layers onto Ni nanowire substrates. The synthesis approach produced catalysts with high specific activities and high Pt surface areas. Hydrogen annealing improved Pt and Ni mixing and specific activity. Acid leaching was used to preferentially remove Ni near the nanowire surface, and oxygen annealing was used to stabilize near-surface Ni, improving durability and minimizing Ni dissolution. These protocols detail the optimization of each post-synthesis processing step, including hydrogen annealing tomore » 250 degrees C, exposure to 0.1 M nitric acid, and oxygen annealing to 175 degrees C. Through these steps, Pt-Ni nanowires produced increased activities more than an order of magnitude than Pt nanoparticles, while offering significant durability improvements. The presented protocols are based on Pt-Ni systems in the development of fuel cell catalysts. Furthermore, these techniques have also been used for a variety of metal combinations, and can be applied to develop catalysts for a number of electrochemical processes.« less
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Microstructure and Magnetic Properties of Optimally Annealed Ni43Mn41Co5Sn11Heusler Alloy
NASA Astrophysics Data System (ADS)
Elwindari, Nastiti; Kurniawan, Budhy; Kurniawan, Candra; Manaf, Azwar
2017-05-01
In this work, synthesis and characterization of a polycrystalline Ni43Mn41Co5Sn11 (NMCS) alloy are reported. Alloy preparation was conducted by melting the constituent components of the designated alloy under an inert Argon (Ar) atmosphere in a vacuum mini arc-melting furnace. Microstructure observation to the as-cast and annealed ingots showing dendritic structure in the as-cast sample. Series of annealing treatment to the sample at 1173 K have changed dendrites progressively in the homogeneous structure after 24 hours annealing time. The annealed sample consisted of a NiMnCoSn main phase with 99.3 % volume fraction. Hence, the 24 hours annealed ingot is a single phase alloy. The curie temperature of the annealed NMCS alloys was found in the range 348∼351 K. Loop hysteresis evaluation of the annealed ingots showed that ingot which annealed for 12 hours showed the largest magnetization value of 57.96 emu/g.
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NASA Technical Reports Server (NTRS)
Wang, Wenlong; Mandra, Salvatore; Katzgraber, Helmut G.
2016-01-01
In this paper, we propose a patch planting method for creating arbitrarily large spin glass instances with known ground states. The scaling of the computational complexity of these instances with various block numbers and sizes is investigated and compared with random instances using population annealing Monte Carlo and the quantum annealing DW2X machine. The method can be useful for benchmarking tests for future generation quantum annealing machines, classical and quantum mechanical optimization algorithms.
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Review: Optimization methods for groundwater modeling and management
NASA Astrophysics Data System (ADS)
Yeh, William W.-G.
2015-09-01
Optimization methods have been used in groundwater modeling as well as for the planning and management of groundwater systems. This paper reviews and evaluates the various optimization methods that have been used for solving the inverse problem of parameter identification (estimation), experimental design, and groundwater planning and management. Various model selection criteria are discussed, as well as criteria used for model discrimination. The inverse problem of parameter identification concerns the optimal determination of model parameters using water-level observations. In general, the optimal experimental design seeks to find sampling strategies for the purpose of estimating the unknown model parameters. A typical objective of optimal conjunctive-use planning of surface water and groundwater is to minimize the operational costs of meeting water demand. The optimization methods include mathematical programming techniques such as linear programming, quadratic programming, dynamic programming, stochastic programming, nonlinear programming, and the global search algorithms such as genetic algorithms, simulated annealing, and tabu search. Emphasis is placed on groundwater flow problems as opposed to contaminant transport problems. A typical two-dimensional groundwater flow problem is used to explain the basic formulations and algorithms that have been used to solve the formulated optimization problems.
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Sergeant, Martin J.; Constantinidou, Chrystala; Cogan, Tristan; Penn, Charles W.; Pallen, Mark J.
2012-01-01
The analysis of 16S-rDNA sequences to assess the bacterial community composition of a sample is a widely used technique that has increased with the advent of high throughput sequencing. Although considerable effort has been devoted to identifying the most informative region of the 16S gene and the optimal informatics procedures to process the data, little attention has been paid to the PCR step, in particular annealing temperature and primer length. To address this, amplicons derived from 16S-rDNA were generated from chicken caecal content DNA using different annealing temperatures, primers and different DNA extraction procedures. The amplicons were pyrosequenced to determine the optimal protocols for capture of maximum bacterial diversity from a chicken caecal sample. Even at very low annealing temperatures there was little effect on the community structure, although the abundance of some OTUs such as Bifidobacterium increased. Using shorter primers did not reveal any novel OTUs but did change the community profile obtained. Mechanical disruption of the sample by bead beating had a significant effect on the results obtained, as did repeated freezing and thawing. In conclusion, existing primers and standard annealing temperatures captured as much diversity as lower annealing temperatures and shorter primers. PMID:22666455
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Experimental quantum annealing: case study involving the graph isomorphism problem.
Zick, Kenneth M; Shehab, Omar; French, Matthew
2015-06-08
Quantum annealing is a proposed combinatorial optimization technique meant to exploit quantum mechanical effects such as tunneling and entanglement. Real-world quantum annealing-based solvers require a combination of annealing and classical pre- and post-processing; at this early stage, little is known about how to partition and optimize the processing. This article presents an experimental case study of quantum annealing and some of the factors involved in real-world solvers, using a 504-qubit D-Wave Two machine and the graph isomorphism problem. To illustrate the role of classical pre-processing, a compact Hamiltonian is presented that enables a reduced Ising model for each problem instance. On random N-vertex graphs, the median number of variables is reduced from N(2) to fewer than N log2 N and solvable graph sizes increase from N = 5 to N = 13. Additionally, error correction via classical post-processing majority voting is evaluated. While the solution times are not competitive with classical approaches to graph isomorphism, the enhanced solver ultimately classified correctly every problem that was mapped to the processor and demonstrated clear advantages over the baseline approach. The results shed some light on the nature of real-world quantum annealing and the associated hybrid classical-quantum solvers.
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Experimental quantum annealing: case study involving the graph isomorphism problem
Zick, Kenneth M.; Shehab, Omar; French, Matthew
2015-01-01
Quantum annealing is a proposed combinatorial optimization technique meant to exploit quantum mechanical effects such as tunneling and entanglement. Real-world quantum annealing-based solvers require a combination of annealing and classical pre- and post-processing; at this early stage, little is known about how to partition and optimize the processing. This article presents an experimental case study of quantum annealing and some of the factors involved in real-world solvers, using a 504-qubit D-Wave Two machine and the graph isomorphism problem. To illustrate the role of classical pre-processing, a compact Hamiltonian is presented that enables a reduced Ising model for each problem instance. On random N-vertex graphs, the median number of variables is reduced from N2 to fewer than N log2 N and solvable graph sizes increase from N = 5 to N = 13. Additionally, error correction via classical post-processing majority voting is evaluated. While the solution times are not competitive with classical approaches to graph isomorphism, the enhanced solver ultimately classified correctly every problem that was mapped to the processor and demonstrated clear advantages over the baseline approach. The results shed some light on the nature of real-world quantum annealing and the associated hybrid classical-quantum solvers. PMID:26053973
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Sergeant, Martin J; Constantinidou, Chrystala; Cogan, Tristan; Penn, Charles W; Pallen, Mark J
2012-01-01
The analysis of 16S-rDNA sequences to assess the bacterial community composition of a sample is a widely used technique that has increased with the advent of high throughput sequencing. Although considerable effort has been devoted to identifying the most informative region of the 16S gene and the optimal informatics procedures to process the data, little attention has been paid to the PCR step, in particular annealing temperature and primer length. To address this, amplicons derived from 16S-rDNA were generated from chicken caecal content DNA using different annealing temperatures, primers and different DNA extraction procedures. The amplicons were pyrosequenced to determine the optimal protocols for capture of maximum bacterial diversity from a chicken caecal sample. Even at very low annealing temperatures there was little effect on the community structure, although the abundance of some OTUs such as Bifidobacterium increased. Using shorter primers did not reveal any novel OTUs but did change the community profile obtained. Mechanical disruption of the sample by bead beating had a significant effect on the results obtained, as did repeated freezing and thawing. In conclusion, existing primers and standard annealing temperatures captured as much diversity as lower annealing temperatures and shorter primers.
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Prediction-based Dynamic Energy Management in Wireless Sensor Networks
Wang, Xue; Ma, Jun-Jie; Wang, Sheng; Bi, Dao-Wei
2007-01-01
Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Dong, Futao, E-mail: dongft@sina.com; Du, Linxiu; Liu, Xianghua
2013-10-15
The influence of Mn,S and B contents on microstructural characteristics, mechanical properties and hydrogen trapping ability of low-carbon Al-killed enamel steel was investigated. The materials were produced and processed in a laboratory and the ultra-fast continuous annealing processing was performed using a continuous annealing simulator. It was found that increasing Mn,S contents in steel can improve its hydrogen trapping ability which is attributed by refined ferrite grains, more dispersed cementite and added MnS inclusions. Nevertheless, it deteriorates mechanical properties of steel sheet. Addition of trace boron results in both good mechanical properties and significantly improved hydrogen trapping ability. The boronmore » combined with nitrogen segregating at grain boundaries, cementite and MnS inclusions, provides higher amount of attractive hydrogen trapping sites and raises the activation energy for hydrogen desorption from them. - Highlights: • We study microstructures and properties in low-carbon Al-killed enamel steel. • Hydrogen diffusion coefficients are measured to reflect fish-scale resistance. • Manganese improves hydrogen trapping ability but decrease deep-drawing ability. • Boron improves both hydrogen trapping ability and deep-drawing ability. • Both excellent mechanical properties and fish-scale resistance can be matched.« less
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2017-01-01
The Annual Crop Planning (ACP) problem was a recently introduced problem in the literature. This study further expounds on this problem by presenting a new mathematical formulation, which is based on market economic factors. To determine solutions, a new local search metaheuristic algorithm is investigated which is called the enhanced Best Performance Algorithm (eBPA). eBPA’s results are compared against two well-known local search metaheuristic algorithms; these include Tabu Search and Simulated Annealing. The results show the potential of the eBPA for continuous optimization problems. PMID:28792495
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Wahyuningsih, Hesty; K Cayami, Ferdy; Bahrudin, Udin; A Sobirin, Mochamad; Ep Mundhofir, Farmaditya; Mh Faradz, Sultana; Hisatome, Ichiro
2017-03-01
High resolution melting (HRM) is a post-PCR technique for variant screening and genotyping based on the different melting points of DNA fragments. The advantages of this technique are that it is fast, simple, and efficient and has a high output, particularly for screening of a large number of samples. APOA1 encodes apolipoprotein A1 (apoA1) which is a major component of high density lipoprotein cholesterol (HDL-C). This study aimed to obtain an optimal quantitative polymerase chain reaction (qPCR)-HRM condition for screening of APOA1 variance. Genomic DNA was isolated from a peripheral blood sample using the salting out method. APOA1 was amplified using the RotorGeneQ 5Plex HRM. The PCR product was visualized with the HRM amplification curve and confirmed using gel electrophoresis. The melting profile was confirmed by looking at the melting curve. Five sets of primers covering the translated region of APOA1 exons were designed with expected PCR product size of 100-400 bps. The amplified segments of DNA were amplicons 2, 3, 4A, 4B, and 4C. Amplicons 2, 3 and 4B were optimized at an annealing temperature of 60 °C at 40 PCR cycles. Amplicon 4A was optimized at an annealing temperature of 62 °C at 45 PCR cycles. Amplicon 4C was optimized at an annealing temperature of 63 °C at 50 PCR cycles. In addition to the suitable procedures of DNA isolation and quantification, primer design and an estimated PCR product size, the data of this study showed that appropriate annealing temperature and PCR cycles were important factors in optimization of HRM technique for variant screening in APOA1 .
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Wahyuningsih, Hesty; K Cayami, Ferdy; Bahrudin, Udin; A Sobirin, Mochamad; EP Mundhofir, Farmaditya; MH Faradz, Sultana; Hisatome, Ichiro
2017-01-01
Background High resolution melting (HRM) is a post-PCR technique for variant screening and genotyping based on the different melting points of DNA fragments. The advantages of this technique are that it is fast, simple, and efficient and has a high output, particularly for screening of a large number of samples. APOA1 encodes apolipoprotein A1 (apoA1) which is a major component of high density lipoprotein cholesterol (HDL-C). This study aimed to obtain an optimal quantitative polymerase chain reaction (qPCR)-HRM condition for screening of APOA1 variance. Methods Genomic DNA was isolated from a peripheral blood sample using the salting out method. APOA1 was amplified using the RotorGeneQ 5Plex HRM. The PCR product was visualized with the HRM amplification curve and confirmed using gel electrophoresis. The melting profile was confirmed by looking at the melting curve. Results Five sets of primers covering the translated region of APOA1 exons were designed with expected PCR product size of 100–400 bps. The amplified segments of DNA were amplicons 2, 3, 4A, 4B, and 4C. Amplicons 2, 3 and 4B were optimized at an annealing temperature of 60 °C at 40 PCR cycles. Amplicon 4A was optimized at an annealing temperature of 62 °C at 45 PCR cycles. Amplicon 4C was optimized at an annealing temperature of 63 °C at 50 PCR cycles. Conclusion In addition to the suitable procedures of DNA isolation and quantification, primer design and an estimated PCR product size, the data of this study showed that appropriate annealing temperature and PCR cycles were important factors in optimization of HRM technique for variant screening in APOA1. PMID:28331418
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Population annealing with weighted averages: A Monte Carlo method for rough free-energy landscapes
NASA Astrophysics Data System (ADS)
Machta, J.
2010-08-01
The population annealing algorithm introduced by Hukushima and Iba is described. Population annealing combines simulated annealing and Boltzmann weighted differential reproduction within a population of replicas to sample equilibrium states. Population annealing gives direct access to the free energy. It is shown that unbiased measurements of observables can be obtained by weighted averages over many runs with weight factors related to the free-energy estimate from the run. Population annealing is well suited to parallelization and may be a useful alternative to parallel tempering for systems with rough free-energy landscapes such as spin glasses. The method is demonstrated for spin glasses.
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Coherent Coupled Qubits for Quantum Annealing
NASA Astrophysics Data System (ADS)
Weber, Steven J.; Samach, Gabriel O.; Hover, David; Gustavsson, Simon; Kim, David K.; Melville, Alexander; Rosenberg, Danna; Sears, Adam P.; Yan, Fei; Yoder, Jonilyn L.; Oliver, William D.; Kerman, Andrew J.
2017-07-01
Quantum annealing is an optimization technique which potentially leverages quantum tunneling to enhance computational performance. Existing quantum annealers use superconducting flux qubits with short coherence times limited primarily by the use of large persistent currents Ip. Here, we examine an alternative approach using qubits with smaller Ip and longer coherence times. We demonstrate tunable coupling, a basic building block for quantum annealing, between two flux qubits with small (approximately 50-nA) persistent currents. Furthermore, we characterize qubit coherence as a function of coupler setting and investigate the effect of flux noise in the coupler loop on qubit coherence. Our results provide insight into the available design space for next-generation quantum annealers with improved coherence.
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Designs for Risk Evaluation and Management
DOE Office of Scientific and Technical Information (OSTI.GOV)
The Designs for Risk Evaluation and Management (DREAM) tool was developed as part of the effort to quantify the risk of geologic storage of carbon dioxide (CO 2) under the U.S. Department of Energy's National Risk Assessment Partnership (NRAP). DREAM is an optimization tool created to identify optimal monitoring schemes that minimize the time to first detection of CO 2 leakage from a subsurface storage formation. DREAM acts as a post-processer on user-provided output from subsurface leakage simulations. While DREAM was developed for CO 2 leakage scenarios, it is applicable to any subsurface leakage simulation of the same output format.more » The DREAM tool is comprised of three main components: (1) a Java wizard used to configure and execute the simulations, (2) a visualization tool to view the domain space and optimization results, and (3) a plotting tool used to analyze the results. A secondary Java application is provided to aid users in converting common American Standard Code for Information Interchange (ASCII) output data to the standard DREAM hierarchical data format (HDF5). DREAM employs a simulated annealing approach that searches the solution space by iteratively mutating potential monitoring schemes built of various configurations of monitoring locations and leak detection parameters. This approach has proven to be orders of magnitude faster than an exhaustive search of the entire solution space. The user's manual illustrates the program graphical user interface (GUI), describes the tool inputs, and includes an example application.« less
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Nandipati, Giridhar; Setyawan, Wahyu; Heinisch, Howard L.
2015-07-01
Object kinetic Monte Carlo (OKMC) simulations have been performed to investigate various aspects of cascade aging in bulk tungsten and to determine the sensitivity of the results to the kinetic parameters. The primary focus is on how the kinetic parameters affect the initial recombination of defects in the first few ns of a simulation. The simulations were carried out using the object kinetic Monte Carlo (OKMC) code KSOME (kinetic simulations of microstructure evolution), using a database of cascades obtained from results of molecular dynamics (MD) simulations at various primary knock-on atom (PKA) energies and directions at temperatures of 300, 1025more » and 2050 K. The OKMC model was parameterized using defect migration barriers and binding energies from ab initio calculations. Results indicate that, due to the disparate mobilities of SIA and vacancy clusters in tungsten, annealing is dominated by SIA migration even at temperatures as high as 2050 K. For 100 keV cascades initiated at 300 K recombination is dominated by annihilation of large defect clusters. But for all other PKA energies and temperatures most of the recombination is due to the migration and rotation of small SIA clusters, while all the large SIA clusters escape the cubic simulation cell. The inverse U-shape behavior exhibited by the annealing efficiency as a function of temperature curve, especially for cascades of large PKA energies, is due to asymmetry in SIA and vacancy clustering assisted by the large difference in mobilities of SIAs and vacancies. This annealing behavior is unaffected by the dimensionality of SIA migration persists over a broad range of relative mobilities of SIAs and vacancies.« less
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NASA Astrophysics Data System (ADS)
Jafari, S.; Hojjati, M. H.
2011-12-01
Rotating disks work mostly at high angular velocity and this results a large centrifugal force and consequently induce large stresses and deformations. Minimizing weight of such disks yields to benefits such as low dead weights and lower costs. This paper aims at finding an optimal disk thickness profile for minimum weight design using the simulated annealing (SA) and particle swarm optimization (PSO) as two modern optimization techniques. In using semi-analytical the radial domain of the disk is divided into some virtual sub-domains as rings where the weight of each rings must be minimized. Inequality constrain equation used in optimization is to make sure that maximum von Mises stress is always less than yielding strength of the material of the disk and rotating disk does not fail. The results show that the minimum weight obtained for all two methods is almost identical. The PSO method gives a profile with slightly less weight (6.9% less than SA) while the implementation of both PSO and SA methods are easy and provide more flexibility compared with classical methods.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Talukder, Srijeeta; Chaudhury, Pinaki, E-mail: pinakc@rediffmail.com; Sen, Shrabani
We propose a strategy of using a stochastic optimization technique, namely, simulated annealing to design optimum laser pulses (both IR and UV) to achieve greater fluxes along the two dissociating channels (O{sup 18} + O{sup 16}O{sup 16} and O{sup 16} + O{sup 16}O{sup 18}) in O{sup 16}O{sup 16}O{sup 18} molecule. We show that the integrated fluxes obtained along the targeted dissociating channel is larger with the optimized pulse than with the unoptimized one. The flux ratios are also more impressive with the optimized pulse than with the unoptimized one. We also look at the evolution contours of the wavefunctions alongmore » the two channels with time after the actions of both the IR and UV pulses and compare the profiles for unoptimized (initial) and optimized fields for better understanding the results that we achieve. We also report the pulse parameters obtained as well as the final shapes they take.« less
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Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.
Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie
2018-06-12
Particle swarm optimization (PSO) is a powerful metaheuristic population-based global optimization algorithm. However, when it is applied to nonseparable objective functions, its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant PSO algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates superior performance across several nonlinear, multimodal benchmark functions compared with the rotation-invariant PSO algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in the ReaxFF- lg reactive force field was carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents better performance compared to a genetic algorithm optimization method in the optimization of the parameters of a ReaxFF- lg correction model. The computational framework is implemented in a stand-alone C++ code that allows the straightforward development of ReaxFF reactive force fields.
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Shape optimization techniques for musical instrument design
NASA Astrophysics Data System (ADS)
Henrique, Luis; Antunes, Jose; Carvalho, Joao S.
2002-11-01
The design of musical instruments is still mostly based on empirical knowledge and costly experimentation. One interesting improvement is the shape optimization of resonating components, given a number of constraints (allowed parameter ranges, shape smoothness, etc.), so that vibrations occur at specified modal frequencies. Each admissible geometrical configuration generates an error between computed eigenfrequencies and the target set. Typically, error surfaces present many local minima, corresponding to suboptimal designs. This difficulty can be overcome using global optimization techniques, such as simulated annealing. However these methods are greedy, concerning the number of function evaluations required. Thus, the computational effort can be unacceptable if complex problems, such as bell optimization, are tackled. Those issues are addressed in this paper, and a method for improving optimization procedures is proposed. Instead of using the local geometric parameters as searched variables, the system geometry is modeled in terms of truncated series of orthogonal space-funcitons, and optimization is performed on their amplitude coefficients. Fourier series and orthogonal polynomials are typical such functions. This technique reduces considerably the number of searched variables, and has a potential for significant computational savings in complex problems. It is illustrated by optimizing the shapes of both current and uncommon marimba bars.
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NASA Astrophysics Data System (ADS)
Byerly, K.; Ohodnicki, P. R.; Moon, S. R.; Leary, A. M.; Keylin, V.; McHenry, M. E.; Simizu, S.; Beddingfield, R.; Yu, Y.; Feichter, G.; Noebe, R.; Bowman, R.; Bhattacharya, S.
2018-04-01
Metal amorphous nanocomposite (MANC) alloys are an emerging class of soft magnetic materials showing promise for a range of inductive components targeted for higher power density and higher efficiency power conversion applications including inductors, transformers, and rotating electrical machinery. Magnetization reversal mechanisms within these alloys are typically determined by composition optimization as well as controlled annealing treatments to generate a nanocomposite structure composed of nanocrystals embedded in an amorphous precursor. Here we demonstrate the concept of spatially varying the permeability within a given component for optimization of performance by using the strain annealing process. The concept is realized experimentally through the smoothing of the flux profile from the inner to outer core radius achieved by a monotonic variation in tension during the strain annealing process. Great potential exists for an extension of this concept to a wide range of other power magnetic components and more complex spatially varying permeability profiles through advances in strain annealing techniques and controls.
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NASA Astrophysics Data System (ADS)
Byerly, K.; Ohodnicki, P. R.; Moon, S. R.; Leary, A. M.; Keylin, V.; McHenry, M. E.; Simizu, S.; Beddingfield, R.; Yu, Y.; Feichter, G.; Noebe, R.; Bowman, R.; Bhattacharya, S.
2018-06-01
Metal amorphous nanocomposite (MANC) alloys are an emerging class of soft magnetic materials showing promise for a range of inductive components targeted for higher power density and higher efficiency power conversion applications including inductors, transformers, and rotating electrical machinery. Magnetization reversal mechanisms within these alloys are typically determined by composition optimization as well as controlled annealing treatments to generate a nanocomposite structure composed of nanocrystals embedded in an amorphous precursor. Here we demonstrate the concept of spatially varying the permeability within a given component for optimization of performance by using the strain annealing process. The concept is realized experimentally through the smoothing of the flux profile from the inner to outer core radius achieved by a monotonic variation in tension during the strain annealing process. Great potential exists for an extension of this concept to a wide range of other power magnetic components and more complex spatially varying permeability profiles through advances in strain annealing techniques and controls.
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Controlling superconductivity in La 2-xSr xCuO 4+δ by ozone and vacuum annealing
Leng, Xiang; Bozovic, Ivan
2014-11-21
In this study we performed a series of ozone and vacuum annealing experiments on epitaxial La 2-xSr xCuO 4+δ thin films. The transition temperature after each annealing step has been measured by the mutual inductance technique. The relationship between the effective doping and the vacuum annealing time has been studied. Short-time ozone annealing at 470 °C oxidizes an underdoped film all the way to the overdoped regime. The subsequent vacuum annealing at 350 °C to 380 °C slowly brings the sample across the optimal doping point back to the undoped, non-superconducting state. Several ozone and vacuum annealing cycles have beenmore » done on the same sample and the effects were found to be repeatable and reversible Vacuum annealing of ozone-loaded LSCO films is a very controllable process, allowing one to tune the doping level of LSCO in small steps across the superconducting dome, which can be used for fundamental physics studies.« less
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Controlling superconductivity in La 2-xSr xCuO 4+δ by ozone and vacuum annealing
DOE Office of Scientific and Technical Information (OSTI.GOV)
Leng, Xiang; Bozovic, Ivan
In this study we performed a series of ozone and vacuum annealing experiments on epitaxial La 2-xSr xCuO 4+δ thin films. The transition temperature after each annealing step has been measured by the mutual inductance technique. The relationship between the effective doping and the vacuum annealing time has been studied. Short-time ozone annealing at 470 °C oxidizes an underdoped film all the way to the overdoped regime. The subsequent vacuum annealing at 350 °C to 380 °C slowly brings the sample across the optimal doping point back to the undoped, non-superconducting state. Several ozone and vacuum annealing cycles have beenmore » done on the same sample and the effects were found to be repeatable and reversible Vacuum annealing of ozone-loaded LSCO films is a very controllable process, allowing one to tune the doping level of LSCO in small steps across the superconducting dome, which can be used for fundamental physics studies.« less
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Rafieerad, A R; Bushroa, A R; Nasiri-Tabrizi, B; Kaboli, S H A; Khanahmadi, S; Amiri, Ahmad; Vadivelu, J; Yusof, F; Basirun, W J; Wasa, K
2017-05-01
Recently, the robust optimization and prediction models have been highly noticed in district of surface engineering and coating techniques to obtain the highest possible output values through least trial and error experiments. Besides, due to necessity of finding the optimum value of dependent variables, the multi-objective metaheuristic models have been proposed to optimize various processes. Herein, oriented mixed oxide nanotubular arrays were grown on Ti-6Al-7Nb (Ti67) implant using physical vapor deposition magnetron sputtering (PVDMS) designed by Taguchi and following electrochemical anodization. The obtained adhesion strength and hardness of Ti67/Nb were modeled by particle swarm optimization (PSO) to predict the outputs performance. According to developed models, multi-objective PSO (MOPSO) run aimed at finding PVDMS inputs to maximize current outputs simultaneously. The provided sputtering parameters were applied as validation experiment and resulted in higher adhesion strength and hardness of interfaced layer with Ti67. The as-deposited Nb layer before and after optimization were anodized in fluoride-base electrolyte for 300min. To crystallize the coatings, the anodically grown mixed oxide TiO 2 -Nb 2 O 5 -Al 2 O 3 nanotubes were annealed at 440°C for 30min. From the FESEM observations, the optimized adhesive Nb interlayer led to further homogeneity of mixed nanotube arrays. As a result of this surface modification, the anodized sample after annealing showed the highest mechanical, tribological, corrosion resistant and in-vitro bioactivity properties, where a thick bone-like apatite layer was formed on the mixed oxide nanotubes surface within 10 days immersion in simulated body fluid (SBF) after applied MOPSO. The novel results of this study can be effective in optimizing a variety of the surface properties of the nanostructured implants. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Direct Immersion Annealing of Block Copolymer Thin Films
NASA Astrophysics Data System (ADS)
Karim, Alamgir
We demonstrate ordering of thin block copolymer (BCP) films via direct immersion annealing (DIA) at enhanced rate leading to stable morphologies. The BCP films are immersed in carefully selected mixtures of good and marginal solvents that can impart enhanced polymer mobility, while inhibiting film dissolution. DIA is compatible with roll-to-roll assembly manufacturing and has distinct advantages over conventional thermal annealing and batch processing solvent-vapor annealing methods. We identify three solvent composition-dependent BCP film ordering regimes in DIA for the weakly interacting polystyrene -poly(methyl methacrylate) (PS -PMMA) system: rapid short range order, optimal long-range order, and a film instability regime. Kinetic studies in the ``optimal long-range order'' processing regime as a function of temperature indicate a significant reduction of activation energy for BCP grain growth compared to oven annealing at conventional temperatures. An attractive feature of DIA is its robustness to ordering other BCP (e.g. PS-P2VP) and PS-PMMA systems exhibiting spherical, lamellar and cylindrical ordering. Inclusion of nanoparticles in these films at high concentrations and fast ordering kinetics study with neutron reflectivity and SANS will be discussed. This is (late) Contributed Talk Abstract for Dillon Medal Symposium at DPOLY - discussed with DPOLY Chair Dvora Perahia.
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Baranowski, M; Kudrawiec, R; Latkowska, M; Syperek, M; Misiewicz, J; Sarmiento, T; Harris, J S
2013-02-13
In this study we apply time resolved photoluminescence and contactless electroreflectance to study the carrier collection efficiency of a GaInNAsSb/GaAs quantum well (QW). We show that the enhancement of photoluminescence from GaInNAsSb quantum wells annealed at different temperatures originates not only from (i) the improvement of the optical quality of the GaInNAsSb material (i.e., removal of point defects, which are the source of nonradiative recombination) but it is also affected by (ii) the improvement of carrier collection by the QW region. The total PL efficiency is the product of these two factors, for which the optimal annealing temperatures are found to be ~700 °C and ~760 °C, respectively, whereas the optimal annealing temperature for the integrated PL intensity is found to be between the two temperatures and equals ~720 °C. We connect the variation of the carrier collection efficiency with the modification of the band bending conditions in the investigated structure due to the Fermi level shift in the GaInNAsSb layer after annealing.
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D'Amours, Michel; Pouliot, Jean; Dagnault, Anne; Verhaegen, Frank; Beaulieu, Luc
2011-12-01
Brachytherapy planning software relies on the Task Group report 43 dosimetry formalism. This formalism, based on a water approximation, neglects various heterogeneous materials present during treatment. Various studies have suggested that these heterogeneities should be taken into account to improve the treatment quality. The present study sought to demonstrate the feasibility of incorporating Monte Carlo (MC) dosimetry within an inverse planning algorithm to improve the dose conformity and increase the treatment quality. The method was based on precalculated dose kernels in full patient geometries, representing the dose distribution of a brachytherapy source at a single dwell position using MC simulations and the Geant4 toolkit. These dose kernels are used by the inverse planning by simulated annealing tool to produce a fast MC-based plan. A test was performed for an interstitial brachytherapy breast treatment using two different high-dose-rate brachytherapy sources: the microSelectron iridium-192 source and the electronic brachytherapy source Axxent operating at 50 kVp. A research version of the inverse planning by simulated annealing algorithm was combined with MC to provide a method to fully account for the heterogeneities in dose optimization, using the MC method. The effect of the water approximation was found to depend on photon energy, with greater dose attenuation for the lower energies of the Axxent source compared with iridium-192. For the latter, an underdosage of 5.1% for the dose received by 90% of the clinical target volume was found. A new method to optimize afterloading brachytherapy plans that uses MC dosimetric information was developed. Including computed tomography-based information in MC dosimetry in the inverse planning process was shown to take into account the full range of scatter and heterogeneity conditions. This led to significant dose differences compared with the Task Group report 43 approach for the Axxent source. Copyright © 2011 Elsevier Inc. All rights reserved.
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NASA Technical Reports Server (NTRS)
Phillips, Dave; Haas, William; Barth, Tim; Benjamin, Perakath; Graul, Michael; Bagatourova, Olga
2005-01-01
Range Process Simulation Tool (RPST) is a computer program that assists managers in rapidly predicting and quantitatively assessing the operational effects of proposed technological additions to, and/or upgrades of, complex facilities and engineering systems such as the Eastern Test Range. Originally designed for application to space transportation systems, RPST is also suitable for assessing effects of proposed changes in industrial facilities and large organizations. RPST follows a model-based approach that includes finite-capacity schedule analysis and discrete-event process simulation. A component-based, scalable, open architecture makes RPST easily and rapidly tailorable for diverse applications. Specific RPST functions include: (1) definition of analysis objectives and performance metrics; (2) selection of process templates from a processtemplate library; (3) configuration of process models for detailed simulation and schedule analysis; (4) design of operations- analysis experiments; (5) schedule and simulation-based process analysis; and (6) optimization of performance by use of genetic algorithms and simulated annealing. The main benefits afforded by RPST are provision of information that can be used to reduce costs of operation and maintenance, and the capability for affordable, accurate, and reliable prediction and exploration of the consequences of many alternative proposed decisions.
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Design and landing dynamic analysis of reusable landing leg for a near-space manned capsule
NASA Astrophysics Data System (ADS)
Yue, Shuai; Nie, Hong; Zhang, Ming; Wei, Xiaohui; Gan, Shengyong
2018-06-01
To improve the landing performance of a near-space manned capsule under various landing conditions, a novel landing system is designed that employs double chamber and single chamber dampers in the primary and auxiliary struts, respectively. A dynamic model of the landing system is established, and the damper parameters are determined by employing the design method. A single-leg drop test with different initial pitch angles is then conducted to compare and validate the simulation model. Based on the validated simulation model, seven critical landing conditions regarding nine crucial landing responses are found by combining the radial basis function (RBF) surrogate model and adaptive simulated annealing (ASA) optimization method. Subsequently, the adaptability of the landing system under critical landing conditions is analyzed. The results show that the simulation effectively results match the test results, which validates the accuracy of the dynamic model. In addition, all of the crucial responses under their corresponding critical landing conditions satisfy the design specifications, demonstrating the feasibility of the landing system.
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Evolutionary squeaky wheel optimization: a new framework for analysis.
Li, Jingpeng; Parkes, Andrew J; Burke, Edmund K
2011-01-01
Squeaky wheel optimization (SWO) is a relatively new metaheuristic that has been shown to be effective for many real-world problems. At each iteration SWO does a complete construction of a solution starting from the empty assignment. Although the construction uses information from previous iterations, the complete rebuilding does mean that SWO is generally effective at diversification but can suffer from a relatively weak intensification. Evolutionary SWO (ESWO) is a recent extension to SWO that is designed to improve the intensification by keeping the good components of solutions and only using SWO to reconstruct other poorer components of the solution. In such algorithms a standard challenge is to understand how the various parameters affect the search process. In order to support the future study of such issues, we propose a formal framework for the analysis of ESWO. The framework is based on Markov chains, and the main novelty arises because ESWO moves through the space of partial assignments. This makes it significantly different from the analyses used in local search (such as simulated annealing) which only move through complete assignments. Generally, the exact details of ESWO will depend on various heuristics; so we focus our approach on a case of ESWO that we call ESWO-II and that has probabilistic as opposed to heuristic selection and construction operators. For ESWO-II, we study a simple problem instance and explicitly compute the stationary distribution probability over the states of the search space. We find interesting properties of the distribution. In particular, we find that the probabilities of states generally, but not always, increase with their fitness. This nonmonotonocity is quite different from the monotonicity expected in algorithms such as simulated annealing.
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Fabrication of nanostructured Al-doped ZnO thin film for methane sensing applications
DOE Office of Scientific and Technical Information (OSTI.GOV)
Shafura, A. K., E-mail: shafura@ymail.com; Azhar, N. E. I.; Uzer, M.
2016-07-06
CH{sub 4} gas sensor was fabricated using spin-coating method of the nanostructured ZnO thin film. Effect of annealing temperature on the electrical and structural properties of the film was investigated. Dense nanostructured ZnO film are obtained at higher annealing temperature. The optimal condition of annealing temperature is 500°C which has conductivity and sensitivity value of 3.3 × 10{sup −3} S/cm and 11.5%, respectively.
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Quantum annealing of the traveling-salesman problem.
Martonák, Roman; Santoro, Giuseppe E; Tosatti, Erio
2004-11-01
We propose a path-integral Monte Carlo quantum annealing scheme for the symmetric traveling-salesman problem, based on a highly constrained Ising-like representation, and we compare its performance against standard thermal simulated annealing. The Monte Carlo moves implemented are standard, and consist in restructuring a tour by exchanging two links (two-opt moves). The quantum annealing scheme, even with a drastically simple form of kinetic energy, appears definitely superior to the classical one, when tested on a 1002-city instance of the standard TSPLIB.
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NASA Astrophysics Data System (ADS)
Ou, Jiemei; Yang, Yuzhao; Lin, Wensheng; Yuan, Zhongke; Gan, Lin; Lin, Xiaofeng; Chen, Xudong; Chen, Yujie
2015-03-01
We investigated the transitions of conformations and their effects on emission properties of poly[2-methoxy-5-(2'-ethyl-hexyloxy)-1,4-phenylene vinylene] (MEH-PPV) single molecules in PMMA matrix during thermal annealing process. Total internal reflection fluorescence microscopy measurements reveal the transformation from collapsed conformations to extended, highly ordered rod-like structures of MEH-PPV single molecules during thermal annealing. The blue shifts in the ensemble single molecule PL spectra support our hypnosis. The transition occurs as the annealing temperature exceeds 100 °C, implying that an annealing temperature near the glass transition temperature Tg of matrix is ideal for the control and optimization of blend polymer films.
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Bassen, David M; Vilkhovoy, Michael; Minot, Mason; Butcher, Jonathan T; Varner, Jeffrey D
2017-01-25
Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open source, available under an MIT license, and can be installed using the Julia package manager from the JuPOETs GitHub repository.
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Quantum-enhanced reinforcement learning for finite-episode games with discrete state spaces
NASA Astrophysics Data System (ADS)
Neukart, Florian; Von Dollen, David; Seidel, Christian; Compostella, Gabriele
2017-12-01
Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum annealing machines produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization and sampling tasks. Here, we present a way to partially embed both Monte Carlo policy iteration for finding an optimal policy on random observations, as well as how to embed n sub-optimal state-value functions for approximating an improved state-value function given a policy for finite horizon games with discrete state spaces on a D-Wave 2000Q quantum processing unit (QPU). We explain how both problems can be expressed as a quadratic unconstrained binary optimization (QUBO) problem, and show that quantum-enhanced Monte Carlo policy evaluation allows for finding equivalent or better state-value functions for a given policy with the same number episodes compared to a purely classical Monte Carlo algorithm. Additionally, we describe a quantum-classical policy learning algorithm. Our first and foremost aim is to explain how to represent and solve parts of these problems with the help of the QPU, and not to prove supremacy over every existing classical policy evaluation algorithm.
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NASA Astrophysics Data System (ADS)
Mirab, Hadi; Fathi, Reza; Jahangiri, Vahid; Ettefagh, Mir Mohammad; Hassannejad, Reza
2015-12-01
One of the new methods for powering low-power electronic devices at sea is a wave energy harvesting system. In this method, piezoelectric material is employed to convert the mechanical energy of sea waves into electrical energy. The advantage of this method is based on avoiding a battery charging system. Studies have been done on energy harvesting from sea waves, however, considering energy harvesting with random JONSWAP wave theory, then determining the optimum values of energy harvested is new. This paper does that by implementing the JONSWAP wave model, calculating produced power, and realistically showing that output power is decreased in comparison with the more simple airy wave model. In addition, parameters of the energy harvester system are optimized using a simulated annealing algorithm, yielding increased produced power.
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Streamflow Prediction based on Chaos Theory
NASA Astrophysics Data System (ADS)
Li, X.; Wang, X.; Babovic, V. M.
2015-12-01
Chaos theory is a popular method in hydrologic time series prediction. Local model (LM) based on this theory utilizes time-delay embedding to reconstruct the phase-space diagram. For this method, its efficacy is dependent on the embedding parameters, i.e. embedding dimension, time lag, and nearest neighbor number. The optimal estimation of these parameters is thus critical to the application of Local model. However, these embedding parameters are conventionally estimated using Average Mutual Information (AMI) and False Nearest Neighbors (FNN) separately. This may leads to local optimization and thus has limitation to its prediction accuracy. Considering about these limitation, this paper applies a local model combined with simulated annealing (SA) to find the global optimization of embedding parameters. It is also compared with another global optimization approach of Genetic Algorithm (GA). These proposed hybrid methods are applied in daily and monthly streamflow time series for examination. The results show that global optimization can contribute to the local model to provide more accurate prediction results compared with local optimization. The LM combined with SA shows more advantages in terms of its computational efficiency. The proposed scheme here can also be applied to other fields such as prediction of hydro-climatic time series, error correction, etc.
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Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing
2015-01-01
In order to recycle and dispose of all people’s expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies. PMID:26184252
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Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing
2015-07-09
In order to recycle and dispose of all people's expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies.
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Quantum annealing correction with minor embedding
NASA Astrophysics Data System (ADS)
Vinci, Walter; Albash, Tameem; Paz-Silva, Gerardo; Hen, Itay; Lidar, Daniel A.
2015-10-01
Quantum annealing provides a promising route for the development of quantum optimization devices, but the usefulness of such devices will be limited in part by the range of implementable problems as dictated by hardware constraints. To overcome constraints imposed by restricted connectivity between qubits, a larger set of interactions can be approximated using minor embedding techniques whereby several physical qubits are used to represent a single logical qubit. However, minor embedding introduces new types of errors due to its approximate nature. We introduce and study quantum annealing correction schemes designed to improve the performance of quantum annealers in conjunction with minor embedding, thus leading to a hybrid scheme defined over an encoded graph. We argue that this scheme can be efficiently decoded using an energy minimization technique provided the density of errors does not exceed the per-site percolation threshold of the encoded graph. We test the hybrid scheme using a D-Wave Two processor on problems for which the encoded graph is a two-level grid and the Ising model is known to be NP-hard. The problems we consider are frustrated Ising model problem instances with "planted" (a priori known) solutions. Applied in conjunction with optimized energy penalties and decoding techniques, we find that this approach enables the quantum annealer to solve minor embedded instances with significantly higher success probability than it would without error correction. Our work demonstrates that quantum annealing correction can and should be used to improve the robustness of quantum annealing not only for natively embeddable problems but also when minor embedding is used to extend the connectivity of physical devices.
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Close packing of rods on spherical surfaces
NASA Astrophysics Data System (ADS)
Smallenburg, Frank; Löwen, Hartmut
2016-04-01
We study the optimal packing of short, hard spherocylinders confined to lie tangential to a spherical surface, using simulated annealing and molecular dynamics simulations. For clusters of up to twelve particles, we map out the changes in the geometry of the closest-packed configuration as a function of the aspect ratio L/D, where L is the cylinder length and D the diameter of the rods. We find a rich variety of cluster structures. For larger clusters, we find that the best-packed configurations up to around 100 particles are highly dependent on the exact number of particles and aspect ratio. For even larger clusters, we find largely disordered clusters for very short rods (L/D = 0.25), while slightly longer rods (L/D = 0.5 or 1) prefer a global baseball-like geometry of smectic-like domains, similar to the behavior of large-scale nematic shells. Intriguingly, we observe that when compared to their optimal flat-plane packing, short rods adapt to the spherical geometry more efficiently than both spheres and longer rods. Our results provide predictions for experimentally realizable systems of colloidal rods trapped at the interface of emulsion droplets.
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An improved immune algorithm for optimizing the pulse width modulation control sequence of inverters
NASA Astrophysics Data System (ADS)
Sheng, L.; Qian, S. Q.; Ye, Y. Q.; Wu, Y. H.
2017-09-01
In this article, an improved immune algorithm (IIA), based on the fundamental principles of the biological immune system, is proposed for optimizing the pulse width modulation (PWM) control sequence of a single-phase full-bridge inverter. The IIA takes advantage of the receptor editing and adaptive mutation mechanisms of the immune system to develop two operations that enhance the population diversity and convergence of the proposed algorithm. To verify the effectiveness and examine the performance of the IIA, 17 cases are considered, including fixed and disturbed resistances. Simulation results show that the IIA is able to obtain an effective PWM control sequence. Furthermore, when compared with existing immune algorithms (IAs), genetic algorithms (GAs), a non-traditional GA, simplified simulated annealing, and a generalized Hopfield neural network method, the IIA can achieve small total harmonic distortion (THD) and large magnitude. Meanwhile, a non-parametric test indicates that the IIA is significantly better than most comparison algorithms. Supplemental data for this article can be accessed at http://dx.doi.org/10.1080/0305215X.2016.1250894.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Heinisch, H.L.
1997-04-01
The intracascade evolution of the defect distributions of cascades in copper is investigated using stochastic annealing simulations applied to cascades generated with molecular dynamics (MD). The temperature and energy dependencies of annihilation, clustering and free defect production are determined for individual cascades. The annealing simulation results illustrate the strong influence on intracascade evolution of the defect configuration existing in the primary damage state. Another factor significantly affecting the evolution of the defect distribution is the rapid one-dimensional diffusion of small, glissile interstitial loops produced directly in cascades. This phenomenon introduces a cascade energy dependence of defect evolution that is apparentmore » only beyond the primary damage state, amplifying the need for further study of the annealing phase of cascade evolution and for performing many more MD cascade simulations at higher energies.« less
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NASA Astrophysics Data System (ADS)
Kaliszewski, M.; Mazuro, P.
2016-09-01
Simulated Annealing Method of optimisation for the sealing piston ring geometry is tested. The aim of optimisation is to develop ring geometry which would exert demanded pressure on a cylinder just while being bended to fit the cylinder. Method of FEM analysis of an arbitrary piston ring geometry is applied in an ANSYS software. The demanded pressure function (basing on formulae presented by A. Iskra) as well as objective function are introduced. Geometry definition constructed by polynomials in radial coordinate system is delivered and discussed. Possible application of Simulated Annealing Method in a piston ring optimisation task is proposed and visualised. Difficulties leading to possible lack of convergence of optimisation are presented. An example of an unsuccessful optimisation performed in APDL is discussed. Possible line of further optimisation improvement is proposed.
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Post deposition annealing effect on the properties of Al2O3/InP interface
NASA Astrophysics Data System (ADS)
Kim, Hogyoung; Kim, Dong Ha; Choi, Byung Joon
2018-02-01
Post deposition in-situ annealing effect on the interfacial and electrical properties of Au/Al2O3/n-InP junctions were investigated. With increasing the annealing time, both the barrier height and ideality factor changed slightly but the series resistance decreased significantly. Photoluminescence (PL) measurements showed that the intensities of both the near band edge (NBE) emission from InP and defect-related bands (DBs) from Al2O3 decreased with 30 min annealing. With increasing the annealing time, the diffusion of oxygen (indium) atoms into Al2O3/InP interface (into Al2O3 layer) occurred more significantly, giving rise to the increase of the interface state density. Therefore, the out-diffusion of oxygen atoms from Al2O3 during the annealing process should be controlled carefully to optimize the Al2O3/InP based devices.
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Bifurcation-based adiabatic quantum computation with a nonlinear oscillator network
Goto, Hayato
2016-01-01
The dynamics of nonlinear systems qualitatively change depending on their parameters, which is called bifurcation. A quantum-mechanical nonlinear oscillator can yield a quantum superposition of two oscillation states, known as a Schrödinger cat state, via quantum adiabatic evolution through its bifurcation point. Here we propose a quantum computer comprising such quantum nonlinear oscillators, instead of quantum bits, to solve hard combinatorial optimization problems. The nonlinear oscillator network finds optimal solutions via quantum adiabatic evolution, where nonlinear terms are increased slowly, in contrast to conventional adiabatic quantum computation or quantum annealing, where quantum fluctuation terms are decreased slowly. As a result of numerical simulations, it is concluded that quantum superposition and quantum fluctuation work effectively to find optimal solutions. It is also notable that the present computer is analogous to neural computers, which are also networks of nonlinear components. Thus, the present scheme will open new possibilities for quantum computation, nonlinear science, and artificial intelligence. PMID:26899997
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Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods †
Gonzalez-Navarro, Felix F.; Stilianova-Stoytcheva, Margarita; Renteria-Gutierrez, Livier; Belanche-Muñoz, Lluís A.; Flores-Rios, Brenda L.; Ibarra-Esquer, Jorge E.
2016-01-01
Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization. PMID:27792165
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Automatic discovery of optimal classes
NASA Technical Reports Server (NTRS)
Cheeseman, Peter; Stutz, John; Freeman, Don; Self, Matthew
1986-01-01
A criterion, based on Bayes' theorem, is described that defines the optimal set of classes (a classification) for a given set of examples. This criterion is transformed into an equivalent minimum message length criterion with an intuitive information interpretation. This criterion does not require that the number of classes be specified in advance, this is determined by the data. The minimum message length criterion includes the message length required to describe the classes, so there is a built in bias against adding new classes unless they lead to a reduction in the message length required to describe the data. Unfortunately, the search space of possible classifications is too large to search exhaustively, so heuristic search methods, such as simulated annealing, are applied. Tutored learning and probabilistic prediction in particular cases are an important indirect result of optimal class discovery. Extensions to the basic class induction program include the ability to combine category and real value data, hierarchical classes, independent classifications and deciding for each class which attributes are relevant.
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Study on probability distributions for evolution in modified extremal optimization
NASA Astrophysics Data System (ADS)
Zeng, Guo-Qiang; Lu, Yong-Zai; Mao, Wei-Jie; Chu, Jian
2010-05-01
It is widely believed that the power-law is a proper probability distribution being effectively applied for evolution in τ-EO (extremal optimization), a general-purpose stochastic local-search approach inspired by self-organized criticality, and its applications in some NP-hard problems, e.g., graph partitioning, graph coloring, spin glass, etc. In this study, we discover that the exponential distributions or hybrid ones (e.g., power-laws with exponential cutoff) being popularly used in the research of network sciences may replace the original power-laws in a modified τ-EO method called self-organized algorithm (SOA), and provide better performances than other statistical physics oriented methods, such as simulated annealing, τ-EO and SOA etc., from the experimental results on random Euclidean traveling salesman problems (TSP) and non-uniform instances. From the perspective of optimization, our results appear to demonstrate that the power-law is not the only proper probability distribution for evolution in EO-similar methods at least for TSP, the exponential and hybrid distributions may be other choices.
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Adaptive photoacoustic imaging quality optimization with EMD and reconstruction
NASA Astrophysics Data System (ADS)
Guo, Chengwen; Ding, Yao; Yuan, Jie; Xu, Guan; Wang, Xueding; Carson, Paul L.
2016-10-01
Biomedical photoacoustic (PA) signal is characterized with extremely low signal to noise ratio which will yield significant artifacts in photoacoustic tomography (PAT) images. Since PA signals acquired by ultrasound transducers are non-linear and non-stationary, traditional data analysis methods such as Fourier and wavelet method cannot give useful information for further research. In this paper, we introduce an adaptive method to improve the quality of PA imaging based on empirical mode decomposition (EMD) and reconstruction. Data acquired by ultrasound transducers are adaptively decomposed into several intrinsic mode functions (IMFs) after a sifting pre-process. Since noise is randomly distributed in different IMFs, depressing IMFs with more noise while enhancing IMFs with less noise can effectively enhance the quality of reconstructed PAT images. However, searching optimal parameters by means of brute force searching algorithms will cost too much time, which prevent this method from practical use. To find parameters within reasonable time, heuristic algorithms, which are designed for finding good solutions more efficiently when traditional methods are too slow, are adopted in our method. Two of the heuristic algorithms, Simulated Annealing Algorithm, a probabilistic method to approximate the global optimal solution, and Artificial Bee Colony Algorithm, an optimization method inspired by the foraging behavior of bee swarm, are selected to search optimal parameters of IMFs in this paper. The effectiveness of our proposed method is proved both on simulated data and PA signals from real biomedical tissue, which might bear the potential for future clinical PA imaging de-noising.
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A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems
Zhang, Le; Wu, Jinnan
2014-01-01
This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature. PMID:24672389
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A PSO-based hybrid metaheuristic for permutation flowshop scheduling problems.
Zhang, Le; Wu, Jinnan
2014-01-01
This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.
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High-NA metrology and sensing on Berkeley MET5
NASA Astrophysics Data System (ADS)
Miyakawa, Ryan; Anderson, Chris; Naulleau, Patrick
2017-03-01
In this paper we compare two non-interferometric wavefront sensors suitable for in-situ high-NA EUV optical testing. The first is the AIS sensor, which has been deployed in both inspection and exposure tools. AIS is a compact, optical test that directly measures a wavefront by probing various parts of the imaging optic pupil and measuring localized wavefront curvature. The second is an image-based technique that uses an iterative algorithm based on simulated annealing to reconstruct a wavefront based on matching aerial images through focus. In this technique, customized illumination is used to probe the pupil at specific points to optimize differences in aberration signatures.
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Design of stapled DNA-minor-groove-binding molecules with a mutable atom simulated annealing method
NASA Astrophysics Data System (ADS)
Walker, Wynn L.; Kopka, Mary L.; Dickerson, Richard E.; Goodsell, David S.
1997-11-01
We report the design of optimal linker geometries for the synthesis of stapledDNA-minor-groove-binding molecules. Netropsin, distamycin, and lexitropsinsbind side-by-side to mixed-sequence DNA and offer an opportunity for thedesign of sequence-reading molecules. Stapled molecules, with two moleculescovalently linked side-by-side, provide entropic gains and restrain theposition of one molecule relative to its neighbor. Using a free-atom simulatedannealing technique combined with a discrete mutable atom definition, optimallengths and atomic composition for covalent linkages are determined, and anovel hydrogen bond `zipper' is proposed to phase two molecules accuratelyside-by-side.
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NASA Astrophysics Data System (ADS)
Moghaddam, Kamran S.; Usher, John S.
2011-07-01
In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.
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NASA Astrophysics Data System (ADS)
Lagos, Soledad R.; Velis, Danilo R.
2018-02-01
We perform the location of microseismic events generated in hydraulic fracturing monitoring scenarios using two global optimization techniques: Very Fast Simulated Annealing (VFSA) and Particle Swarm Optimization (PSO), and compare them against the classical grid search (GS). To this end, we present an integrated and optimized workflow that concatenates into an automated bash script the different steps that lead to the microseismic events location from raw 3C data. First, we carry out the automatic detection, denoising and identification of the P- and S-waves. Secondly, we estimate their corresponding backazimuths using polarization information, and propose a simple energy-based criterion to automatically decide which is the most reliable estimate. Finally, after taking proper care of the size of the search space using the backazimuth information, we perform the location using the aforementioned algorithms for 2D and 3D usual scenarios of hydraulic fracturing processes. We assess the impact of restricting the search space and show the advantages of using either VFSA or PSO over GS to attain significant speed-ups.
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Energy minimization in medical image analysis: Methodologies and applications.
Zhao, Feng; Xie, Xianghua
2016-02-01
Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two categories: continuous method and discrete method. The former includes Newton-Raphson method, gradient descent method, conjugate gradient method, proximal gradient method, coordinate descent method, and genetic algorithm-based method, while the latter covers graph cuts method, belief propagation method, tree-reweighted message passing method, linear programming method, maximum margin learning method, simulated annealing method, and iterated conditional modes method. We also discuss the minimal surface method, primal-dual method, and the multi-objective optimization method. In addition, we review several comparative studies that evaluate the performance of different minimization techniques in terms of accuracy, efficiency, or complexity. These optimization techniques are widely used in many medical applications, for example, image segmentation, registration, reconstruction, motion tracking, and compressed sensing. We thus give an overview on those applications as well. Copyright © 2015 John Wiley & Sons, Ltd.
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Engine performance analysis and optimization of a dual-mode scramjet with varied inlet conditions
NASA Astrophysics Data System (ADS)
Tian, Lu; Chen, Li-Hong; Chen, Qiang; Zhong, Feng-Quan; Chang, Xin-Yu
2016-02-01
A dual-mode scramjet can operate in a wide range of flight conditions. Higher thrust can be generated by adopting suitable combustion modes. Based on the net thrust, an analysis and preliminary optimal design of a kerosene-fueled parameterized dual-mode scramjet at a crucial flight Mach number of 6 were investigated by using a modified quasi-one-dimensional method and simulated annealing strategy. Engine structure and heat release distributions, affecting the engine thrust, were chosen as analytical parameters for varied inlet conditions (isolator entrance Mach number: 1.5-3.5). Results show that different optimal heat release distributions and structural conditions can be obtained at five different inlet conditions. The highest net thrust of the parameterized dual-mode engine can be achieved by a subsonic combustion mode at an isolator entrance Mach number of 2.5. Additionally, the effects of heat release and scramjet structure on net thrust have been discussed. The present results and the developed analytical method can provide guidance for the design and optimization of high-performance dual-mode scramjets.
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Heuristics for Multiobjective Optimization of Two-Sided Assembly Line Systems
Jawahar, N.; Ponnambalam, S. G.; Sivakumar, K.; Thangadurai, V.
2014-01-01
Products such as cars, trucks, and heavy machinery are assembled by two-sided assembly line. Assembly line balancing has significant impacts on the performance and productivity of flow line manufacturing systems and is an active research area for several decades. This paper addresses the line balancing problem of a two-sided assembly line in which the tasks are to be assigned at L side or R side or any one side (addressed as E). Two objectives, minimum number of workstations and minimum unbalance time among workstations, have been considered for balancing the assembly line. There are two approaches to solve multiobjective optimization problem: first approach combines all the objectives into a single composite function or moves all but one objective to the constraint set; second approach determines the Pareto optimal solution set. This paper proposes two heuristics to evolve optimal Pareto front for the TALBP under consideration: Enumerative Heuristic Algorithm (EHA) to handle problems of small and medium size and Simulated Annealing Algorithm (SAA) for large-sized problems. The proposed approaches are illustrated with example problems and their performances are compared with a set of test problems. PMID:24790568
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2013-01-01
We report on efficient ZnO nanocrystal (ZnO-NC) emission in the near-UV region. We show that luminescence from ZnO nanocrystals embedded in a SiO2 matrix can vary significantly as a function of the annealing temperature from 450°C to 700°C. We manage to correlate the emission of the ZnO nanocrystals embedded in SiO2 thin films with transmission electron microscopy images in order to optimize the fabrication process. Emission can be explained using two main contributions, near-band-edge emission (UV range) and defect-related emissions (visible). Both contributions over 500°C are found to be size dependent in intensity due to a decrease of the absorption cross section. For the smallest-size nanocrystals, UV emission can only be accounted for using a blueshifted UV contribution as compared to the ZnO band gap. In order to further optimize the emission properties, we have studied different annealing atmospheres under oxygen and under argon gas. We conclude that a softer annealing temperature at 450°C but with longer annealing time under oxygen is the most preferable scenario in order to improve near-UV emission of the ZnO nanocrystals embedded in an SiO2 matrix. PMID:24314071
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Pita, Kantisara; Baudin, Pierre; Vu, Quang Vinh; Aad, Roy; Couteau, Christophe; Lérondel, Gilles
2013-12-06
We report on efficient ZnO nanocrystal (ZnO-NC) emission in the near-UV region. We show that luminescence from ZnO nanocrystals embedded in a SiO2 matrix can vary significantly as a function of the annealing temperature from 450°C to 700°C. We manage to correlate the emission of the ZnO nanocrystals embedded in SiO2 thin films with transmission electron microscopy images in order to optimize the fabrication process. Emission can be explained using two main contributions, near-band-edge emission (UV range) and defect-related emissions (visible). Both contributions over 500°C are found to be size dependent in intensity due to a decrease of the absorption cross section. For the smallest-size nanocrystals, UV emission can only be accounted for using a blueshifted UV contribution as compared to the ZnO band gap. In order to further optimize the emission properties, we have studied different annealing atmospheres under oxygen and under argon gas. We conclude that a softer annealing temperature at 450°C but with longer annealing time under oxygen is the most preferable scenario in order to improve near-UV emission of the ZnO nanocrystals embedded in an SiO2 matrix.
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2012-05-30
annealing-based or Bayesian sequential simulation approaches B. Dafflon1,2 and W. Barrash1 Received 13 May 2011; revised 12 March 2012; accepted 17 April 2012...the withheld porosity log are also withheld for this estimation process. For both cases we do this for two wells having locally variable stratigraphy ...borehole location is given at the bottom of each log comparison panel. For comparison with stratigraphy at the BHRS, contacts between Units 1 to 4
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Zou, Liwei; Qian, Yun; Zhou, Tianjun
2014-10-01
In this study, we calibrated the performance of regional climate model RegCM3 with Massachusetts Institute of Technology (MIT)-Emanuel cumulus parameterization scheme over CORDEX East Asia domain by tuning the selected seven parameters through multiple very fast simulated annealing (MVFSA) sampling method. The seven parameters were selected based on previous studies, which customized the RegCM3 with MIT-Emanuel scheme through three different ways by using the sensitivity experiments. The responses of model results to the seven parameters were investigated. Since the monthly total rainfall is constrained, the simulated spatial pattern of rainfall and the probability density function (PDF) distribution of daily rainfallmore » rates are significantly improved in the optimal simulation. Sensitivity analysis suggest that the parameter “relative humidity criteria” (RH), which has not been considered in the default simulation, has the largest effect on the model results. The responses of total rainfall over different regions to RH were examined. Positive responses of total rainfall to RH are found over northern equatorial western Pacific, which are contributed by the positive responses of explicit rainfall. Followed by an increase of RH, the increases of the low-level convergence and the associated increases in cloud water favor the increase of the explicit rainfall. The identified optimal parameters constrained by the total rainfall have positive effects on the low-level circulation and the surface air temperature. Furthermore, the optimized parameters based on the extreme case are suitable for a normal case and the model’s new version with mixed convection scheme.« less
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Composition dependent thermal annealing behaviour of ion tracks in apatite
NASA Astrophysics Data System (ADS)
Nadzri, A.; Schauries, D.; Mota-Santiago, P.; Muradoglu, S.; Trautmann, C.; Gleadow, A. J. W.; Hawley, A.; Kluth, P.
2016-07-01
Natural apatite samples with different F/Cl content from a variety of geological locations (Durango, Mexico; Mud Tank, Australia; and Snarum, Norway) were irradiated with swift heavy ions to simulate fission tracks. The annealing kinetics of the resulting ion tracks was investigated using synchrotron-based small-angle X-ray scattering (SAXS) combined with ex situ annealing. The activation energies for track recrystallization were extracted and consistent with previous studies using track-etching, tracks in the chlorine-rich Snarum apatite are more resistant to annealing than in the other compositions.
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Trajectory optimization for the National Aerospace Plane
NASA Technical Reports Server (NTRS)
Lu, Ping
1993-01-01
The objective of this second phase research is to investigate the optimal ascent trajectory for the National Aerospace Plane (NASP) from runway take-off to orbital insertion and address the unique problems associated with the hypersonic flight trajectory optimization. The trajectory optimization problem for an aerospace plane is a highly challenging problem because of the complexity involved. Previous work has been successful in obtaining sub-optimal trajectories by using energy-state approximation and time-scale decomposition techniques. But it is known that the energy-state approximation is not valid in certain portions of the trajectory. This research aims at employing full dynamics of the aerospace plane and emphasizing direct trajectory optimization methods. The major accomplishments of this research include the first-time development of an inverse dynamics approach in trajectory optimization which enables us to generate optimal trajectories for the aerospace plane efficiently and reliably, and general analytical solutions to constrained hypersonic trajectories that has wide application in trajectory optimization as well as in guidance and flight dynamics. Optimal trajectories in abort landing and ascent augmented with rocket propulsion and thrust vectoring control were also investigated. Motivated by this study, a new global trajectory optimization tool using continuous simulated annealing and a nonlinear predictive feedback guidance law have been under investigation and some promising results have been obtained, which may well lead to more significant development and application in the near future.
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Liu, Datong; Peng, Yu; Peng, Xiyuan
2018-01-01
Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR) and relevance vector machine (RVM)) are especially adaptable to perform anomaly detection for sensing series. Generally, one key parameter of prediction models is coverage probability (CP), which controls the judging threshold of the testing sample and is generally set to a default value (e.g., 90% or 95%). There are few criteria to determine the optimal CP for anomaly detection. Therefore, this paper designs a graphic indicator of the receiver operating characteristic curve of prediction interval (ROC-PI) based on the definition of the ROC curve which can depict the trade-off between the PI width and PI coverage probability across a series of cut-off points. Furthermore, the Youden index is modified to assess the performance of different CPs, by the minimization of which the optimal CP is derived by the simulated annealing (SA) algorithm. Experiments conducted on two simulation datasets demonstrate the validity of the proposed method. Especially, an actual case study on sensing series from an on-orbit satellite illustrates its significant performance in practical application. PMID:29587372
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Optimized Gen-II FeCrAl cladding production in large quantity for campaign testing
DOE Office of Scientific and Technical Information (OSTI.GOV)
Yamamoto, Yukinori; Sun, Zhiqian; Pint, Bruce A.
2016-06-03
There are two major objectives in this report; (1) to optimize microstructure control of ATF FeCrAl alloys during tube drawing processes, and (2) to provide an update on the progress of ATF FeCrAl tube production via commercial manufacturers. Experimental efforts have been made to optimize the process parameters balancing the tube fabricability, especially for tube drawing processes, and microstructure control of the final tube products. Lab-scale sheet materials of Gen II FeCrAl alloys (Mo-containing and Nb-containing FeCrAl alloys) were used in the study, combined with a stepwise warm-rolling process and intermediate annealing, aiming to simulate the tube drawing process inmore » a commercial tube manufacturer. The intermediate annealing at 650ºC for 1h was suggested for the tube-drawing process of Mo-containing FeCrAl alloys because it successfully softened the material by recovering the work hardening introduced through the rolling step, without inducing grain coarsening due to recrystallization. The final tube product is expected to have stabilized deformed microstructure providing the improved tensile properties with sufficient ductility. Optimization efforts on Nb-containing FeCrAl alloys focused on the effect of alloying additions and annealing conditions on the stability of deformed microstructure. Relationships between the second-phase precipitates (Fe 2Nb-Laves phase) and microstructure stability are discussed. FeCrAl tube production through commercial tube manufacturers is currently in progress. Three different manufacturers, Century Tubes, Inc. (CTI), Rhenium Alloys, Inc. (RAI), and Superior Tube Company, Inc. (STC), are providing capabilities for cold-drawing, warm-drawing, and HPTR cold-pilgering, respectively. The first two companies are currently working on large quantity tube production (expected 250 ft length) of Gen I model FeCrAl alloy (B136Y3, at CTI) and Gen II (C35M4, at RAI), with the process parameters obtained from the experimental efforts. The expected delivery dates are at the end of July, 2016, and the middle of June, 2016, respectively. Tube production at STC would be the first attempt to apply cold-pilgering to the FeCrAl alloys. Communication has been initiated, and the materials have been machined for the cold-pilgering process.« less
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Hybrid optimization and Bayesian inference techniques for a non-smooth radiation detection problem
Stefanescu, Razvan; Schmidt, Kathleen; Hite, Jason; ...
2016-12-12
In this paper, we propose several algorithms to recover the location and intensity of a radiation source located in a simulated 250 × 180 m block of an urban center based on synthetic measurements. Radioactive decay and detection are Poisson random processes, so we employ likelihood functions based on this distribution. Owing to the domain geometry and the proposed response model, the negative logarithm of the likelihood is only piecewise continuous differentiable, and it has multiple local minima. To address these difficulties, we investigate three hybrid algorithms composed of mixed optimization techniques. For global optimization, we consider simulated annealing, particlemore » swarm, and genetic algorithm, which rely solely on objective function evaluations; that is, they do not evaluate the gradient in the objective function. By employing early stopping criteria for the global optimization methods, a pseudo-optimum point is obtained. This is subsequently utilized as the initial value by the deterministic implicit filtering method, which is able to find local extrema in non-smooth functions, to finish the search in a narrow domain. These new hybrid techniques, combining global optimization and implicit filtering address, difficulties associated with the non-smooth response, and their performances, are shown to significantly decrease the computational time over the global optimization methods. To quantify uncertainties associated with the source location and intensity, we employ the delayed rejection adaptive Metropolis and DiffeRential Evolution Adaptive Metropolis algorithms. Finally, marginal densities of the source properties are obtained, and the means of the chains compare accurately with the estimates produced by the hybrid algorithms.« less
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Preparation and magnetic properties of multiferroic CuMnO2 nanoparticles.
Kurokawa, Akinobu; Yanoh, Tkuya; Yano, Shinya; Ichiyanagi, Yuko
2014-03-01
CuMnO2 nanoparticles with diameters of 64 nm were synthesized by a novel wet chemical method. An optimized two-step annealing method was developed through the analysis of thermogravimetric differential thermal analysis (TG-DTA) measurements in order to obtain single-phase CuMnO2. A sharp exothermic peak was observed in the DTA curve at approximately 500 K where structural changes of the copper oxides and manganese oxides in the precursor are expected to occur. It is believed that Cu+ ions were oxidized to Cu2+ ions and that Mn2+ ions were oxidized to Mn3+ ions in the Cu-Mn-O system. Deoxidization reactions were also found at approximately 1200 K. The optimized annealing temperature for the first step was determined to be 623 K in air. The optimized annealing temperature for the second step was 1173 K in an Ar atmosphere. Magnetization measurements suggested an antiferromagnetic spin ordering at approximately 50 K. It was expected that Mn3+ spin interactions induced magnetic phase transition affected by definite temperature.
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NASA Astrophysics Data System (ADS)
Nkuissi Tchognia, Joël Hervé; Hartiti, Bouchaib; Ridah, Abderraouf; Ndjaka, Jean-Marie; Thevenin, Philippe
2016-07-01
Present research deals with the optimal deposition parameters configuration for the synthesis of Cu2ZnSnS4 (CZTS) thin films using the sol-gel method associated to spin coating on ordinary glass substrates without sulfurization. The Taguchi design with a L9 (34) orthogonal array, a signal-to-noise (S/N) ratio and an analysis of variance (ANOVA) are used to optimize the performance characteristic (optical band gap) of CZTS thin films. Four deposition parameters called factors namely the annealing temperature, the annealing time, the ratios Cu/(Zn + Sn) and Zn/Sn were chosen. To conduct the tests using the Taguchi method, three levels were chosen for each factor. The effects of the deposition parameters on structural and optical properties are studied. The determination of the most significant factors of the deposition process on optical properties of as-prepared films is also done. The results showed that the significant parameters are Zn/Sn ratio and the annealing temperature by applying the Taguchi method.
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A quantum annealing approach for fault detection and diagnosis of graph-based systems
NASA Astrophysics Data System (ADS)
Perdomo-Ortiz, A.; Fluegemann, J.; Narasimhan, S.; Biswas, R.; Smelyanskiy, V. N.
2015-02-01
Diagnosing the minimal set of faults capable of explaining a set of given observations, e.g., from sensor readouts, is a hard combinatorial optimization problem usually tackled with artificial intelligence techniques. We present the mapping of this combinatorial problem to quadratic unconstrained binary optimization (QUBO), and the experimental results of instances embedded onto a quantum annealing device with 509 quantum bits. Besides being the first time a quantum approach has been proposed for problems in the advanced diagnostics community, to the best of our knowledge this work is also the first research utilizing the route Problem → QUBO → Direct embedding into quantum hardware, where we are able to implement and tackle problem instances with sizes that go beyond previously reported toy-model proof-of-principle quantum annealing implementations; this is a significant leap in the solution of problems via direct-embedding adiabatic quantum optimization. We discuss some of the programmability challenges in the current generation of the quantum device as well as a few possible ways to extend this work to more complex arbitrary network graphs.
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NASA Astrophysics Data System (ADS)
Salem, Mohamed Shaker; Ibrahim, Shaimaa Moustafa; Amin, Mohamed
2017-07-01
A novel silicon-based optical microcavity composed of a defect layer sandwiched between two parallel rugate mirrors is created by the electrochemical anodization of silicon in a hydrofluoric acid-based electrolyte using a precisely controlled current density profile. The profile consists of two sinusoidally modulated current waveforms separated by a fixed current that is applied to produce a defect layer between the mirrors. The spectral response of the rugate-based microcavity is simulated using the transfer matrix method and compared to the conventional Bragg-based microcavity. It is found that the resonance position of both microcavities is unchanged. However, the rugate-based microcavity exhibits a distinct reduction of the sidebands' intensity. Further attenuation of the sidebands' intensity is obtained by creating refractive index matching layers with optimized thickness at the bottom and top of the rugate-based microcavity. In order to stabilize the produced microcavity against natural oxidation, atomic layer deposition of an ultra-thin titanium dioxide layer on the pore wall is carried out followed by thermal annealing. The microcavity resonance position shows an observable sensitivity to the deposition and annealing processes.
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Improved mapping of the travelling salesman problem for quantum annealing
NASA Astrophysics Data System (ADS)
Troyer, Matthias; Heim, Bettina; Brown, Ethan; Wecker, David
2015-03-01
We consider the quantum adiabatic algorithm as applied to the travelling salesman problem (TSP). We introduce a novel mapping of TSP to an Ising spin glass Hamiltonian and compare it to previous known mappings. Through direct perturbative analysis, unitary evolution, and simulated quantum annealing, we show this new mapping to be significantly superior. We discuss how this advantage can translate to actual physical implementations of TSP on quantum annealers.
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Harmony Search as a Powerful Tool for Feature Selection in QSPR Study of the Drugs Lipophilicity.
Bahadori, Behnoosh; Atabati, Morteza
2017-01-01
Aims & Scope: Lipophilicity represents one of the most studied and most frequently used fundamental physicochemical properties. In the present work, harmony search (HS) algorithm is suggested to feature selection in quantitative structure-property relationship (QSPR) modeling to predict lipophilicity of neutral, acidic, basic and amphotheric drugs that were determined by UHPLC. Harmony search is a music-based metaheuristic optimization algorithm. It was affected by the observation that the aim of music is to search for a perfect state of harmony. Semi-empirical quantum-chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied molecules and variant descriptors (1497 descriptors) were calculated by the Dragon software. The selected descriptors by harmony search algorithm (9 descriptors) were applied for model development using multiple linear regression (MLR). In comparison with other feature selection methods such as genetic algorithm and simulated annealing, harmony search algorithm has better results. The root mean square error (RMSE) with and without leave-one out cross validation (LOOCV) were obtained 0.417 and 0.302, respectively. The results were compared with those obtained from the genetic algorithm and simulated annealing methods and it showed that the HS is a helpful tool for feature selection with fine performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Finding a Hadamard matrix by simulated annealing of spin vectors
NASA Astrophysics Data System (ADS)
Bayu Suksmono, Andriyan
2017-05-01
Reformulation of a combinatorial problem into optimization of a statistical-mechanics system enables finding a better solution using heuristics derived from a physical process, such as by the simulated annealing (SA). In this paper, we present a Hadamard matrix (H-matrix) searching method based on the SA on an Ising model. By equivalence, an H-matrix can be converted into a seminormalized Hadamard (SH) matrix, whose first column is unit vector and the rest ones are vectors with equal number of -1 and +1 called SH-vectors. We define SH spin vectors as representation of the SH vectors, which play a similar role as the spins on Ising model. The topology of the lattice is generalized into a graph, whose edges represent orthogonality relationship among the SH spin vectors. Starting from a randomly generated quasi H-matrix Q, which is a matrix similar to the SH-matrix without imposing orthogonality, we perform the SA. The transitions of Q are conducted by random exchange of {+, -} spin-pair within the SH-spin vectors that follow the Metropolis update rule. Upon transition toward zeroth energy, the Q-matrix is evolved following a Markov chain toward an orthogonal matrix, at which the H-matrix is said to be found. We demonstrate the capability of the proposed method to find some low-order H-matrices, including the ones that cannot trivially be constructed by the Sylvester method.
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A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.
Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing
2018-01-15
Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.
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OBJECT KINETIC MONTE CARLO SIMULATIONS OF CASCADE ANNEALING IN TUNGSTEN
DOE Office of Scientific and Technical Information (OSTI.GOV)
Nandipati, Giridhar; Setyawan, Wahyu; Heinisch, Howard L.
2014-03-31
The objective of this work is to study the annealing of primary cascade damage created by primary knock-on atoms (PKAs) of various energies, at various temperatures in bulk tungsten using the object kinetic Monte Carlo (OKMC) method.
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NASA Astrophysics Data System (ADS)
Wrożyna, Andrzej; Pernach, Monika; Kuziak, Roman; Pietrzyk, Maciej
2016-04-01
Due to their exceptional strength properties combined with good workability the Advanced High-Strength Steels (AHSS) are commonly used in automotive industry. Manufacturing of these steels is a complex process which requires precise control of technological parameters during thermo-mechanical treatment. Design of these processes can be significantly improved by the numerical models of phase transformations. Evaluation of predictive capabilities of models, as far as their applicability in simulation of thermal cycles thermal cycles for AHSS is considered, was the objective of the paper. Two models were considered. The former was upgrade of the JMAK equation while the latter was an upgrade of the Leblond model. The models can be applied to any AHSS though the examples quoted in the paper refer to the Dual Phase (DP) steel. Three series of experimental simulations were performed. The first included various thermal cycles going beyond limitations of the continuous annealing lines. The objective was to validate models behavior in more complex cooling conditions. The second set of tests included experimental simulations of the thermal cycle characteristic for the continuous annealing lines. Capability of the models to describe properly phase transformations in this process was evaluated. The third set included data from the industrial continuous annealing line. Validation and verification of models confirmed their good predictive capabilities. Since it does not require application of the additivity rule, the upgrade of the Leblond model was selected as the better one for simulation of industrial processes in AHSS production.
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Enhanced sampling techniques in molecular dynamics simulations of biological systems.
Bernardi, Rafael C; Melo, Marcelo C R; Schulten, Klaus
2015-05-01
Molecular dynamics has emerged as an important research methodology covering systems to the level of millions of atoms. However, insufficient sampling often limits its application. The limitation is due to rough energy landscapes, with many local minima separated by high-energy barriers, which govern the biomolecular motion. In the past few decades methods have been developed that address the sampling problem, such as replica-exchange molecular dynamics, metadynamics and simulated annealing. Here we present an overview over theses sampling methods in an attempt to shed light on which should be selected depending on the type of system property studied. Enhanced sampling methods have been employed for a broad range of biological systems and the choice of a suitable method is connected to biological and physical characteristics of the system, in particular system size. While metadynamics and replica-exchange molecular dynamics are the most adopted sampling methods to study biomolecular dynamics, simulated annealing is well suited to characterize very flexible systems. The use of annealing methods for a long time was restricted to simulation of small proteins; however, a variant of the method, generalized simulated annealing, can be employed at a relatively low computational cost to large macromolecular complexes. Molecular dynamics trajectories frequently do not reach all relevant conformational substates, for example those connected with biological function, a problem that can be addressed by employing enhanced sampling algorithms. This article is part of a Special Issue entitled Recent developments of molecular dynamics. Copyright © 2014 Elsevier B.V. All rights reserved.
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NASA Astrophysics Data System (ADS)
Goulko, Olga; Kent, Adrian
2017-11-01
We introduce and physically motivate the following problem in geometric combinatorics, originally inspired by analysing Bell inequalities. A grasshopper lands at a random point on a planar lawn of area 1. It then jumps once, a fixed distance d, in a random direction. What shape should the lawn be to maximize the chance that the grasshopper remains on the lawn after jumping? We show that, perhaps surprisingly, a disc-shaped lawn is not optimal for any d>0. We investigate further by introducing a spin model whose ground state corresponds to the solution of a discrete version of the grasshopper problem. Simulated annealing and parallel tempering searches are consistent with the hypothesis that, for d<π-1/2, the optimal lawn resembles a cogwheel with n cogs, where the integer n is close to π (arcsin(√{π }d / 2 )) -1. We find transitions to other shapes for d ≳π-1 / 2.
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FPFH-based graph matching for 3D point cloud registration
NASA Astrophysics Data System (ADS)
Zhao, Jiapeng; Li, Chen; Tian, Lihua; Zhu, Jihua
2018-04-01
Correspondence detection is a vital step in point cloud registration and it can help getting a reliable initial alignment. In this paper, we put forward an advanced point feature-based graph matching algorithm to solve the initial alignment problem of rigid 3D point cloud registration with partial overlap. Specifically, Fast Point Feature Histograms are used to determine the initial possible correspondences firstly. Next, a new objective function is provided to make the graph matching more suitable for partially overlapping point cloud. The objective function is optimized by the simulated annealing algorithm for final group of correct correspondences. Finally, we present a novel set partitioning method which can transform the NP-hard optimization problem into a O(n3)-solvable one. Experiments on the Stanford and UWA public data sets indicates that our method can obtain better result in terms of both accuracy and time cost compared with other point cloud registration methods.
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Self-Adaptive Stepsize Search Applied to Optimal Structural Design
NASA Astrophysics Data System (ADS)
Nolle, L.; Bland, J. A.
Structural engineering often involves the design of space frames that are required to resist predefined external forces without exhibiting plastic deformation. The weight of the structure and hence the weight of its constituent members has to be as low as possible for economical reasons without violating any of the load constraints. Design spaces are usually vast and the computational costs for analyzing a single design are usually high. Therefore, not every possible design can be evaluated for real-world problems. In this work, a standard structural design problem, the 25-bar problem, has been solved using self-adaptive stepsize search (SASS), a relatively new search heuristic. This algorithm has only one control parameter and therefore overcomes the drawback of modern search heuristics, i.e. the need to first find a set of optimum control parameter settings for the problem at hand. In this work, SASS outperforms simulated-annealing, genetic algorithms, tabu search and ant colony optimization.
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A FODO racetrack ring for nuSTORM: design and optimization
DOE Office of Scientific and Technical Information (OSTI.GOV)
Liu, A.; Bross, A.; Neuffer, D.
2017-07-01
The goal of nuSTORM is to provide well-defined neutrino beams for precise measurements of neutrino cross-sections and oscillations. The nuSTORM decay ring is a compact racetrack storage ring with a circumference of ~ 480 m that incorporates large aperture (60 cm diameter) magnets. There are many challenges in the design. In order to incorporate the Orbit Combination section (OCS), used for injecting the pion beam into the ring, a dispersion suppressor is needed adjacent to the OCS . Concurrently, in order to maximize the number of useful muon decays, strong bending dipoles are needed in the arcs to minimize the arcmore » length. These dipoles create strong chromatic effects, which need to be corrected by nonlinear sextupole elements in the ring. In this paper, a FODO racetrack ring design and its optimization using sextupolar fields via both a Genetic Algorithm (GA) and a Simulated Annealing (SA) algorithm will be discussed.« less
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Intrinsic optimization using stochastic nanomagnets
Sutton, Brian; Camsari, Kerem Yunus; Behin-Aein, Behtash; Datta, Supriyo
2017-01-01
This paper draws attention to a hardware system which can be engineered so that its intrinsic physics is described by the generalized Ising model and can encode the solution to many important NP-hard problems as its ground state. The basic constituents are stochastic nanomagnets which switch randomly between the ±1 Ising states and can be monitored continuously with standard electronics. Their mutual interactions can be short or long range, and their strengths can be reconfigured as needed to solve specific problems and to anneal the system at room temperature. The natural laws of statistical mechanics guide the network of stochastic nanomagnets at GHz speeds through the collective states with an emphasis on the low energy states that represent optimal solutions. As proof-of-concept, we present simulation results for standard NP-complete examples including a 16-city traveling salesman problem using experimentally benchmarked models for spin-transfer torque driven stochastic nanomagnets. PMID:28295053
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Intrinsic optimization using stochastic nanomagnets
NASA Astrophysics Data System (ADS)
Sutton, Brian; Camsari, Kerem Yunus; Behin-Aein, Behtash; Datta, Supriyo
2017-03-01
This paper draws attention to a hardware system which can be engineered so that its intrinsic physics is described by the generalized Ising model and can encode the solution to many important NP-hard problems as its ground state. The basic constituents are stochastic nanomagnets which switch randomly between the ±1 Ising states and can be monitored continuously with standard electronics. Their mutual interactions can be short or long range, and their strengths can be reconfigured as needed to solve specific problems and to anneal the system at room temperature. The natural laws of statistical mechanics guide the network of stochastic nanomagnets at GHz speeds through the collective states with an emphasis on the low energy states that represent optimal solutions. As proof-of-concept, we present simulation results for standard NP-complete examples including a 16-city traveling salesman problem using experimentally benchmarked models for spin-transfer torque driven stochastic nanomagnets.
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Xu, Kai; Wei, Dong-Qing; Chen, Xiang-Rong; Ji, Guang-Fu
2014-10-01
The Car-Parrinello molecular dynamics simulation was applied to study the thermal decomposition of solid phase nitromethane under gradual heating and fast annealing conditions. In gradual heating simulations, we found that, rather than C-N bond cleavage, intermolecular proton transfer is more likely to be the first reaction in the decomposition process. At high temperature, the first reaction in fast annealing simulation is intermolecular proton transfer leading to CH3NOOH and CH2NO2, whereas the initial chemical event at low temperature tends to be a unimolecular C-N bond cleavage, producing CH3 and NO2 fragments. It is the first time to date that the direct rupture of a C-N bond has been reported as the first reaction in solid phase nitromethane. In addition, the fast annealing simulations on a supercell at different temperatures are conducted to validate the effect of simulation cell size on initial reaction mechanisms. The results are in qualitative agreement with the simulations on a unit cell. By analyzing the time evolution of some molecules, we also found that the time of first water molecule formation is clearly sensitive to heating rates and target temperatures when the first reaction is an intermolecular proton transfer.
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NASA Astrophysics Data System (ADS)
Ren, Jiyun; Menon, Geetha; Sloboda, Ron
2013-04-01
Although the Manchester system is still extensively used to prescribe dose in brachytherapy (BT) for locally advanced cervix cancer, many radiation oncology centers are transitioning to 3D image-guided BT, owing to the excellent anatomy definition offered by modern imaging modalities. As automatic dose optimization is highly desirable for 3D image-based BT, this study comparatively evaluates the performance of two optimization methods used in BT treatment planning—Nelder-Mead simplex (NMS) and simulated annealing (SA)—for a cervix BT computer simulation model incorporating a Manchester-style applicator. Eight model cases were constructed based on anatomical structure data (for high risk-clinical target volume (HR-CTV), bladder, rectum and sigmoid) obtained from measurements on fused MR-CT images for BT patients. D90 and V100 for HR-CTV, D2cc for organs at risk (OARs), dose to point A, conformation index and the sum of dwell times within the tandem and ovoids were calculated for optimized treatment plans designed to treat the HR-CTV in a highly conformal manner. Compared to the NMS algorithm, SA was found to be superior as it could perform optimization starting from a range of initial dwell times, while the performance of NMS was strongly dependent on their initial choice. SA-optimized plans also exhibited lower D2cc to OARs, especially the bladder and sigmoid, and reduced tandem dwell times. For cases with smaller HR-CTV having good separation from adjoining OARs, multiple SA-optimized solutions were found which differed markedly from each other and were associated with different choices for initial dwell times. Finally and importantly, the SA method yielded plans with lower dwell time variability compared with the NMS method.
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NASA Astrophysics Data System (ADS)
Jang, Seon-Ho; Jo, Yong-Ryun; Lee, Young-Woong; Kim, Sei-Min; Kim, Bong-Joong; Bae, Jae-Hyun; An, Huei-Chun; Jang, Ja-Soon
2015-05-01
We report a highly transparent conducting electrode (TCE) scheme of MgxZn1-xO:Ga/Au/NiOx which was deposited on p-GaN by e-beam for GaN-based light emitting diodes (LEDs). The optical and electrical properties of the electrode were optimized by thermal annealing at 500°C for 1 minute in N2 + O2 (5:3) ambient. The light transmittance at the optimal condition increased up to 84-97% from the UV-A to yellow region. The specific contact resistance decreased to 4.3(±0.3) × 10-5 Ωcm2. The improved properties of the electrode were attributed to the directionally elongated crystalline nanostructures formed in the MgxZn1-xO:Ga layer which is compositionally uniform. Interestingly, the Au alloy nano-clusters created in the MgxZn1-xO:Ga layer during annealing at 500°C may also enhance the properties of the electrode by acting as a conducting bridge and a nano-sized mirror. Based on studies of the external quantum efficiency of blue LED devices, the proposed electrode scheme combined with an optimized annealing treatment suggests a potential alternative to ITO. [Figure not available: see fulltext.
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Hao, Ge-Fei; Xu, Wei-Fang; Yang, Sheng-Gang; Yang, Guang-Fu
2015-01-01
Protein and peptide structure predictions are of paramount importance for understanding their functions, as well as the interactions with other molecules. However, the use of molecular simulation techniques to directly predict the peptide structure from the primary amino acid sequence is always hindered by the rough topology of the conformational space and the limited simulation time scale. We developed here a new strategy, named Multiple Simulated Annealing-Molecular Dynamics (MSA-MD) to identify the native states of a peptide and miniprotein. A cluster of near native structures could be obtained by using the MSA-MD method, which turned out to be significantly more efficient in reaching the native structure compared to continuous MD and conventional SA-MD simulation. PMID:26492886
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NASA Astrophysics Data System (ADS)
Valova-Zaharevskaya, E. G.; Popova, E. N.; Deryagina, I. L.; Abdyukhanov, I. M.; Tsapleva, A. S.
2018-03-01
The goal of the present study is to characterize the growth kinetics and structural parameters of the Nb3Sn layers formed under various regimes of the diffusion annealing of bronze-processed Nb/Cu-Sn composites. The structure of the superconducting layers is characterized by their thickness, average size of equiaxed grains and by the ratio of fractions of columnar and equiaxed grains. It was found that at higher diffusion annealing temperatures (above 650°C) thicker superconducting layers are obtained, but the average sizes of equiaxed Nb3Sn grains even under short exposures (10 h) are much larger than after the long low-temperature annealing. At the low-temperature (575 °C) annealing the relative fraction of columnar grains increases with increasing annealing time. Based on the data obtained, optimal regimes of the diffusion annealing can be chosen, which would on the one hand ensure complete transformation of Nb into Nb3Sn of close to the stoichiometric composition, and on the other hand prevent the formation of coarse and columnar grains.
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NASA Astrophysics Data System (ADS)
Soylu, M.; Yazici, T.
2017-12-01
Undoped CdO films were prepared on glass substrate and p-type silicon wafer using sol-gel spin coating method. The structural and optical properties of the films were investigated as a function of the annealing temperature. X-ray diffraction (XRD) patterns reveal that the films are formed from CdO with cubic crystal structure and (1 1 1) preferred orientation. It is seen that good crystallinity is due to the high annealing temperature. The surface morphology of the CdO films was found to be depending on the annealing temperature, showing cauliflower like structure. Optical band gaps for annealing temperature of 250 °C and 450 °C were found to be 2.49 eV and 2.27 eV, respectively, showing a decrease with raising temperature. Optics parameters such as extinction coefficient, refractive index, and surface-volume energy loss were determined with spectrophotometric analysis as a function of annealing temperature. CdO/p-Si heterojunction structure showed weak rectifying behavior. The diode parameters were found to be depending on annealing temperature. The results are encouraging to get better conjunction with CdO thin film component at optimize annealing temperature.
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NASA Astrophysics Data System (ADS)
Hiller, Daniel; Gutsch, Sebastian; Hartel, Andreas M.; Löper, Philipp; Gebel, Thoralf; Zacharias, Margit
2014-04-01
Silicon nanocrystals (Si NCs) for 3rd generation photovoltaics or optoelectronic applications can be produced by several industrially compatible physical or chemical vapor deposition technologies. A major obstacle for the integration into a fabrication process is the typical annealing to form and crystallize these Si quantum dots (QDs) which involves temperatures ≥1100 °C for 1 h. This standard annealing procedure allows for interface qualities that correspond to more than 95% dangling bond defect free Si NCs. We study the possibilities to use rapid thermal annealing (RTA) and flash lamp annealing to crystallize the Si QDs within seconds or milliseconds at high temperatures. The Si NC interface of such samples exhibits huge dangling bond defect densities which makes them inapplicable for photovoltaics or optoelectronics. However, if the RTA high temperature annealing is combined with a medium temperature inert gas post-annealing and a H2 passivation, luminescent Si NC fractions of up to 90% can be achieved with a significantly reduced thermal load. A new figure or merit, the relative dopant diffusion length, is introduced as a measure for the impact of a Si NC annealing procedure on doping profiles of device structures.
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Deformation and annealing response of TD-nickel chromium sheet
NASA Technical Reports Server (NTRS)
Kane, R. D.; Ebert, L. J.
1973-01-01
The deformation and annealing response of TD-nickel chromium (TD-NiCr) 0.1 inch thick sheet was examined using various cold-rolling and annealing treatments. Upon annealing (above 816 C (1500 F), the as-received material was converted from an initially ultra-fine grain size (average grain dimension 0.51 micron) to a large grain structure. Increases in grain size by a factor of 100 to 200 were observed for this transformation. However, in those material states where the large grain transformation was absent, a fine grain recrystallized structure formed upon annealing (above 732 C (1350 F)). The deformation and annealing response of TD-NiCr sheet was evaluated with respect to the processing related variables as mode and severity of deformation and annealing temperature. Results indicate that the large grain transformation, classical primary recrystallization occurs. Using selected materials produced during the deformation and annealing study, the elevated temperature tensile properties of TD-NiCr sheet were examined in the temperature range 593 C (1100 F) to 1093 C (2000 F). It was observed that the elevated temperature tensile properties of TD-NiCr sheet could be optimized by the stabilization of a large grain size in this material using the cold working and/or annealing treatments developed during the present investigation.
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An efficient and scalable deformable model for virtual reality-based medical applications.
Choi, Kup-Sze; Sun, Hanqiu; Heng, Pheng-Ann
2004-09-01
Modeling of tissue deformation is of great importance to virtual reality (VR)-based medical simulations. Considerable effort has been dedicated to the development of interactively deformable virtual tissues. In this paper, an efficient and scalable deformable model is presented for virtual-reality-based medical applications. It considers deformation as a localized force transmittal process which is governed by algorithms based on breadth-first search (BFS). The computational speed is scalable to facilitate real-time interaction by adjusting the penetration depth. Simulated annealing (SA) algorithms are developed to optimize the model parameters by using the reference data generated with the linear static finite element method (FEM). The mechanical behavior and timing performance of the model have been evaluated. The model has been applied to simulate the typical behavior of living tissues and anisotropic materials. Integration with a haptic device has also been achieved on a generic personal computer (PC) platform. The proposed technique provides a feasible solution for VR-based medical simulations and has the potential for multi-user collaborative work in virtual environment.
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Sadeghi, Maryam; Faghihi, Reza; Sina, Sedigheh
2017-06-15
Thermoluminescence dosimetry (TLD) is a powerful technique with wide applications in personal, environmental and clinical dosimetry. The optimum annealing, storage and reading protocols are very effective in accuracy of TLD response. The purpose of this study is to obtain an optimum protocol for GR-200; LiF: Mg, Cu, P, by optimizing the effective parameters, to increase the reliability of the TLD response using Taguchi method. Taguchi method has been used in this study for optimization of annealing, storage and reading protocols of the TLDs. A number of 108 GR-200 chips were divided into 27 groups, each containing four chips. The TLDs were exposed to three different doses, and stored, annealed and read out by different procedures as suggested by Taguchi Method. By comparing the signal-to-noise ratios the optimum dosimetry procedure was obtained. According to the results, the optimum values for annealing temperature (°C), Annealing Time (s), Annealing to Exposure time (d), Exposure to Readout time (d), Pre-heat Temperature (°C), Pre-heat Time (s), Heating Rate (°C/s), Maximum Temperature of Readout (°C), readout time (s) and Storage Temperature (°C) are 240, 90, 1, 2, 50, 0, 15, 240, 13 and -20, respectively. Using the optimum protocol, an efficient glow curve with low residual signals can be achieved. Using optimum protocol obtained by Taguchi method, the dosimetry can be effectively performed with great accuracy. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
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NASA Astrophysics Data System (ADS)
Jia, Bo Wen; Tan, Kian Hua; Loke, Wan Khai; Wicaksono, Satrio; Yoon, Soon Fatt
2018-01-01
This work presents the effects of in situ thermal annealing under antimony overpressure on the structural, electrical, and optical properties of III-Sb (GaSb and InSb) grown on (100) GaAs using an interfacial misfit array to accommodate the lattice mismatch. Both the sample growth and the in situ thermal annealing were carried out in the in the molecular beam epitaxy system, and the temperature of the as-grown sample was increased to exceed its growth temperature during the annealing. X-ray diffraction demonstrates nearly fully relaxed as-grown and annealed III-Sb layers. The optimal annealing temperatures and durations are for 590 °C, 5 min for GaSb and 420 °C, 15 min for InSb, respectively. In situ annealing decreased the surface roughness of the III-Sb layers. X-ray reciprocal space mapping and transmission electron microscopy observation showed stable interfacial misfit arrays, and no interfacial diffusion occurred in the annealed III-Sb layers. A Hall measurement of unintentionally doped III-Sb layers showed greater carrier mobility and a lower carrier concentration in the annealed samples at both 77 and 300 K. In situ annealing improved the photoresponsivity of GaSb and InSb photoconductors grown on GaAs in the near- and mid-infrared ranges, respectively.
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Assessment of MARMOT Grain Growth Model
DOE Office of Scientific and Technical Information (OSTI.GOV)
Fromm, B.; Zhang, Y.; Schwen, D.
2015-12-01
This report assesses the MARMOT grain growth model by comparing modeling predictions with experimental results from thermal annealing. The purpose here is threefold: (1) to demonstrate the validation approach of using thermal annealing experiments with non-destructive characterization, (2) to test the reconstruction capability and computation efficiency in MOOSE, and (3) to validate the grain growth model and the associated parameters that are implemented in MARMOT for UO 2. To assure a rigorous comparison, the 2D and 3D initial experimental microstructures of UO 2 samples were characterized using non-destructive Synchrotron x-ray. The same samples were then annealed at 2273K for grainmore » growth, and their initial microstructures were used as initial conditions for simulated annealing at the same temperature using MARMOT. After annealing, the final experimental microstructures were characterized again to compare with the results from simulations. So far, comparison between modeling and experiments has been done for 2D microstructures, and 3D comparison is underway. The preliminary results demonstrated the usefulness of the non-destructive characterization method for MARMOT grain growth model validation. A detailed analysis of the 3D microstructures is in progress to fully validate the current model in MARMOT.« less
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CATO: a CAD tool for intelligent design of optical networks and interconnects
NASA Astrophysics Data System (ADS)
Chlamtac, Imrich; Ciesielski, Maciej; Fumagalli, Andrea F.; Ruszczyk, Chester; Wedzinga, Gosse
1997-10-01
Increasing communication speed requirements have created a great interest in very high speed optical and all-optical networks and interconnects. The design of these optical systems is a highly complex task, requiring the simultaneous optimization of various parts of the system, ranging from optical components' characteristics to access protocol techniques. Currently there are no computer aided design (CAD) tools on the market to support the interrelated design of all parts of optical communication systems, thus the designer has to rely on costly and time consuming testbed evaluations. The objective of the CATO (CAD tool for optical networks and interconnects) project is to develop a prototype of an intelligent CAD tool for the specification, design, simulation and optimization of optical communication networks. CATO allows the user to build an abstract, possible incomplete, model of the system, and determine its expected performance. Based on design constraints provided by the user, CATO will automatically complete an optimum design, using mathematical programming techniques, intelligent search methods and artificial intelligence (AI). Initial design and testing of a CATO prototype (CATO-1) has been completed recently. The objective was to prove the feasibility of combining AI techniques, simulation techniques, an optical device library and a graphical user interface into a flexible CAD tool for obtaining optimal communication network designs in terms of system cost and performance. CATO-1 is an experimental tool for designing packet-switching wavelength division multiplexing all-optical communication systems using a LAN/MAN ring topology as the underlying network. The two specific AI algorithms incorporated are simulated annealing and a genetic algorithm. CATO-1 finds the optimal number of transceivers for each network node, using an objective function that includes the cost of the devices and the overall system performance.
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The Mechanical Property of Batch Annealed High Strength Low Alloy Steel HC260LA
NASA Astrophysics Data System (ADS)
Yang, Xiaojiang; Xia, Mingsheng; Zhang, Hongbo; Han, Bin; Li, Guilan
Cold rolled high strength low alloy steel is widely applied in the automotive parts due to its excellent formability and weldability. In this paper, the steel grade HC260LA according to European Norm was developed with batch annealing process. With commercial C-Mn mild steel as a benchmark, three different groups of chemistry namely C-Mn-Si, C-Mn-Nb-Ti and C-Mn-Nb were compared in terms of yield-tensile strength (Y/T) ratio. Microstructure and mechanical properties were characterized as well. Based on industrial production results, chemistry and detailed process parameters for batch annealing were identified. In the end the optimal Y/T ratio was proposed for this steel grade under batch annealing process.
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Advances in Integrated Computational Materials Engineering "ICME"
NASA Astrophysics Data System (ADS)
Hirsch, Jürgen
The methods of Integrated Computational Materials Engineering that were developed and successfully applied for Aluminium have been constantly improved. The main aspects and recent advances of integrated material and process modeling are simulations of material properties like strength and forming properties and for the specific microstructure evolution during processing (rolling, extrusion, annealing) under the influence of material constitution and process variations through the production process down to the final application. Examples are discussed for the through-process simulation of microstructures and related properties of Aluminium sheet, including DC ingot casting, pre-heating and homogenization, hot and cold rolling, final annealing. New results are included of simulation solution annealing and age hardening of 6xxx alloys for automotive applications. Physically based quantitative descriptions and computer assisted evaluation methods are new ICME methods of integrating new simulation tools also for customer applications, like heat affected zones in welding of age hardening alloys. The aspects of estimating the effect of specific elements due to growing recycling volumes requested also for high end Aluminium products are also discussed, being of special interest in the Aluminium producing industries.
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NASA Astrophysics Data System (ADS)
Zhang, Pengpeng
The Leksell Gamma KnifeRTM (LGK) is a tool for providing accurate stereotactic radiosurgical treatment of brain lesions, especially tumors. Currently, the treatment planning team "forward" plans radiation treatment parameters while viewing a series of 2D MR scans. This primarily manual process is cumbersome and time consuming because the difficulty in visualizing the large search space for the radiation parameters (i.e., shot overlap, number, location, size, and weight). I hypothesize that a computer-aided "inverse" planning procedure that utilizes tumor geometry and treatment goals could significantly improve the planning process and therapeutic outcome of LGK radiosurgery. My basic observation is that the treatment team is best at identification of the location of the lesion and prescribing a lethal, yet safe, radiation dose. The treatment planning computer is best at determining both the 3D tumor geometry and optimal LGK shot parameters necessary to deliver a desirable dose pattern to the tumor while sparing adjacent normal tissue. My treatment planning procedure asks the neurosurgeon to identify the tumor and critical structures in MR images and the oncologist to prescribe a tumoricidal radiation dose. Computer-assistance begins with geometric modeling of the 3D tumor's medial axis properties. This begins with a new algorithm, a Gradient-Phase Plot (G-P Plot) decomposition of the tumor object's medial axis. I have found that medial axis seeding, while insufficient in most cases to produce an acceptable treatment plan, greatly reduces the solution space for Guided Evolutionary Simulated Annealing (GESA) treatment plan optimization by specifying an initial estimate for shot number, size, and location, but not weight. They are used to generate multiple initial plans which become initial seed plans for GESA. The shot location and weight parameters evolve and compete in the GESA procedure. The GESA objective function optimizes tumor irradiation (i.e., as close to the prescribed dose as possible) and minimizes normal tissue and critical structure damage. In tests of five patient data sets (4 acoustic neuromas and 1 meningioma), the G-P Plot/GESA-generated treatment plans improved conformality of the lethal dose to the tumor, required no human interaction, improved dose homogeneity, suggested use of fewer shots, and reduced treatment administration time.
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Signatures of active and passive optimized Lévy searching in jellyfish.
Reynolds, Andy M
2014-10-06
Some of the strongest empirical support for Lévy search theory has come from telemetry data for the dive patterns of marine predators (sharks, bony fishes, sea turtles and penguins). The dive patterns of the unusually large jellyfish Rhizostoma octopus do, however, sit outside of current Lévy search theory which predicts that a single search strategy is optimal. When searching the water column, the movement patterns of these jellyfish change over time. Movement bouts can be approximated by a variety of Lévy and Brownian (exponential) walks. The adaptive value of this variation is not known. On some occasions movement pattern data are consistent with the jellyfish prospecting away from a preferred depth, not finding an improvement in conditions elsewhere and so returning to their original depth. This 'bounce' behaviour also sits outside of current Lévy walk search theory. Here, it is shown that the jellyfish movement patterns are consistent with their using optimized 'fast simulated annealing'--a novel kind of Lévy walk search pattern--to locate the maximum prey concentration in the water column and/or to locate the strongest of many olfactory trails emanating from more distant prey. Fast simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a large search space. This new finding shows that the notion of active optimized Lévy walk searching is not limited to the search for randomly and sparsely distributed resources, as previously thought, but can be extended to embrace other scenarios, including that of the jellyfish R. octopus. In the presence of convective currents, it could become energetically favourable to search the water column by riding the convective currents. Here, it is shown that these passive movements can be represented accurately by Lévy walks of the type occasionally seen in R. octopus. This result vividly illustrates that Lévy walks are not necessarily the result of selection pressures for advantageous searching behaviour but can instead arise freely and naturally from simple processes. It also shows that the family of Lévy walkers is vastly larger than previously thought and includes spores, pollens, seeds and minute wingless arthropods that on warm days disperse passively within the atmospheric boundary layer. © 2014 The Author(s) Published by the Royal Society. All rights reserved.
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Thin film module electrical configuration versus electrical performance
NASA Technical Reports Server (NTRS)
Morel, D. L.
1985-01-01
The as made and degraded states of thin film silicon (TFS) based modules have been modelled in terms of series resistance losses. The origins of these losses lie in interface and bulk regions of the devices. When modules degrade under light exposure, increases occur in both the interface and bulk components of the loss based on series resistance. Actual module performance can thus be simulated by use of only one unknown parameter, shunt losses. Use of the simulation to optimize module design indicates that the current design of 25 cells per linear foot is near optimum. Degradation performance suggests a shift to approx. 35 cells to effect maximum output for applications not constrained to 12 volts. Earlier studies of energy based performance and tandem structures should be updated to include stability factors, not only the initial loss factor tested here, but also appropriate annealing factors.
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Antimony-Doped Tin Oxide Thin Films Grown by Home Made Spray Pyrolysis Technique
NASA Astrophysics Data System (ADS)
Yusuf, Gbadebo; Babatola, Babatunde Keji; Ishola, Abdulahi Dimeji; Awodugba, Ayodeji O.; Solar cell Collaboration
2016-03-01
Transparent conducting antimony-doped tin oxide (ATO) films have been deposited on glass substrates by home made spray pyrolysis technique. The structural, electrical and optical properties of the ATO films have been investigated as a function of Sb-doping level and annealing temperature. The optimum target composition for high conductivity and low resistivity was found to be 20 wt. % SnSb2 + 90 wt. ATO. Under optimized deposition conditions of 450oC annealing temperature, electrical resistivity of 5.2×10-4 Ω -cm, sheet resistance of 16.4 Ω/sq, average optical transmittance of 86% in the visible range, and average optical band-gap of 3.34eV were obtained. The film deposited at lower annealing temperature shows a relatively rough, loosely bound slightly porous surface morphology while the film deposited at higher annealing temperature shows uniformly distributed grains of greater size. Keywords: Annealing, Doping, Homemade spray pyrolysis, Tin oxide, Resistivity
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Phase Formation and Superconductivity of Fe-TUBE Encapsulated and Vacuum-Annealed MgB2
NASA Astrophysics Data System (ADS)
Singh, K. P.; Awana, V. P. S.; Shahabuddin, Md.; Husain, M.; Saxena, R. B.; Nigam, Rashmi; Ansari, M. A.; Gupta, Anurag; Narayan, Himanshu; Halder, S. K.; Kishan, H.
We report optimization of the synthesis parameters viz. heating temperature (TH), and hold time (thold) for vacuum-annealed (10-5 Torr) and LN2 (liquid nitrogen) quenched MgB2 compound. These are single-phase compounds crystallizing in the hexagonal structure (space group P6/mmm) at room temperature. Our XRD results indicated that for phase-pure MgB2, the TH for 10-5 Torr annealed and LN2-quenched samples is 750°C. The right stoichiometry i.e., MgB2 of the compound corresponding to 10-5 Torr and TH of 750°C is found for the hold time (thold) of 2.30 hours. With varying thold from 1-4 hours at fixed TH (750°C) and vacuum (10-5 Torr), the c-lattice parameter decreases first and later increases with thold (hours) before a near saturation, while the a-lattice parameter first increases and later decreases beyond a thold of 2.30 hours. The c/a ratio versus thold plot showed an inverted bell-shaped curve, touching the lowest value of 1.141, which is the reported value for perfect stoichiometry of MgB2. The optimized stoichimetric MgB2 compound exhibited superconductivity at 39.2 K with a transition width of 0.6 K. In conclusion, the synthesis parameters for phase pure stoichimetric vacuum-annealed MgB2 compound are optimized and are compared with widely-reported Ta tube encapsulated samples.
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Obtaining phase-pure CZTS thin films by annealing vacuum evaporated CuS/SnS/ZnS stack
NASA Astrophysics Data System (ADS)
Sánchez, T. G.; Mathew, X.; Mathews, N. R.
2016-07-01
Cu2ZnSnS4 (CZTS) thin films were obtained by the sequential thermal evaporation of metal binary sulfides in the order CuS/SnS/ZnS, followed by annealing in Ar/S atmosphere. The as-grown films were annealed at different temperatures ranging between 350 and 600 °C, for 10 min. Based on the preliminary results, the temperatures 550 °C and 600 °C were selected for further optimization and a second batch of films were annealed for different time durations (10 min, 30 min and 60 min) at these temperatures in order to identify the conditions to obtain phase-pure CZTS films. The structural properties and chemical compositions at each temperature were investigated in order to optimize the phase purity and film stoichiometry. We have identified adequate and reproducible conditions to obtain the elemental ratio Cu/(Zn+Sn) and Zn/Sn close to 0.78 and 1.19 respectively, which is in the range of material composition required for promising solar cells. In addition the optimized material showed excellent optical and electrical properties to be used as a photovoltaic absorber layer. The optical band gap was found to be about 1.52 eV, and the carrier concentration, hall mobility, and resistivity were in the range of 8.372×1015 cm-3, 3.103 cm2/Vs and 340.3 Ω-cm respectively. Three traps with activation energies 4.39, 8.1, and 34 meV were detected.
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Stamping an AA5754 Train Window Panel with High Dent Resistance Using Locally Annealed Blanks
NASA Astrophysics Data System (ADS)
Piccininni, A.; Guglielmi, P.; Lo Franco, A.; Palumbo, G.
2017-09-01
The warm stamping of an AA5754-H32 window panel for railway vehicles applications has been proposed in the present work. The adoption of increased working temperatures can be surely considered the most effective solution for this alloy to overcome the limited material formability at room temperature [Palumbo et al. “Warm Forming of an AA5754 Component for Railway Vehicle Applications”, Procedia Engineering, Vol. 183, 2017, Pages 351-356] but, in order to improve the overall dent resistance of the component, the initial wrought conditions have been chosen in the present work. The manufacturing of the window panel was thus subdivided into a preliminary local heat treatment (assumed to be performed by laser) to anneal the material and a subsequent warm stamping step using heated tools. The best combination of temperature and holding time able to produce the annealing of the investigated alloy was determined using the physical simulator Gleeble 3180. On the contrary, the warm forming step was designed by means of thermo-mechanical simulations: in order to model the AA5754-H32 blank with annealed regions, an extensive experimental campaign (tensile and formability tests) was conducted using specimens in the annealed (H111) and in the wrought (H32) conditions. Through the numerical approach it was thus possible define: (i) the extent of the annealed regions; (ii) the punch speed to get a sound component.
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The placement of equipment in the Space Station Freedom using constraint based reasoning
NASA Technical Reports Server (NTRS)
Tanner, Steve; Fennel, Randy
1991-01-01
This paper describes the Rack Equipment Placement and Optimization System. The primary objective of this system is to assist engineers with the placement of equipment into the racks of the modules of Space Station Freedom. It accomplishes this by showing a user where equipment placement is possible and by generating potential layouts. The system uses an explicit representation of integration constraints to search for potential solutions for individual rack equipment items. A simulated annealing process is being evaluated for total solution generation as well. Versions of this system are in use now and are assisting with the development of the Space Station Freedom at the Marshall Space Flight Center in Huntsville, Alabama.
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Iterative repair for scheduling and rescheduling
NASA Technical Reports Server (NTRS)
Zweben, Monte; Davis, Eugene; Deale, Michael
1991-01-01
An iterative repair search method is described called constraint based simulated annealing. Simulated annealing is a hill climbing search technique capable of escaping local minima. The utility of the constraint based framework is shown by comparing search performance with and without the constraint framework on a suite of randomly generated problems. Results are also shown of applying the technique to the NASA Space Shuttle ground processing problem. These experiments show that the search methods scales to complex, real world problems and reflects interesting anytime behavior.
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NASA Astrophysics Data System (ADS)
Lin, Y.; Kessler, T. J.; Lawrence, G. N.
1996-10-01
High-performance phase plates are of vital concern for controlling the far-field irradiance of laser-fusion systems. Several designs for solving this difficult problem have been reported in Optics Letters [e. g., S. N. Dixit et al., Opt. Lett. 19, 417 (1994)]. We report a surface-based form of simulated annealing that significantly improves the irradiance control while eliminating the high-scatter problems that have plagued other methods.
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NASA Astrophysics Data System (ADS)
Piccininni, A.; Palumbo, G.; Franco, A. Lo; Sorgente, D.; Tricarico, L.; Russello, G.
2018-05-01
The continuous research for lightweight components for transport applications to reduce the harmful emissions drives the attention to the light alloys as in the case of Aluminium (Al) alloys, capable to combine low density with high values of the strength-to-weight ratio. Such advantages are partially counterbalanced by the poor formability at room temperature. A viable solution is to adopt a localized heat treatment by laser of the blank before the forming process to obtain a tailored distribution of material properties so that the blank can be formed at room temperature by means of conventional press machines. Such an approach has been extensively investigated for age hardenable alloys, but in the present work the attention is focused on the 5000 series; in particular, the optimization of the deep drawing process of the alloy AA5754 H32 is proposed through a numerical/experimental approach. A preliminary investigation was necessary to correctly tune the laser parameters (focus length, spot dimension) to effectively obtain the annealed state. Optimal process parameters were then obtained coupling a 2D FE model with an optimization platform managed by a multi-objective genetic algorithm. The optimal solution (i.e. able to maximize the LDR) in terms of blankholder force and extent of the annealed region was thus evaluated and validated through experimental trials. A good matching between experimental and numerical results was found. The optimal solution allowed to obtain an LDR of the locally heat treated blank larger than the one of the material either in the wrought condition (H32) either in the annealed condition (H111).
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Enhancement of visible photoluminescence in the SiNx films by SiO2 buffer and annealing
NASA Astrophysics Data System (ADS)
Xu, M.; Xu, S.; Chai, J. W.; Long, J. D.; Ee, Y. C.
2006-12-01
The authors report a simple method to significantly enhance the photoluminescence (PL) of SiNx films by incorporating a SiO2 buffer and annealing treatment under N2 protection. Strong visible PL is achieved with annealing temperature above 650°C. Optimal PL is obtained at 800°C. The composition and structure analysis reveal that strong PL is directly related to the content of the Si-O and Si-N bonds in the SiNx films. These bonds provide effective luminescent centers and passivate the interface between Si core and the surrounding oxide.
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Zhang, Yuwei; Cao, Zexing; Zhang, John Zenghui; Xia, Fei
2017-02-27
Construction of coarse-grained (CG) models for large biomolecules used for multiscale simulations demands a rigorous definition of CG sites for them. Several coarse-graining methods such as the simulated annealing and steepest descent (SASD) based on the essential dynamics coarse-graining (ED-CG) or the stepwise local iterative optimization (SLIO) based on the fluctuation maximization coarse-graining (FM-CG), were developed to do it. However, the practical applications of these methods such as SASD based on ED-CG are subject to limitations because they are too expensive. In this work, we extend the applicability of ED-CG by combining it with the SLIO algorithm. A comprehensive comparison of optimized results and accuracy of various algorithms based on ED-CG show that SLIO is the fastest as well as the most accurate algorithm among them. ED-CG combined with SLIO could give converged results as the number of CG sites increases, which demonstrates that it is another efficient method for coarse-graining large biomolecules. The construction of CG sites for Ras protein by using MD fluctuations demonstrates that the CG sites derived from FM-CG can reflect the fluctuation properties of secondary structures in Ras accurately.
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Annealing effect of the InAs dot-in-well structure grown by MBE
NASA Astrophysics Data System (ADS)
Zhao, Xuyi; Wang, Peng; Cao, Chunfang; Yan, Jinyi; Zha, Fangxing; Wang, Hailong; Gong, Qian
2017-12-01
We have demonstrated that in situ annealing effect has to be taken into account in order to realize the 1.31 μm InAs quantum dot (QD) lasers with the dot-in-well (DWELL) structure. The photoluminescence (PL) properties have been investigated for the InAs DWELL samples annealed at different temperatures in situ, simulating the annealing process during the growth of the top cladding AlGaAs layer in the laser structure. The QDs with large size in the DWELL structure are vulnerable to the annealing process at temperatures above 550 °C, revealed by the drastic change in the PL spectra. However, the DWELL structure is stable during the annealing process at 540 °C for three hours. The thermal stability of the QDs in the DWELL structure has to be considered in the growth of QD lasers for long wavelength operation.
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Microstructure engineering of Pt-Al alloy thin films through Monte Carlo simulations
NASA Astrophysics Data System (ADS)
Harris, R. A.; Terblans, J. J.; Swart, H. C.
2014-06-01
A kinetic algorithm, based on the regular solution model, was used in conjunction with the Monte Carlo method to simulate the evolution of a micro-scaled thin film system during exposure to a high temperature environment. Pt-Al thin films were prepared via electron beam physical vapor deposition (EB-PVD) with an atomic concentration ratio of Pt63:Al37. These films were heat treated at an annealing temperature of 400 °C for 16 and 49 minutes. Scanning Auger Microscopy (SAM) (PHI 700) was used to obtain elemental maps while sputtering through the thin films. Simulations were run for the same annealing temperatures and thin-film composition. From these simulations theoretical depth profiles and simulated microstructures were obtained. These were compared to the experimentally measured depth profiles and elemental maps.
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Optimized multiple quantum MAS lineshape simulations in solid state NMR
NASA Astrophysics Data System (ADS)
Brouwer, William J.; Davis, Michael C.; Mueller, Karl T.
2009-10-01
The majority of nuclei available for study in solid state Nuclear Magnetic Resonance have half-integer spin I>1/2, with corresponding electric quadrupole moment. As such, they may couple with a surrounding electric field gradient. This effect introduces anisotropic line broadening to spectra, arising from distinct chemical species within polycrystalline solids. In Multiple Quantum Magic Angle Spinning (MQMAS) experiments, a second frequency dimension is created, devoid of quadrupolar anisotropy. As a result, the center of gravity of peaks in the high resolution dimension is a function of isotropic second order quadrupole and chemical shift alone. However, for complex materials, these parameters take on a stochastic nature due in turn to structural and chemical disorder. Lineshapes may still overlap in the isotropic dimension, complicating the task of assignment and interpretation. A distributed computational approach is presented here which permits simulation of the two-dimensional MQMAS spectrum, generated by random variates from model distributions of isotropic chemical and quadrupole shifts. Owing to the non-convex nature of the residual sum of squares (RSS) function between experimental and simulated spectra, simulated annealing is used to optimize the simulation parameters. In this manner, local chemical environments for disordered materials may be characterized, and via a re-sampling approach, error estimates for parameters produced. Program summaryProgram title: mqmasOPT Catalogue identifier: AEEC_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEEC_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 3650 No. of bytes in distributed program, including test data, etc.: 73 853 Distribution format: tar.gz Programming language: C, OCTAVE Computer: UNIX/Linux Operating system: UNIX/Linux Has the code been vectorised or parallelized?: Yes RAM: Example: (1597 powder angles) × (200 Samples) × (81 F2 frequency pts) × (31 F1 frequency points) = 3.5M, SMP AMD opteron Classification: 2.3 External routines: OCTAVE ( http://www.gnu.org/software/octave/), GNU Scientific Library ( http://www.gnu.org/software/gsl/), OPENMP ( http://openmp.org/wp/) Nature of problem: The optimal simulation and modeling of multiple quantum magic angle spinning NMR spectra, for general systems, especially those with mild to significant disorder. The approach outlined and implemented in C and OCTAVE also produces model parameter error estimates. Solution method: A model for each distinct chemical site is first proposed, for the individual contribution of crystallite orientations to the spectrum. This model is averaged over all powder angles [1], as well as the (stochastic) parameters; isotropic chemical shift and quadrupole coupling constant. The latter is accomplished via sampling from a bi-variate Gaussian distribution, using the Box-Muller algorithm to transform Sobol (quasi) random numbers [2]. A simulated annealing optimization is performed, and finally the non-linear jackknife [3] is applied in developing model parameter error estimates. Additional comments: The distribution contains a script, mqmasOpt.m, which runs in the OCTAVE language workspace. Running time: Example: (1597 powder angles) × (200 Samples) × (81 F2 frequency pts) × (31 F1 frequency points) = 58.35 seconds, SMP AMD opteron. References:S.K. Zaremba, Annali di Matematica Pura ed Applicata 73 (1966) 293. H. Niederreiter, Random Number Generation and Quasi-Monte Carlo Methods, SIAM, 1992. T. Fox, D. Hinkley, K. Larntz, Technometrics 22 (1980) 29.
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NASA Astrophysics Data System (ADS)
Lok, R.; Kaya, S.; Yilmaz, E.
2018-05-01
In this work, the thermal phase separation and annealing optimization of ZrSiO4 thin films have been carried out. Following annealing optimization, the frequency-dependent electrical characteristics of the Al/ZrSiO4/p-Si/Al MOS capacitors were investigated in detail. The chemical evolution of the films under various annealing temperatures was determined by Fourier transform infrared spectroscopy (FTIR) measurements. The phase separation was determined by x-ray diffraction (XRD) measurements. The electrical parameters were determined via the capacitance–voltage (C–V), conductance–voltage (G/ω) and leakage-current–voltage (Ig–Vg ). The results demonstrate that zirconium silicate formations are present at 1000 °C annealing with the SiO2 interfacial layer. The film was in amorphous form after annealing at 250 °C. The tetragonal phases of ZrO2 were obtained after annealing at 500 °C. When the temperature approaches 750 °C, transitions from the tetragonal phase to the monoclinic phase were observed. The obtained XRD peaks after 1000 °C annealing matched the crystalline peaks of ZrSiO4. This means that the crystalline zirconium dioxide in the structure has been converted into a crystalline silicate phase. The interface states increased to 5.71 × 1010 and the number of border traps decreased to 7.18 × 1010 cm‑2 with the increasing temperature. These results indicate that an excellent ZrSiO4/Si interface has been fabricated. The order of the leakage current varied from 10‑9 Acm‑2 to 10‑6 Acm‑2. The MOS capacitor fabricated with the films annealed at 1000 °C shows better behavior in terms of its structural, chemical and electrical properties. Hence, detailed frequency-dependent electrical characteristics were performed for the ZrSiO4 thin film annealed at 1000 °C. Very slight capacitance variations were observed under the frequency variations. This shows that the density of frequency-dependent charges is very low at the ZrSiO4/Si interface. The barrier height of the device varies slightly from 0.776 eV to 0.827 eV under frequency dispersion. Briefly, it is concluded that the devices annealed at 1000 °C exhibit promising electrical characteristics.
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SME Worker Affective (SWA) index based on environmental ergonomics
NASA Astrophysics Data System (ADS)
Ushada, M.; Kusuma Aji, G.; Okayama, T.; Khidir, M.
2018-04-01
Small-Medium sized (SME) is a focal type of Indonesian industry which contributes to national emerging economies. Indonesian goverment has developed employee social security system (BPJS Ketenagakerjaan) to support worker quality of life. However, there were limited research which could assist BPJS Ketenagakerjaan in evaluating worker quality of life. Worker quality of life could be categorized as the highest worker needs or affective states. SME Worker Affective (SWA) index is being concerned as a basic tool to make balance between worker performance and quality of life in workstation of SMEs. The research objectives are: 1) To optimize the environmental ergonomics in SMEs; 2) To quantify SME Worker Affective (SWA) index based on optimized environmental ergonomics. The research advantage is to support Indonesian goverment in monitoring SMEs good practices to its worker quality of life. Simulated annealing optimized the heart rate and environmental ergonomics parameters. SWA index was determined based on comparison between optimized heart rate and environmental ergonomics parameters. SWA index were quantified for 380 data of worker. The evaluation indicated 51.3% worker in affective and 48.7% in non-affective condition. Research results indicated that stakeholders of SMEs should put more attention on environmental ergonomics and worker affective.
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Social Milieu Oriented Routing: A New Dimension to Enhance Network Security in WSNs.
Liu, Lianggui; Chen, Li; Jia, Huiling
2016-02-19
In large-scale wireless sensor networks (WSNs), in order to enhance network security, it is crucial for a trustor node to perform social milieu oriented routing to a target a trustee node to carry out trust evaluation. This challenging social milieu oriented routing with more than one end-to-end Quality of Trust (QoT) constraint has proved to be NP-complete. Heuristic algorithms with polynomial and pseudo-polynomial-time complexities are often used to deal with this challenging problem. However, existing solutions cannot guarantee the efficiency of searching; that is, they can hardly avoid obtaining partial optimal solutions during a searching process. Quantum annealing (QA) uses delocalization and tunneling to avoid falling into local minima without sacrificing execution time. This has been proven a promising way to many optimization problems in recently published literatures. In this paper, for the first time, with the help of a novel approach, that is, configuration path-integral Monte Carlo (CPIMC) simulations, a QA-based optimal social trust path (QA_OSTP) selection algorithm is applied to the extraction of the optimal social trust path in large-scale WSNs. Extensive experiments have been conducted, and the experiment results demonstrate that QA_OSTP outperforms its heuristic opponents.
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Air-annealing of Cu(In, Ga)Se2/CdS and performances of CIGS solar cells
NASA Astrophysics Data System (ADS)
Niu, X.; Zhu, H.; Liang, X.; Guo, Y.; Li, Z.; Mai, Y.
2017-12-01
In this study, the annealing treatment on Cu(In, Ga)Se2 (CIGS)/CdS interface in air is systematically investigated under different annealing temperatures from room temperature to 150 °C and different durations. It is found that when CIGS/CdS interface is annealed for a proper duration the corresponding CIGS thin film solar cells show enhanced open circuit voltage (Voc) and fill factor (FF) as well as corresponding conversion efficiency. The capacitance-voltage (C-V) and time-resolved photoluminescence (TR-PL) measurement results indicate that the CIGS thin film solar cells exhibit an increase in net defect density (NCV) and long lifetime for the carriers, respectively, after the annealing treatment of CIGS/CdS at a mediate annealing temperature here. Moreover, the net defect density of annealed solar cells at higher annealing temperatures for a long duration is reduced. All the variations in the solar cell performances, NCV and carrier lifetime would be related to the passivation of Se vacancies and InCu defects, surface (interface) states as well as positive interface discharges and Cu migration etc. A high efficiency CIGS solar cell of 14.4% is achieved. The optimized solar cell of 17.2% with a MgF2 anti-reflective layer has been obtained.
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Stochastic annealing simulations of defect interactions among subcascades
DOE Office of Scientific and Technical Information (OSTI.GOV)
Heinisch, H.L.; Singh, B.N.
1997-04-01
The effects of the subcascade structure of high energy cascades on the temperature dependencies of annihilation, clustering and free defect production are investigated. The subcascade structure is simulated by closely spaced groups of lower energy MD cascades. The simulation results illustrate the strong influence of the defect configuration existing in the primary damage state on subsequent intracascade evolution. Other significant factors affecting the evolution of the defect distribution are the large differences in mobility and stability of vacancy and interstitial defects and the rapid one-dimensional diffusion of small, glissile interstitial loops produced directly in cascades. Annealing simulations are also performedmore » on high-energy, subcascade-producing cascades generated with the binary collision approximation and calibrated to MD results.« less
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Multicompare tests of the performance of different metaheuristics in EEG dipole source localization.
Escalona-Vargas, Diana Irazú; Lopez-Arevalo, Ivan; Gutiérrez, David
2014-01-01
We study the use of nonparametric multicompare statistical tests on the performance of simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), when used for electroencephalographic (EEG) source localization. Such task can be posed as an optimization problem for which the referred metaheuristic methods are well suited. Hence, we evaluate the localization's performance in terms of metaheuristics' operational parameters and for a fixed number of evaluations of the objective function. In this way, we are able to link the efficiency of the metaheuristics with a common measure of computational cost. Our results did not show significant differences in the metaheuristics' performance for the case of single source localization. In case of localizing two correlated sources, we found that PSO (ring and tree topologies) and DE performed the worst, then they should not be considered in large-scale EEG source localization problems. Overall, the multicompare tests allowed to demonstrate the little effect that the selection of a particular metaheuristic and the variations in their operational parameters have in this optimization problem.
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NASA Astrophysics Data System (ADS)
Xiao, Ying; Michalski, Darek; Censor, Yair; Galvin, James M.
2004-07-01
The efficient delivery of intensity modulated radiation therapy (IMRT) depends on finding optimized beam intensity patterns that produce dose distributions, which meet given constraints for the tumour as well as any critical organs to be spared. Many optimization algorithms that are used for beamlet-based inverse planning are susceptible to large variations of neighbouring intensities. Accurately delivering an intensity pattern with a large number of extrema can prove impossible given the mechanical limitations of standard multileaf collimator (MLC) delivery systems. In this study, we apply Cimmino's simultaneous projection algorithm to the beamlet-based inverse planning problem, modelled mathematically as a system of linear inequalities. We show that using this method allows us to arrive at a smoother intensity pattern. Including nonlinear terms in the simultaneous projection algorithm to deal with dose-volume histogram (DVH) constraints does not compromise this property from our experimental observation. The smoothness properties are compared with those from other optimization algorithms which include simulated annealing and the gradient descent method. The simultaneous property of these algorithms is ideally suited to parallel computing technologies.
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Yin, Changchuan
2015-04-01
To apply digital signal processing (DSP) methods to analyze DNA sequences, the sequences first must be specially mapped into numerical sequences. Thus, effective numerical mappings of DNA sequences play key roles in the effectiveness of DSP-based methods such as exon prediction. Despite numerous mappings of symbolic DNA sequences to numerical series, the existing mapping methods do not include the genetic coding features of DNA sequences. We present a novel numerical representation of DNA sequences using genetic codon context (GCC) in which the numerical values are optimized by simulation annealing to maximize the 3-periodicity signal to noise ratio (SNR). The optimized GCC representation is then applied in exon and intron prediction by Short-Time Fourier Transform (STFT) approach. The results show the GCC method enhances the SNR values of exon sequences and thus increases the accuracy of predicting protein coding regions in genomes compared with the commonly used 4D binary representation. In addition, this study offers a novel way to reveal specific features of DNA sequences by optimizing numerical mappings of symbolic DNA sequences.
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Fitting and Modeling in the ASC Data Analysis Environment
NASA Astrophysics Data System (ADS)
Doe, S.; Siemiginowska, A.; Joye, W.; McDowell, J.
As part of the AXAF Science Center (ASC) Data Analysis Environment, we will provide to the astronomical community a Fitting Application. We present a design of the application in this paper. Our design goal is to give the user the flexibility to use a variety of optimization techniques (Levenberg-Marquardt, maximum entropy, Monte Carlo, Powell, downhill simplex, CERN-Minuit, and simulated annealing) and fit statistics (chi (2) , Cash, variance, and maximum likelihood); our modular design allows the user easily to add their own optimization techniques and/or fit statistics. We also present a comparison of the optimization techniques to be provided by the Application. The high spatial and spectral resolutions that will be obtained with AXAF instruments require a sophisticated data modeling capability. We will provide not only a suite of astronomical spatial and spectral source models, but also the capability of combining these models into source models of up to four data dimensions (i.e., into source functions f(E,x,y,t)). We will also provide tools to create instrument response models appropriate for each observation.
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Design of focused and restrained subsets from extremely large virtual libraries.
Jamois, Eric A; Lin, Chien T; Waldman, Marvin
2003-11-01
With the current and ever-growing offering of reagents along with the vast palette of organic reactions, virtual libraries accessible to combinatorial chemists can reach sizes of billions of compounds or more. Extracting practical size subsets for experimentation has remained an essential step in the design of combinatorial libraries. A typical approach to computational library design involves enumeration of structures and properties for the entire virtual library, which may be unpractical for such large libraries. This study describes a new approach termed as on the fly optimization (OTFO) where descriptors are computed as needed within the subset optimization cycle and without intermediate enumeration of structures. Results reported herein highlight the advantages of coupling an ultra-fast descriptor calculation engine to subset optimization capabilities. We also show that enumeration of properties for the entire virtual library may not only be unpractical but also wasteful. Successful design of focused and restrained subsets can be achieved while sampling only a small fraction of the virtual library. We also investigate the stability of the method and compare results obtained from simulated annealing (SA) and genetic algorithms (GA).
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NASA Technical Reports Server (NTRS)
Lee, C. S. G.; Chen, C. L.
1989-01-01
Two efficient mapping algorithms for scheduling the robot inverse dynamics computation consisting of m computational modules with precedence relationship to be executed on a multiprocessor system consisting of p identical homogeneous processors with processor and communication costs to achieve minimum computation time are presented. An objective function is defined in terms of the sum of the processor finishing time and the interprocessor communication time. The minimax optimization is performed on the objective function to obtain the best mapping. This mapping problem can be formulated as a combination of the graph partitioning and the scheduling problems; both have been known to be NP-complete. Thus, to speed up the searching for a solution, two heuristic algorithms were proposed to obtain fast but suboptimal mapping solutions. The first algorithm utilizes the level and the communication intensity of the task modules to construct an ordered priority list of ready modules and the module assignment is performed by a weighted bipartite matching algorithm. For a near-optimal mapping solution, the problem can be solved by the heuristic algorithm with simulated annealing. These proposed optimization algorithms can solve various large-scale problems within a reasonable time. Computer simulations were performed to evaluate and verify the performance and the validity of the proposed mapping algorithms. Finally, experiments for computing the inverse dynamics of a six-jointed PUMA-like manipulator based on the Newton-Euler dynamic equations were implemented on an NCUBE/ten hypercube computer to verify the proposed mapping algorithms. Computer simulation and experimental results are compared and discussed.
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Optimization of Focusing by Strip and Pixel Arrays
DOE Office of Scientific and Technical Information (OSTI.GOV)
Burke, G J; White, D A; Thompson, C A
Professor Kevin Webb and students at Purdue University have demonstrated the design of conducting strip and pixel arrays for focusing electromagnetic waves [1, 2]. Their key point was to design structures to focus waves in the near field using full wave modeling and optimization methods for design. Their designs included arrays of conducting strips optimized with a downhill search algorithm and arrays of conducting and dielectric pixels optimized with the iterative direct binary search method. They used a finite element code for modeling. This report documents our attempts to duplicate and verify their results. We have modeled 2D conducting stripsmore » and both conducting and dielectric pixel arrays with moment method and FDTD codes to compare with Webb's results. New designs for strip arrays were developed with optimization by the downhill simplex method with simulated annealing. Strip arrays were optimized to focus an incident plane wave at a point or at two separated points and to switch between focusing points with a change in frequency. We also tried putting a line current source at the focus point for the plane wave to see how it would work as a directive antenna. We have not tried optimizing the conducting or dielectric pixel arrays, but modeled the structures designed by Webb with the moment method and FDTD to compare with the Purdue results.« less
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Preparation and Analysis of Platinum Thin Films for High Temperature Sensor Applications
NASA Technical Reports Server (NTRS)
Wrbanek, John D.; Laster, Kimala L. H.
2005-01-01
A study has been made of platinum thin films for application as high temperature resistive sensors. To support NASA Glenn Research Center s high temperature thin film sensor effort, a magnetron sputtering system was installed recently in the GRC Microsystems Fabrication Clean Room Facility. Several samples of platinum films were prepared using various system parameters to establish run conditions. These films were characterized with the intended application of being used as resistive sensing elements, either for temperature or strain measurement. The resistances of several patterned sensors were monitored to document the effect of changes in parameters of deposition and annealing. The parameters were optimized for uniformity and intrinsic strain. The evaporation of platinum via oxidation during annealing over 900 C was documented, and a model for the process developed. The film adhesion was explored on films annealed to 1000 C with various bondcoats on fused quartz and alumina. From this compiled data, a list of optimal parameters and characteristics determined for patterned platinum thin films is given.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Simimol, A.; Department of Physics, National Institute of Technology, Calicut 673601; Manikandanath, N. T.
Highly dense and c-axis oriented zinc oxide (ZnO) nanorods with hexagonal wurtzite facets were deposited on fluorine doped tin oxide coated glass substrates by a simple and cost-effective electrodeposition method at low bath temperature (80 °C). The as-grown samples were then annealed at various temperatures (T{sub A} = 100–500 °C) in different environments (e.g., zinc, oxygen, air, and vacuum) to understand their photoluminescence (PL) behavior in the ultra-violet (UV) and the visible regions. The PL results revealed that the as-deposited ZnO nanorods consisted of oxygen vacancy (V{sub O}), zinc interstitial (Zn{sub i}), and oxygen interstitial (O{sub i}) defects and these can be reduced significantlymore » by annealing in different environments at optimal annealing temperatures. However, the intensity of deep level emission increased for T{sub A} greater than the optimized values for the respective environments due to the introduction of various defect centers. For example, for T{sub A} ≥ 450 °C in the oxygen and air environments, the density of O{sub i} defects increased, whereas, the green emission associated with V{sub O} is dominant in the vacuum annealed (T{sub A} = 500 °C) ZnO nanorods. The UV peak red shifted after the post-growth annealing treatments in all the environments and the vacuum annealed sample exhibited highest UV peak intensity. The observations from the PL data are supported by the micro-Raman spectroscopy. The present study gives new insight into the origin of different defects that exist in the electrodeposited ZnO nanorods and how these defects can be precisely controlled in order to get the desired emissions for the opto-electronic applications.« less
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NASA Astrophysics Data System (ADS)
Tarsoly, Gergely; Pyo, Seungmoon
2018-06-01
We report the opto-electrical response of organic field-effect transistors based on a thin-film of a semiconducting diketopyrrolopyrrole (DPP) core, a popular building block for molecular semiconductors, and a polymeric gate dielectric. The thin-film of the DPP core was thermally annealed at different temperatures under N2 atmosphere to investigate the relationship between the annealing temperature and the electrical properties of the device. The results showed that the annealing process induces morphological changes in the thin film, and properly controlling the thermal annealing conditions can enhance the device performance. In addition, we also investigated in detail the photo-response behaviors by analyzing the responsivity (R) of the device with the optimally annealed DPP-core thin film under two light illumination conditions by considering the irradiance absorbed by the thin film instead of the total irradiance of the light source. We found that the proposed model could lead to a light-source-independent description of the photo-response behavior of the device, and which can be used for other applications.
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NASA Astrophysics Data System (ADS)
Zhang, Bo; Zhang, Weiyong; Zhu, Jian
2012-04-01
The transfer matrix method, based on plane wave theory, of multi-layer equivalent fluid is employed to evaluate the sound absorbing properties of two-layer-assembled and three-layer-assembled sintered fibrous sheets (generally regarded as a kind of compound absorber or structures). Two objective functions which are more suitable for the optimization of sound absorption properties of multi-layer absorbers within the wider frequency ranges are developed and the optimized results of using two objective functions are also compared with each other. It is found that using the two objective functions, especially the second one, may be more helpful to exert the sound absorbing properties of absorbers at lower frequencies to the best of their abilities. Then the calculation and optimization of sound absorption properties of multi-layer-assembled structures are performed by developing a simulated annealing genetic arithmetic program and using above-mentioned objective functions. Finally, based on the optimization in this work the thoughts of the gradient design over the acoustic parameters- the porosity, the tortuosity, the viscous and thermal characteristic lengths and the thickness of each samples- of porous metals are put forth and thereby some useful design criteria upon the acoustic parameters of each layer of porous fibrous metals are given while applying the multi-layer-assembled compound absorbers in noise control engineering.
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A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM
Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei
2018-01-01
Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942
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Optimizing Requirements Decisions with KEYS
NASA Technical Reports Server (NTRS)
Jalali, Omid; Menzies, Tim; Feather, Martin
2008-01-01
Recent work with NASA's Jet Propulsion Laboratory has allowed for external access to five of JPL's real-world requirements models, anonymized to conceal proprietary information, but retaining their computational nature. Experimentation with these models, reported herein, demonstrates a dramatic speedup in the computations performed on them. These models have a well defined goal: select mitigations that retire risks which, in turn, increases the number of attainable requirements. Such a non-linear optimization is a well-studied problem. However identification of not only (a) the optimal solution(s) but also (b) the key factors leading to them is less well studied. Our technique, called KEYS, shows a rapid way of simultaneously identifying the solutions and their key factors. KEYS improves on prior work by several orders of magnitude. Prior experiments with simulated annealing or treatment learning took tens of minutes to hours to terminate. KEYS runs much faster than that; e.g for one model, KEYS ran 13,000 times faster than treatment learning (40 minutes versus 0.18 seconds). Processing these JPL models is a non-linear optimization problem: the fewest mitigations must be selected while achieving the most requirements. Non-linear optimization is a well studied problem. With this paper, we challenge other members of the PROMISE community to improve on our results with other techniques.
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Keresztes, Janos C; John Koshel, R; D'huys, Karlien; De Ketelaere, Bart; Audenaert, Jan; Goos, Peter; Saeys, Wouter
2016-12-26
A novel meta-heuristic approach for minimizing nonlinear constrained problems is proposed, which offers tolerance information during the search for the global optimum. The method is based on the concept of design and analysis of computer experiments combined with a novel two phase design augmentation (DACEDA), which models the entire merit space using a Gaussian process, with iteratively increased resolution around the optimum. The algorithm is introduced through a series of cases studies with increasing complexity for optimizing uniformity of a short-wave infrared (SWIR) hyperspectral imaging (HSI) illumination system (IS). The method is first demonstrated for a two-dimensional problem consisting of the positioning of analytical isotropic point sources. The method is further applied to two-dimensional (2D) and five-dimensional (5D) SWIR HSI IS versions using close- and far-field measured source models applied within the non-sequential ray-tracing software FRED, including inherent stochastic noise. The proposed method is compared to other heuristic approaches such as simplex and simulated annealing (SA). It is shown that DACEDA converges towards a minimum with 1 % improvement compared to simplex and SA, and more importantly requiring only half the number of simulations. Finally, a concurrent tolerance analysis is done within DACEDA for to the five-dimensional case such that further simulations are not required.
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Direct aperture optimization: a turnkey solution for step-and-shoot IMRT.
Shepard, D M; Earl, M A; Li, X A; Naqvi, S; Yu, C
2002-06-01
IMRT treatment plans for step-and-shoot delivery have traditionally been produced through the optimization of intensity distributions (or maps) for each beam angle. The optimization step is followed by the application of a leaf-sequencing algorithm that translates each intensity map into a set of deliverable aperture shapes. In this article, we introduce an automated planning system in which we bypass the traditional intensity optimization, and instead directly optimize the shapes and the weights of the apertures. We call this approach "direct aperture optimization." This technique allows the user to specify the maximum number of apertures per beam direction, and hence provides significant control over the complexity of the treatment delivery. This is possible because the machine dependent delivery constraints imposed by the MLC are enforced within the aperture optimization algorithm rather than in a separate leaf-sequencing step. The leaf settings and the aperture intensities are optimized simultaneously using a simulated annealing algorithm. We have tested direct aperture optimization on a variety of patient cases using the EGS4/BEAM Monte Carlo package for our dose calculation engine. The results demonstrate that direct aperture optimization can produce highly conformal step-and-shoot treatment plans using only three to five apertures per beam direction. As compared with traditional optimization strategies, our studies demonstrate that direct aperture optimization can result in a significant reduction in both the number of beam segments and the number of monitor units. Direct aperture optimization therefore produces highly efficient treatment deliveries that maintain the full dosimetric benefits of IMRT.
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An optimized proportional-derivative controller for the human upper extremity with gravity.
Jagodnik, Kathleen M; Blana, Dimitra; van den Bogert, Antonie J; Kirsch, Robert F
2015-10-15
When Functional Electrical Stimulation (FES) is used to restore movement in subjects with spinal cord injury (SCI), muscle stimulation patterns should be selected to generate accurate and efficient movements. Ideally, the controller for such a neuroprosthesis will have the simplest architecture possible, to facilitate translation into a clinical setting. In this study, we used the simulated annealing algorithm to optimize two proportional-derivative (PD) feedback controller gain sets for a 3-dimensional arm model that includes musculoskeletal dynamics and has 5 degrees of freedom and 22 muscles, performing goal-oriented reaching movements. Controller gains were optimized by minimizing a weighted sum of position errors, orientation errors, and muscle activations. After optimization, gain performance was evaluated on the basis of accuracy and efficiency of reaching movements, along with three other benchmark gain sets not optimized for our system, on a large set of dynamic reaching movements for which the controllers had not been optimized, to test ability to generalize. Robustness in the presence of weakened muscles was also tested. The two optimized gain sets were found to have very similar performance to each other on all metrics, and to exhibit significantly better accuracy, compared with the three standard gain sets. All gain sets investigated used physiologically acceptable amounts of muscular activation. It was concluded that optimization can yield significant improvements in controller performance while still maintaining muscular efficiency, and that optimization should be considered as a strategy for future neuroprosthesis controller design. Published by Elsevier Ltd.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Lin, Y.; Kessler, T.J.; Lawrence, G.N.
1996-10-01
High-performance phase plates are of vital concern for controlling the far-field irradiance of laser-fusion systems. Several designs for solving this difficult problem have been reported in {ital Optics} {ital Letters} [e.g., S. N. Dixit {ital et} {ital al}., Opt. Lett. {bold 19}, 417 (1994)]. We report a surface-based form of simulated annealing that significantly improves the irradiance control while eliminating the high-scatter problems that have plagued other methods. {copyright} {ital 1996 Optical Society of America.}
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Chintapalli, Mahati; Higa, Kenneth; Chen, X. Chelsea; ...
2016-12-19
A method is presented in this paper to relate local morphology and ionic conductivity in a solid, lamellar block copolymer electrolyte for lithium batteries, by simulating conductivity through transmission electron micrographs. The electrolyte consists of polystyrene-block-poly(ethylene oxide) mixed with lithium bis(trifluoromethanesulfonyl) imide salt (SEO/LiTFSI), where the polystyrene phase is structural phase and the poly(ethylene oxide)/LiTFSI phase is ionically conductive. The electric potential distribution is simulated in binarized micrographs by solving the Laplace equation with constant potential boundary conditions. A morphology factor, f, is reported for each image by calculating the effective conductivity relative to a homogenous conductor. Images from twomore » samples are examined, one annealed with large lamellar grains and one unannealed with small grains. The average value off is 0.45 ± 0.04 for the annealed sample, and 0.37 ± 0.03 for the unannealed sample, both close to the value predicted by effective medium theory, 1/2. Simulated conductivities are compared to published experimental conductivities. The value of f Unannealed/f Annealed is 0.82 for simulations and 6.2 for experiments. Simulation results correspond well to predictions by effective medium theory but do not explain the experimental measurements. Finally, observation of nanoscale morphology over length scales greater than the size of the micrographs (~1 μm) may be required to explain the experimental results.« less
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Chintapalli, Mahati; Higa, Kenneth; Chen, X. Chelsea
A method is presented in this paper to relate local morphology and ionic conductivity in a solid, lamellar block copolymer electrolyte for lithium batteries, by simulating conductivity through transmission electron micrographs. The electrolyte consists of polystyrene-block-poly(ethylene oxide) mixed with lithium bis(trifluoromethanesulfonyl) imide salt (SEO/LiTFSI), where the polystyrene phase is structural phase and the poly(ethylene oxide)/LiTFSI phase is ionically conductive. The electric potential distribution is simulated in binarized micrographs by solving the Laplace equation with constant potential boundary conditions. A morphology factor, f, is reported for each image by calculating the effective conductivity relative to a homogenous conductor. Images from twomore » samples are examined, one annealed with large lamellar grains and one unannealed with small grains. The average value off is 0.45 ± 0.04 for the annealed sample, and 0.37 ± 0.03 for the unannealed sample, both close to the value predicted by effective medium theory, 1/2. Simulated conductivities are compared to published experimental conductivities. The value of f Unannealed/f Annealed is 0.82 for simulations and 6.2 for experiments. Simulation results correspond well to predictions by effective medium theory but do not explain the experimental measurements. Finally, observation of nanoscale morphology over length scales greater than the size of the micrographs (~1 μm) may be required to explain the experimental results.« less
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Stochastic Optimization for Nuclear Facility Deployment Scenarios
NASA Astrophysics Data System (ADS)
Hays, Ross Daniel
Single-use, low-enriched uranium oxide fuel, consumed through several cycles in a light-water reactor (LWR) before being disposed, has become the dominant source of commercial-scale nuclear electric generation in the United States and throughout the world. However, it is not without its drawbacks and is not the only potential nuclear fuel cycle available. Numerous alternative fuel cycles have been proposed at various times which, through the use of different reactor and recycling technologies, offer to counteract many of the perceived shortcomings with regards to waste management, resource utilization, and proliferation resistance. However, due to the varying maturity levels of these technologies, the complicated material flow feedback interactions their use would require, and the large capital investments in the current technology, one should not deploy these advanced designs without first investigating the potential costs and benefits of so doing. As the interactions among these systems can be complicated, and the ways in which they may be deployed are many, the application of automated numerical optimization to the simulation of the fuel cycle could potentially be of great benefit to researchers and interested policy planners. To investigate the potential of these methods, a computational program has been developed that applies a parallel, multi-objective simulated annealing algorithm to a computational optimization problem defined by a library of relevant objective functions applied to the Ver ifiable Fuel Cycle Simulati on Model (VISION, developed at the Idaho National Laboratory). The VISION model, when given a specified fuel cycle deployment scenario, computes the numbers and types of, and construction, operation, and utilization schedules for, the nuclear facilities required to meet a predetermined electric power demand function. Additionally, it calculates the location and composition of the nuclear fuels within the fuel cycle, from initial mining through to eventual disposal. By varying the specifications of the deployment scenario, the simulated annealing algorithm will seek to either minimize the value of a single objective function, or enumerate the trade-off surface between multiple competing objective functions. The available objective functions represent key stakeholder values, minimizing such important factors as high-level waste disposal burden, required uranium ore supply, relative proliferation potential, and economic cost and uncertainty. The optimization program itself is designed to be modular, allowing for continued expansion and exploration as research needs and curiosity indicate. The utility and functionality of this optimization program are demonstrated through its application to one potential fuel cycle scenario of interest. In this scenario, an existing legacy LWR fleet is assumed at the year 2000. The electric power demand grows exponentially at a rate of 1.8% per year through the year 2100. Initially, new demand is met by the construction of 1-GW(e) LWRs. However, beginning in the year 2040, 600-MW(e) sodium-cooled, fast-spectrum reactors operating in a transuranic burning regime with full recycling of spent fuel become available to meet demand. By varying the fraction of new capacity allocated to each reactor type, the optimization program is able to explicitly show the relationships that exist between uranium utilization, long-term heat for geologic disposal, and cost-of-electricity objective functions. The trends associated with these trade-off surfaces tend to confirm many common expectations about the use of nuclear power, namely that while overall it is quite insensitive to variations in the cost of uranium ore, it is quite sensitive to changes in the capital costs of facilities. The optimization algorithm has shown itself to be robust and extensible, with possible extensions to many further fuel cycle optimization problems of interest.
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Self-assembly of single-wall carbon nanotubes during the cooling process of hot carbon gas.
Wen, Yushi; Zheng, Ke; Long, Xinping; Li, Ming; Xue, Xianggui; Dai, Xiaogan; Deng, Chuan
2018-04-25
In this work, self-assembly mechanism of single-wall carbon nanotube (SWCNT) during the annealing process of hot gaseous carbon is presented using reactive force field (ReaxFF)-based reactive molecular simulations. A series of simulations were performed on the evolution of reactive carbon gas. The simulation results show that the reactive carbon gas can be assembled into regular SWCNT without a catalyst. Five distinct stages of SWCNT self-assembly are proposed. For some initial configurations, the CNT was found to spin at an ultra-high rate after the nucleation. Graphical abstract Self-assembly process of single-wall carbon nanotube from the annealing of hot gaseous carbon.
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NASA Astrophysics Data System (ADS)
Ali, H. M.; Abd El-Raheem, M. M.; Megahed, N. M.; Mohamed, H. A.
2006-08-01
Aluminum-doped zinc oxide (AZO) thin films have been deposited by electron beam evaporation technique on glass substrates. The structural, electrical and optical properties of AZO films have been investigated as a function of annealing temperature. It was observed that the optical properties such as transmittance, reflectance, optical band gap and refractive index of AZO films were strongly affected by annealing temperature. The transmittance values of 84% in the visible region and 97% in the NIR region were obtained for AZO film annealed at 475 °C. The room temperature electrical resistivity of 4.6×10-3 Ω cm has been obtained at the same temperature of annealing. It was found that the calculated refractive index has been affected by the packing density of the thin films, whereas, the high annealing temperature gave rise to improve the homogeneity of the films. The single-oscillator model was used to analyze the optical parameters such as the oscillator and dispersion energies.
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Preparation of clean surfaces and Se vacancy formation in Bi2Se3 by ion bombardment and annealing
NASA Astrophysics Data System (ADS)
Zhou, Weimin; Zhu, Haoshan; Valles, Connie M.; Yarmoff, Jory A.
2017-08-01
Bismuth Selenide (Bi2Se3) is a topological insulator (TI) with a structure consisting of stacked quintuple layers. Single crystal surfaces are commonly prepared by mechanical cleaving. This work explores the use of low energy Ar+ ion bombardment and annealing (IBA) as an alternative method to produce reproducible and stable Bi2Se3 surfaces under ultra-high vacuum (UHV). It is found that a clean and well-ordered surface can be prepared by a single cycle of 1 keV Ar+ ion bombardment and 30 min of annealing. Low energy electron diffraction (LEED) and detailed low energy ion scattering (LEIS) measurements show no differences between IBA-prepared surfaces and those prepared by in situ cleaving in UHV. Analysis of the LEED patterns shows that the optimal annealing temperature is 450 °C. Angular LEIS scans reveal the formation of surface Se vacancies when the annealing temperature exceeds 520 °C.
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Qiu, Xiaofeng; Chen, Ling; Gong, Haibo; Zhu, Min; Han, Jun; Zi, Min; Yang, Xiaopeng; Ji, Changjian; Cao, Bingqiang
2014-09-15
Arrays of ZnO/CdS/CdSe core/shell nanocables with different annealing temperatures have been investigated for CdS/CdSe quantum dots sensitized solar cells (QDSSCs). CdS/CdSe quantum dots were synthesized on the surface of ZnO nanorods that serve as the scaffold via a simple ion-exchange approach. The uniform microstructure was verified by scanning electron microscope and transmission electron microscope. UV-Visible absorption spectrum and Raman spectroscopy analysis indicated noticeable influence of annealing temperature on the interface structural and optical properties of the CdS/CdSe layers. Particularly, the relationship between annealing temperatures and photovoltaic performance of the corresponding QDSSCs was investigated employing photovoltaic conversion, quantum efficiency and electrochemical impedance spectra. It is demonstrated that higher cell efficiency can be obtained by optimizing the annealing temperature through extending the photoresponse range and improving QD layer crystal quality. Copyright © 2014 Elsevier Inc. All rights reserved.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Yamaguchi, Hisato; Ogawa, Shuichi; Watanabe, Daiki
We report valence band electronic structure evolution of graphene oxide (GO) upon its thermal reduction. Degree of oxygen functionalization was controlled by annealing temperatures, and an electronic structure evolution was monitored using real-time ultraviolet photoelectron spectroscopy. We observed a drastic increase in density of states around the Fermi level upon thermal annealing at ~600 °C. The result indicates that while there is an apparent band gap for GO prior to a thermal reduction, the gap closes after an annealing around that temperature. This trend of band gap closure was correlated with electrical, chemical, and structural properties to determine a setmore » of GO material properties that is optimal for optoelectronics. The results revealed that annealing at a temperature of ~500 °C leads to the desired properties, demonstrated by a uniform and an order of magnitude enhanced photocurrent map of an individual GO sheet compared to as-synthesized counterpart.« less
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Yamaguchi, Hisato; Ogawa, Shuichi; Watanabe, Daiki; ...
2016-09-01
We report valence band electronic structure evolution of graphene oxide (GO) upon its thermal reduction. Degree of oxygen functionalization was controlled by annealing temperatures, and an electronic structure evolution was monitored using real-time ultraviolet photoelectron spectroscopy. We observed a drastic increase in density of states around the Fermi level upon thermal annealing at ~600 °C. The result indicates that while there is an apparent band gap for GO prior to a thermal reduction, the gap closes after an annealing around that temperature. This trend of band gap closure was correlated with electrical, chemical, and structural properties to determine a setmore » of GO material properties that is optimal for optoelectronics. The results revealed that annealing at a temperature of ~500 °C leads to the desired properties, demonstrated by a uniform and an order of magnitude enhanced photocurrent map of an individual GO sheet compared to as-synthesized counterpart.« less
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Effect of annealing on optical properties and structure of the vanadium dioxide thin films
NASA Astrophysics Data System (ADS)
Zhu, Huiqun; Li, Yi; Li, Yuming; Huang, Yize; Tong, Guoxiang; Fang, Baoying; Zheng, Qiuxin; Li, Liu; Shen, Yujian
2012-10-01
VO2 thin films were prepared on soda-lime glass substrates by DC magnetron sputtering at room temperature using vanadium target and post annealing in air. X-ray diffraction and FTIR spectroscopy analyses showed that the films obtained at the optimized parameters have high VO2 (011) orientation. Both low temperature deposition and post annealing method were beneficial to grow the nano-films with pure VO2 phase-structure and composition. Metalinsulator transition properties of the VO2 films in terms of infrared transmittance, transmittance variation and film thickness were investigated under varying annealing temperature. Results showed that infrared transmittance variation and transition temperature of the nano-films were significantly improved and reduced respectively. Therefore, this study was able to develop practical low-cost preparation methods for high-performance intelligent energy-saving thin films.
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NASA Astrophysics Data System (ADS)
Gurovich, B. A.; Kuleshova, E. A.; Frolov, A. S.; Maltsev, D. A.; Prikhodko, K. E.; Fedotova, S. V.; Margolin, B. Z.; Sorokin, A. A.
2015-10-01
A complex study of structural state and properties of 18Cr-10Ni-Ti austenitic stainless steel after irradiation in BOR-60 fast research reactor (in the temperature range 330-400 °С up to damaging doses of 145 dpa) and in VVER-1000 light water reactor (at temperature ∼320 °С and damaging doses ∼12-14 dpa) was performed. The possibility of recovery of structural-phase state and mechanical properties to the level almost corresponding to the initial state by the recovery annealing was studied. The principal possibility of the recovery annealing of pressurized water reactor internals that ensures almost complete recovery of its mechanical properties and microstructure was shown. The optimal mode of recovery annealing was established: 1000 °C during 120 h.
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Parameterization of annealing kinetics in pharmaceutical glasses.
Hodge, Ian M
2013-07-01
Numerical simulations indicate that neglecting the canonical nonlinearity of glassy-state annealing kinetics in pharmaceutical (and other) glasses leads to good KWW fits to the dependence of enthalpy on annealing time, but with spurious KWW parameters that are affected by nonlinearity. A simplified treatment of nonlinearity that uses the Struik shift factor is found to be a useful approximation for these analyses, and can account for previously reported differences between linear and nonlinear KWW parameters (Kawakami K, Pikal MJ. 2005. J Pharm Sci 94:948-965). Copyright © 2013 Wiley Periodicals, Inc.
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Yang, Chih-Cheng; Liu, Chang-Lun
2016-08-12
Cold forging is often applied in the fastener industry. Wires in coil form are used as semi-finished products for the production of billets. This process usually requires preliminarily drawing wire coil in order to reduce the diameter of products. The wire usually has to be annealed to improve its cold formability. The quality of spheroidizing annealed wire affects the forming quality of screws. In the fastener industry, most companies use a subcritical process for spheroidized annealing. Various parameters affect the spheroidized annealing quality of steel wire, such as the spheroidized annealing temperature, prolonged heating time, furnace cooling time and flow rate of nitrogen (protective atmosphere). The effects of the spheroidized annealing parameters affect the quality characteristics of steel wire, such as the tensile strength and hardness. A series of experimental tests on AISI 1022 low carbon steel wire are carried out and the Taguchi method is used to obtain optimum spheroidized annealing conditions to improve the mechanical properties of steel wires for cold forming. The results show that the spheroidized annealing temperature and prolonged heating time have the greatest effect on the mechanical properties of steel wires. A comparison between the results obtained using the optimum spheroidizing conditions and the measures using the original settings shows the new spheroidizing parameter settings effectively improve the performance measures over their value at the original settings. The results presented in this paper could be used as a reference for wire manufacturers.
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Non-stoquastic Hamiltonians in quantum annealing via geometric phases
NASA Astrophysics Data System (ADS)
Vinci, Walter; Lidar, Daniel A.
2017-09-01
We argue that a complete description of quantum annealing implemented with continuous variables must take into account the non-adiabatic Aharonov-Anandan geometric phase that arises when the system Hamiltonian changes during the anneal. We show that this geometric effect leads to the appearance of non-stoquasticity in the effective quantum Ising Hamiltonians that are typically used to describe quantum annealing with flux qubits. We explicitly demonstrate the effect of this geometric non-stoquasticity when quantum annealing is performed with a system of one and two coupled flux qubits. The realization of non-stoquastic Hamiltonians has important implications from a computational complexity perspective, since it is believed that in many cases quantum annealing with stoquastic Hamiltonians can be efficiently simulated via classical algorithms such as Quantum Monte Carlo. It is well known that the direct implementation of non-stoquastic Hamiltonians with flux qubits is particularly challenging. Our results suggest an alternative path for the implementation of non-stoquasticity via geometric phases that can be exploited for computational purposes.
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Formation Design Strategy for SCOPE High-Elliptic Formation Flying Mission
NASA Technical Reports Server (NTRS)
Tsuda, Yuichi
2007-01-01
The new formation design strategy using simulated annealing (SA) optimization is presented. The SA algorithm is useful to survey a whole solution space of optimum formation, taking into account realistic constraints composed of continuous and discrete functions. It is revealed that this method is not only applicable for circular orbit, but also for high-elliptic orbit formation flying. The developed algorithm is first tested with a simple cart-wheel motion example, and then applied to the formation design for SCOPE. SCOPE is the next generation geomagnetotail observation mission planned in JAXA, utilizing a formation flying techonology in a high elliptic orbit. A distinctive and useful heuristics is found by investigating SA results, showing the effectiveness of the proposed design process.
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a New Hybrid Yin-Yang Swarm Optimization Algorithm for Uncapacitated Warehouse Location Problems
NASA Astrophysics Data System (ADS)
Heidari, A. A.; Kazemizade, O.; Hakimpour, F.
2017-09-01
Yin-Yang-pair optimization (YYPO) is one of the latest metaheuristic algorithms (MA) proposed in 2015 that tries to inspire the philosophy of balance between conflicting concepts. Particle swarm optimizer (PSO) is one of the first population-based MA inspired by social behaviors of birds. In spite of PSO, the YYPO is not a nature inspired optimizer. It has a low complexity and starts with only two initial positions and can produce more points with regard to the dimension of target problem. Due to unique advantages of these methodologies and to mitigate the immature convergence and local optima (LO) stagnation problems in PSO, in this work, a continuous hybrid strategy based on the behaviors of PSO and YYPO is proposed to attain the suboptimal solutions of uncapacitated warehouse location (UWL) problems. This efficient hierarchical PSO-based optimizer (PSOYPO) can improve the effectiveness of PSO on spatial optimization tasks such as the family of UWL problems. The performance of the proposed PSOYPO is verified according to some UWL benchmark cases. These test cases have been used in several works to evaluate the efficacy of different MA. Then, the PSOYPO is compared to the standard PSO, genetic algorithm (GA), harmony search (HS), modified HS (OBCHS), and evolutionary simulated annealing (ESA). The experimental results demonstrate that the PSOYPO can reveal a better or competitive efficacy compared to the PSO and other MA.
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NASA Astrophysics Data System (ADS)
Gupta, Manisha; Chowdhury, Fatema Rezwana; Barlage, Douglas; Tsui, Ying Yin
2013-03-01
In this work we present the optimization of zinc oxide (ZnO) film properties for a thin-film transistor (TFT) application. Thin films, 50±10 nm, of ZnO were deposited by Pulsed Laser Deposition (PLD) under a variety of growth conditions. The oxygen pressure, laser fluence, substrate temperature and annealing conditions were varied as a part of this study. Mobility and carrier concentration were the focus of the optimization. While room-temperature ZnO growths followed by air and oxygen annealing showed improvement in the (002) phase formation with a carrier concentration in the order of 1017-1018/cm3 with low mobility in the range of 0.01-0.1 cm2/V s, a Hall mobility of 8 cm2/V s and a carrier concentration of 5×1014/cm3 have been achieved on a relatively low temperature growth (250 °C) of ZnO. The low carrier concentration indicates that the number of defects have been reduced by a magnitude of nearly a 1000 as compared to the room-temperature annealed growths. Also, it was very clearly seen that for the (002) oriented films of ZnO a high mobility film is achieved.
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NASA Astrophysics Data System (ADS)
Ullah, Sana; De Matteis, Fabio; Davoli, Ivan
2017-11-01
Transparent conducting oxide films with optimized dopant molar ratio have been prepared with limited pre- and postdeposition annealing duration of 10 min. Multiple aluminum zinc oxide (AZO) layers were spin-coated on ordinary glass substrates. The predeposition consolidation temperature and dopant molar ratio were optimized for electrical conductivity and optical transparency. Next, a group of films were deposited on Corning glass substrates from precursor solutions with the optimized dopant ratio, followed by postdeposition rapid thermal annealing (RTA) at different temperatures and in controlled environments. The lowest resistivity of 10.1 × 10-3 Ω cm was obtained for films receiving RTA at 600°C for 10 min each in vacuum then in N2-5%H2 environment, while resistivity of 20.3 × 10-3 Ω cm was obtained for films subjected to RTA directly in N2-5%H2. Optical measurements revealed average total transmittance of about 85% in the visible region. A direct allowed transition bandgap was determined based on the absorption edge with a value slightly above 3.0 eV, within the typical range for semiconductors. RTA resulted in desorption of oxygen with enhanced carrier concentration and crystallinity, which increased the carrier mobility with decreased bulk resistivity while maintaining the required optical transparency.
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L10-Ordered Thin Films with High Perpendicular Magnetic Anisotropy for STT-MRAM Applications
NASA Astrophysics Data System (ADS)
Huang, Efrem Yuan-Fu
The objective of the research conducted herein was to develop L10-ordered materials and thin film stack structures with high perpendicular magnetic anisotropy (PMA) for spin-transfertorque magnetoresistive random access memory (STT-MRAM) applications. A systematic approach was taken in this dissertation, culminating in exchange coupled L1 0-FePt and L10- MnAl heterogeneous structures showing great promise for developing perpendicular magnetic tunnel junctions (pMTJs) with both high thermal stability and low critical switching current. First, using MgO underlayers on Si substrates, sputtered MnAl films were systematically optimized, ultimately producing a Si substrate/MgO (20 nm)/MnAl (30)/Ta (5) film stack with a high degree of ordering and large PMA. Next, noting the incompatibility of insulating MgO underlayers with industrial-scale CMOS processes, attention was turned to using conductive underlayers. TiN was found to excel at promoting growth of L10-MnAl, with optimized films showing improved magnetic properties over those fabricated on MgO underlayers. The use of different post-annealing processes was then studied as an alternative to in situ annealing. Rapid thermal annealing (RTA) was found to produce PMA in films at lower annealing temperatures than tube furnace annealing, but tube furnace annealing produced films with higher maximum PMA than RTA. While annealed samples had lower surface roughness than those ordered by high in situ deposition temperatures, relying solely on annealing to achieve L10-ordering resulted drastically reduced PMA. Finally, heterogeneous L10-ordered FePt/MgO/MnAl film stacks were explored for pMTJs. Film stacks with MgO barrier layers thinner than 2 nm showed significant interdiffusion between the FePt and MnAl, while film stacks with thicker MgO barrier layers exhibited good ordering and high PMA in both the FePt and MnAl films. It is believed that this limitation is caused by the roughness of the underlying FePt, which was thicker than 2 nm. Unfortunately, MgO barrier layers thinner than 2 nm are needed to make good MTJs. With further study, thin, continuous barriers may be achievable for high-PMA, L10- ordered materials with more materials exploration, deposition optimization, and more advanced thin film processing techniques and fabrication equipment. Use of appropriate underlayers, capping layers, dopant elements, and improved fabrication techniques may help reduce surface roughness while preserving PMA. If smooth electrodes can be developed, the heterogeneous structures discussed have great potential in taking advantage of exchange coupling for developing pMTJs with both high thermal stability and low critical switching current. (Abstract shortened by ProQuest.).
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RNA secondary structure prediction using soft computing.
Ray, Shubhra Sankar; Pal, Sankar K
2013-01-01
Prediction of RNA structure is invaluable in creating new drugs and understanding genetic diseases. Several deterministic algorithms and soft computing-based techniques have been developed for more than a decade to determine the structure from a known RNA sequence. Soft computing gained importance with the need to get approximate solutions for RNA sequences by considering the issues related with kinetic effects, cotranscriptional folding, and estimation of certain energy parameters. A brief description of some of the soft computing-based techniques, developed for RNA secondary structure prediction, is presented along with their relevance. The basic concepts of RNA and its different structural elements like helix, bulge, hairpin loop, internal loop, and multiloop are described. These are followed by different methodologies, employing genetic algorithms, artificial neural networks, and fuzzy logic. The role of various metaheuristics, like simulated annealing, particle swarm optimization, ant colony optimization, and tabu search is also discussed. A relative comparison among different techniques, in predicting 12 known RNA secondary structures, is presented, as an example. Future challenging issues are then mentioned.
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Optimum gradient material for a functionally graded dental implant using metaheuristic algorithms.
Sadollah, Ali; Bahreininejad, Ardeshir
2011-10-01
Despite dental implantation being a great success, one of the key issues facing it is a mismatch of mechanical properties between engineered and native biomaterials, which makes osseointegration and bone remodeling problematical. Functionally graded material (FGM) has been proposed as a potential upgrade to some conventional implant materials such as titanium for selection in prosthetic dentistry. The idea of an FGM dental implant is that the property would vary in a certain pattern to match the biomechanical characteristics required at different regions in the hosting bone. However, matching the properties does not necessarily guarantee the best osseointegration and bone remodeling. Little existing research has been reported on developing an optimal design of an FGM dental implant for promoting long-term success. Based upon remodeling results, metaheuristic algorithms such as the genetic algorithms (GAs) and simulated annealing (SA) have been adopted to develop a multi-objective optimal design for FGM implantation design. The results are compared with those in literature. Copyright © 2011 Elsevier Ltd. All rights reserved.
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Brain tumor segmentation in 3D MRIs using an improved Markov random field model
NASA Astrophysics Data System (ADS)
Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza
2011-10-01
Markov Random Field (MRF) models have been recently suggested for MRI brain segmentation by a large number of researchers. By employing Markovianity, which represents the local property, MRF models are able to solve a global optimization problem locally. But they still have a heavy computation burden, especially when they use stochastic relaxation schemes such as Simulated Annealing (SA). In this paper, a new 3D-MRF model is put forward to raise the speed of the convergence. Although, search procedure of SA is fairly localized and prevents from exploring the same diversity of solutions, it suffers from several limitations. In comparison, Genetic Algorithm (GA) has a good capability of global researching but it is weak in hill climbing. Our proposed algorithm combines SA and an improved GA (IGA) to optimize the solution which speeds up the computation time. What is more, this proposed algorithm outperforms the traditional 2D-MRF in quality of the solution.
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Optimal Bi-Objective Redundancy Allocation for Systems Reliability and Risk Management.
Govindan, Kannan; Jafarian, Ahmad; Azbari, Mostafa E; Choi, Tsan-Ming
2016-08-01
In the big data era, systems reliability is critical to effective systems risk management. In this paper, a novel multiobjective approach, with hybridization of a known algorithm called NSGA-II and an adaptive population-based simulated annealing (APBSA) method is developed to solve the systems reliability optimization problems. In the first step, to create a good algorithm, we use a coevolutionary strategy. Since the proposed algorithm is very sensitive to parameter values, the response surface method is employed to estimate the appropriate parameters of the algorithm. Moreover, to examine the performance of our proposed approach, several test problems are generated, and the proposed hybrid algorithm and other commonly known approaches (i.e., MOGA, NRGA, and NSGA-II) are compared with respect to four performance measures: 1) mean ideal distance; 2) diversification metric; 3) percentage of domination; and 4) data envelopment analysis. The computational studies have shown that the proposed algorithm is an effective approach for systems reliability and risk management.