Optimal design of minimum mean-square error noise reduction algorithms using the simulated annealing technique.

PubMed

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.

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.

Unraveling Quantum Annealers using Classical Hardness

PubMed Central

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

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.

Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem

PubMed Central

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

Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem.

PubMed

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.

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).

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.

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.

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.

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.

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.

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.

Conformational Space Annealing explained: A general optimization algorithm, with diverse applications

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.

An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities.

PubMed

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.

An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities

PubMed Central

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

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP.

PubMed

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.

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.

List-Based Simulated Annealing Algorithm for Traveling Salesman Problem.

PubMed

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.

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.

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.

Advantages of Unfair Quantum Ground-State Sampling.

PubMed

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.

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.

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.

Flight plan optimization

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.

Discrete Optimization of Electronic Hyperpolarizabilities in a Chemical Subspace

DTIC Science & Technology

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

[The utility boiler low NOx combustion optimization based on ANN and simulated annealing algorithm].

PubMed

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.

Angular filter refractometry analysis using simulated annealing.

PubMed

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.

An adaptive evolutionary multi-objective approach based on simulated annealing.

PubMed

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.

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

PubMed Central

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

List-Based Simulated Annealing Algorithm for Traveling Salesman Problem

PubMed Central

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

UAV Mission Planning under Uncertainty

DTIC Science & Technology

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

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.

Joint Optimization of Vertical Component Gravity and Seismic P-wave First Arrivals by Simulated Annealing

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.

Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

PubMed Central

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

Forecasting nonlinear chaotic time series with function expression method based on an improved genetic-simulated annealing algorithm.

PubMed

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.

Parallel computing of physical maps--a comparative study in SIMD and MIMD parallelism.

PubMed

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.

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.

An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation

PubMed Central

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

Comparison of particle swarm optimization and simulated annealing for locating additional boreholes considering combined variance minimization

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.

Spin Glass Patch Planting

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.

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.

Optimization for high-dose-rate brachytherapy of cervical cancer with adaptive simulated annealing and gradient descent.

PubMed

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.

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.

Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters.

PubMed

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.

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

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).

Scheduling of hybrid types of machines with two-machine flowshop as the first type and a single machine as the second type

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.

Two hybrid compaction algorithms for the layout optimization problem.

PubMed

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.

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.

Angular filter refractometry analysis using simulated annealing [An improved method for characterizing plasma density profiles using angular filter refractometry

DOE PAGES

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

Angular filter refractometry analysis using simulated annealing [An improved method for characterizing plasma density profiles using angular filter refractometry

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

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.

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.

A trajectory planning scheme for spacecraft in the space station environment. M.S. Thesis - University of California

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.

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.

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.

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.

Capacity improvement using simulation optimization approaches: A case study in the thermotechnology industry

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.

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.

Optimization of parameter values for complex pulse sequences by simulated annealing: application to 3D MP-RAGE imaging of the brain.

PubMed

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.

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.

Using and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generators

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.

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.

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.

Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine

PubMed Central

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

Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine.

PubMed

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.

Optimization of seasonal ARIMA models using differential evolution - simulated annealing (DESA) algorithm in forecasting dengue cases in Baguio City

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.

High-dose-rate prostate brachytherapy inverse planning on dose-volume criteria by simulated annealing.

PubMed

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.

Optical network unit placement in Fiber-Wireless (FiWi) access network by Moth-Flame optimization algorithm

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.

Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.

PubMed

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.

Simulated Annealing-based Optimal Proportional-Integral-Derivative (PID) Controller Design: A Case Study on Nonlinear Quadcopter Dynamics

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.

Finding Maximum Cliques on the D-Wave Quantum Annealer

DOE PAGES

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

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

Multi objective genetic algorithm to optimize the local heat treatment of a hardenable aluminum alloy

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).

Distributed Optimization

NASA Technical Reports Server (NTRS)

Macready, William; Wolpert, David

2005-01-01

We demonstrate a new framework for analyzing and controlling distributed systems, by solving constrained optimization problems with an algorithm based on that framework. The framework is ar. information-theoretic extension of conventional full-rationality game theory to allow bounded rational agents. The associated optimization algorithm is a game in which agents control the variables of the optimization problem. They do this by jointly minimizing a Lagrangian of (the probability distribution of) their joint state. The updating of the Lagrange parameters in that Lagrangian is a form of automated annealing, one that focuses the multi-agent system on the optimal pure strategy. We present computer experiments for the k-sat constraint satisfaction problem and for unconstrained minimization of NK functions.

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.

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.

Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations

PubMed Central

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

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.

Benchmarking the D-Wave Two

NASA Astrophysics Data System (ADS)

Job, Joshua; Wang, Zhihui; Rønnow, Troels; Troyer, Matthias; Lidar, Daniel

2014-03-01

We report on experimental work benchmarking the performance of the D-Wave Two programmable annealer on its native Ising problem, and a comparison to available classical algorithms. In this talk we will focus on the comparison with an algorithm originally proposed and implemented by Alex Selby. This algorithm uses dynamic programming to repeatedly optimize over randomly selected maximal induced trees of the problem graph starting from a random initial state. If one is looking for a quantum advantage over classical algorithms, one should compare to classical algorithms which are designed and optimized to maximally take advantage of the structure of the type of problem one is using for the comparison. In that light, this classical algorithm should serve as a good gauge for any potential quantum speedup for the D-Wave Two.

Experimental demonstration of a quantum annealing algorithm for the traveling salesman problem in a nuclear-magnetic-resonance quantum simulator

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.

Interactive optimization approach for optimal impulsive rendezvous using primer vector and evolutionary algorithms

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.

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.

On simulated annealing phase transitions in phylogeny reconstruction.

PubMed

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.

A fast and robust kinematic model for a 12 DoF hyper-redundant robot positioning: An optimization proposal

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.

Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line

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.

Guaranteed Discrete Energy Optimization on Large Protein Design Problems.

PubMed

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.

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.

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

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.

Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors.

PubMed

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.

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.

Evolutionary computation applied to the reconstruction of 3-D surface topography in the SEM.

PubMed

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.

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.

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

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.

Generalization and Parallelization of Messy Genetic Algorithms and Communication in Parallel Genetic Algorithms.

DTIC Science & Technology

1992-12-01

Dynamics and Free Energy Perturbation Methods." Reviews in Computational Chem- istry edited by Kenny B. Lipkowitz and Donald B. Boyd, chapter 8, 295-320...atomic motions during annealing, allows the search to probabilistically move in a locally non-optimal direction. The probability of doing so is...Network processors communicate via communication links. This type of communication is generally very slow relative to other processor activities

Alternative Zoning Scenarios for Regional Sustainable Land Use Controls in China: A Knowledge-Based Multiobjective Optimisation Model

PubMed Central

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

Evaluation of hybrid inverse planning and optimization (HIPO) algorithm for optimization in real-time, high-dose-rate (HDR) brachytherapy for prostate.

PubMed

Pokharel, Shyam; Rana, Suresh; Blikenstaff, Joseph; Sadeghi, Amir; Prestidge, Bradley

2013-07-08

The purpose of this study is to investigate the effectiveness of the HIPO planning and optimization algorithm for real-time prostate HDR brachytherapy. This study consists of 20 patients who underwent ultrasound-based real-time HDR brachytherapy of the prostate using the treatment planning system called Oncentra Prostate (SWIFT version 3.0). The treatment plans for all patients were optimized using inverse dose-volume histogram-based optimization followed by graphical optimization (GRO) in real time. The GRO is manual manipulation of isodose lines slice by slice. The quality of the plan heavily depends on planner expertise and experience. The data for all patients were retrieved later, and treatment plans were created and optimized using HIPO algorithm with the same set of dose constraints, number of catheters, and set of contours as in the real-time optimization algorithm. The HIPO algorithm is a hybrid because it combines both stochastic and deterministic algorithms. The stochastic algorithm, called simulated annealing, searches the optimal catheter distributions for a given set of dose objectives. The deterministic algorithm, called dose-volume histogram-based optimization (DVHO), optimizes three-dimensional dose distribution quickly by moving straight downhill once it is in the advantageous region of the search space given by the stochastic algorithm. The PTV receiving 100% of the prescription dose (V100) was 97.56% and 95.38% with GRO and HIPO, respectively. The mean dose (D(mean)) and minimum dose to 10% volume (D10) for the urethra, rectum, and bladder were all statistically lower with HIPO compared to GRO using the student pair t-test at 5% significance level. HIPO can provide treatment plans with comparable target coverage to that of GRO with a reduction in dose to the critical structures.

Optimization of cladding parameters for resisting corrosion on low carbon steels using simulated annealing algorithm

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.

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.

De novo protein structure prediction by dynamic fragment assembly and conformational space annealing.

PubMed

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.

Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors

PubMed Central

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

Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping

NASA Astrophysics Data System (ADS)

Balakrishnan, D.; Quan, C.; Tay, C. J.

2013-06-01

The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.

Inherent smoothness of intensity patterns for intensity modulated radiation therapy generated by simultaneous projection algorithms

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.

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.

Optimally Stopped Optimization

NASA Astrophysics Data System (ADS)

Vinci, Walter; Lidar, Daniel

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 the performance of a D-Wave 2X quantum annealer and the 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 between one to two orders of magnitude faster than the HFS solver.

Minimizing the Discrepancy between Simulated and Historical Failures in Turbine Engines: A Simulation-Based Optimization Method (Postprint)

DTIC Science & Technology

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

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.

a Comparison of Simulated Annealing, Genetic Algorithm and Particle Swarm Optimization in Optimal First-Order Design of Indoor Tls Networks

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.

Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization.

PubMed

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.

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.

Automatic Phase Picker for Local and Teleseismic Events Using Wavelet Transform and Simulated Annealing

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.

Exponentially-Biased Ground-State Sampling of Quantum Annealing Machines with Transverse-Field Driving Hamiltonians

NASA Technical Reports Server (NTRS)

Mandra, Salvatore

2017-01-01

We study the performance of the D-Wave 2X quantum annealing machine on systems with well-controlled ground-state degeneracy. While obtaining the ground state of a spin-glass benchmark instance represents a difficult task, the gold standard for any optimization algorithm or machine is to sample all solutions that minimize the Hamiltonian with more or less equal probability. Our results show that while naive transverse-field quantum annealing on the D-Wave 2X device can find the ground-state energy of the problems, it is not well suited in identifying all degenerate ground-state configurations associated to a particular instance. Even worse, some states are exponentially suppressed, in agreement with previous studies on toy model problems [New J. Phys. 11, 073021 (2009)]. These results suggest that more complex driving Hamiltonians are needed in future quantum annealing machines to ensure a fair sampling of the ground-state manifold.

Optimal Bi-Objective Redundancy Allocation for Systems Reliability and Risk Management.

PubMed

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.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

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.

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.

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.

A hybrid genetic-simulated annealing algorithm for the location-inventory-routing problem considering returns under e-supply chain environment.

PubMed

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.

Routing design and fleet allocation optimization of freeway service patrol: Improved results using genetic algorithm

NASA Astrophysics Data System (ADS)

Sun, Xiuqiao; Wang, Jian

2018-07-01

Freeway service patrol (FSP), is considered to be an effective method for incident management and can help transportation agency decision-makers alter existing route coverage and fleet allocation. This paper investigates the FSP problem of patrol routing design and fleet allocation, with the objective of minimizing the overall average incident response time. While the simulated annealing (SA) algorithm and its improvements have been applied to solve this problem, they often become trapped in local optimal solution. Moreover, the issue of searching efficiency remains to be further addressed. In this paper, we employ the genetic algorithm (GA) and SA to solve the FSP problem. To maintain population diversity and avoid premature convergence, niche strategy is incorporated into the traditional genetic algorithm. We also employ elitist strategy to speed up the convergence. Numerical experiments have been conducted with the help of the Sioux Falls network. Results show that the GA slightly outperforms the dual-based greedy (DBG) algorithm, the very large-scale neighborhood searching (VLNS) algorithm, the SA algorithm and the scenario algorithm.

Determining residual reduction algorithm kinematic tracking weights for a sidestep cut via numerical optimization.

PubMed

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.

Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems

PubMed Central

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

Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems.

PubMed

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.

A tabu search evalutionary algorithm for multiobjective optimization: Application to a bi-criterion aircraft structural reliability problem

NASA Astrophysics Data System (ADS)

Long, Kim Chenming

Real-world engineering optimization problems often require the consideration of multiple conflicting and noncommensurate objectives, subject to nonconvex constraint regions in a high-dimensional decision space. Further challenges occur for combinatorial multiobjective problems in which the decision variables are not continuous. Traditional multiobjective optimization methods of operations research, such as weighting and epsilon constraint methods, are ill-suited to solving these complex, multiobjective problems. This has given rise to the application of a wide range of metaheuristic optimization algorithms, such as evolutionary, particle swarm, simulated annealing, and ant colony methods, to multiobjective optimization. Several multiobjective evolutionary algorithms have been developed, including the strength Pareto evolutionary algorithm (SPEA) and the non-dominated sorting genetic algorithm (NSGA), for determining the Pareto-optimal set of non-dominated solutions. Although numerous researchers have developed a wide range of multiobjective optimization algorithms, there is a continuing need to construct computationally efficient algorithms with an improved ability to converge to globally non-dominated solutions along the Pareto-optimal front for complex, large-scale, multiobjective engineering optimization problems. This is particularly important when the multiple objective functions and constraints of the real-world system cannot be expressed in explicit mathematical representations. This research presents a novel metaheuristic evolutionary algorithm for complex multiobjective optimization problems, which combines the metaheuristic tabu search algorithm with the evolutionary algorithm (TSEA), as embodied in genetic algorithms. TSEA is successfully applied to bicriteria (i.e., structural reliability and retrofit cost) optimization of the aircraft tail structure fatigue life, which increases its reliability by prolonging fatigue life. A comparison for this application of the proposed algorithm, TSEA, with several state-of-the-art multiobjective optimization algorithms reveals that TSEA outperforms these algorithms by providing retrofit solutions with greater reliability for the same costs (i.e., closer to the Pareto-optimal front) after the algorithms are executed for the same number of generations. This research also demonstrates that TSEA competes with and, in some situations, outperforms state-of-the-art multiobjective optimization algorithms such as NSGA II and SPEA 2 when applied to classic bicriteria test problems in the technical literature and other complex, sizable real-world applications. The successful implementation of TSEA contributes to the safety of aeronautical structures by providing a systematic way to guide aircraft structural retrofitting efforts, as well as a potentially useful algorithm for a wide range of multiobjective optimization problems in engineering and other fields.

a New Multimodal Multi-Criteria Route Planning Model by Integrating a Fuzzy-Ahp Weighting Method and a Simulated Annealing Algorithm

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.

Continuous intensity map optimization (CIMO): A novel approach to leaf sequencing in step and shoot IMRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao Daliang; Earl, Matthew A.; Luan, Shuang

2006-04-15

A new leaf-sequencing approach has been developed that is designed to reduce the number of required beam segments for step-and-shoot intensity modulated radiation therapy (IMRT). This approach to leaf sequencing is called continuous-intensity-map-optimization (CIMO). Using a simulated annealing algorithm, CIMO seeks to minimize differences between the optimized and sequenced intensity maps. Two distinguishing features of the CIMO algorithm are (1) CIMO does not require that each optimized intensity map be clustered into discrete levels and (2) CIMO is not rule-based but rather simultaneously optimizes both the aperture shapes and weights. To test the CIMO algorithm, ten IMRT patient cases weremore » selected (four head-and-neck, two pancreas, two prostate, one brain, and one pelvis). For each case, the optimized intensity maps were extracted from the Pinnacle{sup 3} treatment planning system. The CIMO algorithm was applied, and the optimized aperture shapes and weights were loaded back into Pinnacle. A final dose calculation was performed using Pinnacle's convolution/superposition based dose calculation. On average, the CIMO algorithm provided a 54% reduction in the number of beam segments as compared with Pinnacle's leaf sequencer. The plans sequenced using the CIMO algorithm also provided improved target dose uniformity and a reduced discrepancy between the optimized and sequenced intensity maps. For ten clinical intensity maps, comparisons were performed between the CIMO algorithm and the power-of-two reduction algorithm of Xia and Verhey [Med. Phys. 25(8), 1424-1434 (1998)]. When the constraints of a Varian Millennium multileaf collimator were applied, the CIMO algorithm resulted in a 26% reduction in the number of segments. For an Elekta multileaf collimator, the CIMO algorithm resulted in a 67% reduction in the number of segments. An average leaf sequencing time of less than one minute per beam was observed.« less

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.

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.

A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems

NASA Astrophysics Data System (ADS)

Abtahi, Amir-Reza; Bijari, Afsane

2017-03-01

In this paper, a hybrid meta-heuristic algorithm, based on imperialistic competition algorithm (ICA), harmony search (HS), and simulated annealing (SA) is presented. The body of the proposed hybrid algorithm is based on ICA. The proposed hybrid algorithm inherits the advantages of the process of harmony creation in HS algorithm to improve the exploitation phase of the ICA algorithm. In addition, the proposed hybrid algorithm uses SA to make a balance between exploration and exploitation phases. The proposed hybrid algorithm is compared with several meta-heuristic methods, including genetic algorithm (GA), HS, and ICA on several well-known benchmark instances. The comprehensive experiments and statistical analysis on standard benchmark functions certify the superiority of the proposed method over the other algorithms. The efficacy of the proposed hybrid algorithm is promising and can be used in several real-life engineering and management problems.

A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment

PubMed Central

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

Optimization of the resources management in fighting wildfires.

PubMed

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.

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.

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).

Optimizing pulsed Nd:YAG laser beam welding process parameters to attain maximum ultimate tensile strength for thin AISI316L sheet using response surface methodology and simulated annealing algorithm

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.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

PubMed Central

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

Programming and Tuning a Quantum Annealing Device to Solve Real World Problems

NASA Astrophysics Data System (ADS)

Perdomo-Ortiz, Alejandro; O'Gorman, Bryan; Fluegemann, Joseph; Smelyanskiy, Vadim

2015-03-01

Solving real-world applications with quantum algorithms requires overcoming several challenges, ranging from translating the computational problem at hand to the quantum-machine language to tuning parameters of the quantum algorithm that have a significant impact on the performance of the device. In this talk, we discuss these challenges, strategies developed to enhance performance, and also a more efficient implementation of several applications. Although we will focus on applications of interest to NASA's Quantum Artificial Intelligence Laboratory, the methods and concepts presented here apply to a broader family of hard discrete optimization problems, including those that occur in many machine-learning algorithms.

XY vs X Mixer in Quantum Alternating Operator Ansatz for Optimization Problems with Constraints

NASA Technical Reports Server (NTRS)

Wang, Zhihui; Rubin, Nicholas; Rieffel, Eleanor G.

2018-01-01

Quantum Approximate Optimization Algorithm, further generalized as Quantum Alternating Operator Ansatz (QAOA), is a family of algorithms for combinatorial optimization problems. It is a leading candidate to run on emerging universal quantum computers to gain insight into quantum heuristics. In constrained optimization, penalties are often introduced so that the ground state of the cost Hamiltonian encodes the solution (a standard practice in quantum annealing). An alternative is to choose a mixing Hamiltonian such that the constraint corresponds to a constant of motion and the quantum evolution stays in the feasible subspace. Better performance of the algorithm is speculated due to a much smaller search space. We consider problems with a constant Hamming weight as the constraint. We also compare different methods of generating the generalized W-state, which serves as a natural initial state for the Hamming-weight constraint. Using graph-coloring as an example, we compare the performance of using XY model as a mixer that preserves the Hamming weight with the performance of adding a penalty term in the cost Hamiltonian.

SAGRAD: A Program for Neural Network Training with Simulated Annealing and the Conjugate Gradient Method.

PubMed

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.

Protein Loop Structure Prediction Using Conformational Space Annealing.

PubMed

Heo, Seungryong; Lee, Juyong; Joo, Keehyoung; Shin, Hang-Cheol; Lee, Jooyoung

2017-05-22

We have developed a protein loop structure prediction method by combining a new energy function, which we call E PLM (energy for protein loop modeling), with the conformational space annealing (CSA) global optimization algorithm. The energy function includes stereochemistry, dynamic fragment assembly, distance-scaled finite ideal gas reference (DFIRE), and generalized orientation- and distance-dependent terms. For the conformational search of loop structures, we used the CSA algorithm, which has been quite successful in dealing with various hard global optimization problems. We assessed the performance of E PLM with two widely used loop-decoy sets, Jacobson and RAPPER, and compared the results against the DFIRE potential. The accuracy of model selection from a pool of loop decoys as well as de novo loop modeling starting from randomly generated structures was examined separately. For the selection of a nativelike structure from a decoy set, E PLM was more accurate than DFIRE in the case of the Jacobson set and had similar accuracy in the case of the RAPPER set. In terms of sampling more nativelike loop structures, E PLM outperformed E DFIRE for both decoy sets. This new approach equipped with E PLM and CSA can serve as the state-of-the-art de novo loop modeling method.

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.

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.

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.

Mutually beneficial relationship in optimization between search-space smoothing and stochastic search

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.

Harmony Search as a Powerful Tool for Feature Selection in QSPR Study of the Drugs Lipophilicity.

PubMed

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.

SAGRAD: A Program for Neural Network Training with Simulated Annealing and the Conjugate Gradient Method

PubMed Central

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

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.

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.

A stochastic framework for spot-scanning particle therapy.

PubMed

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.

A cross-disciplinary introduction to quantum annealing-based algorithms

NASA Astrophysics Data System (ADS)

Venegas-Andraca, Salvador E.; Cruz-Santos, William; McGeoch, Catherine; Lanzagorta, Marco

2018-04-01

A central goal in quantum computing is the development of quantum hardware and quantum algorithms in order to analyse challenging scientific and engineering problems. Research in quantum computation involves contributions from both physics and computer science; hence this article presents a concise introduction to basic concepts from both fields that are used in annealing-based quantum computation, an alternative to the more familiar quantum gate model. We introduce some concepts from computer science required to define difficult computational problems and to realise the potential relevance of quantum algorithms to find novel solutions to those problems. We introduce the structure of quantum annealing-based algorithms as well as two examples of this kind of algorithms for solving instances of the max-SAT and Minimum Multicut problems. An overview of the quantum annealing systems manufactured by D-Wave Systems is also presented.

Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods.

PubMed

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.

A Multiobjective Interval Programming Model for Wind-Hydrothermal Power System Dispatching Using 2-Step Optimization Algorithm

PubMed Central

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

A multiobjective interval programming model for wind-hydrothermal power system dispatching using 2-step optimization algorithm.

PubMed

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.

Medical image diagnoses by artificial neural networks with image correlation, wavelet transform, simulated annealing

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.

Patch planting of hard spin-glass problems: Getting ready for the next generation of optimization approaches

NASA Astrophysics Data System (ADS)

Wang, Wenlong; Mandrà, Salvatore; Katzgraber, Helmut

We propose a patch planting heuristic that allows us to create arbitrarily-large Ising spin-glass instances on any topology and with any type of disorder, and where the exact ground-state energy of the problem is known by construction. By breaking up the problem into patches that can be treated either with exact or heuristic solvers, we can reconstruct the optimum of the original, considerably larger, problem. The scaling of the computational complexity of these instances with various patch numbers and sizes is investigated and compared with random instances using population annealing Monte Carlo and quantum annealing on the D-Wave 2X quantum annealer. The method can be useful for benchmarking of novel computing technologies and algorithms. NSF-DMR-1208046 and the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via MIT Lincoln Laboratory Air Force Contract No. FA8721-05-C-0002.

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.

An Optimization System with Parallel Processing for Reducing Common-Mode Current on Electronic Control Unit

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.

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.

Characterization of the probabilistic traveling salesman problem.

PubMed

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.

Hybrid optimization and Bayesian inference techniques for a non-smooth radiation detection problem

DOE PAGES

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

A novel constructive-optimizer neural network for the traveling salesman problem.

PubMed

Saadatmand-Tarzjan, Mahdi; Khademi, Morteza; Akbarzadeh-T, Mohammad-R; Moghaddam, Hamid Abrishami

2007-08-01

In this paper, a novel constructive-optimizer neural network (CONN) is proposed for the traveling salesman problem (TSP). CONN uses a feedback structure similar to Hopfield-type neural networks and a competitive training algorithm similar to the Kohonen-type self-organizing maps (K-SOMs). Consequently, CONN is composed of a constructive part, which grows the tour and an optimizer part to optimize it. In the training algorithm, an initial tour is created first and introduced to CONN. Then, it is trained in the constructive phase for adding a number of cities to the tour. Next, the training algorithm switches to the optimizer phase for optimizing the current tour by displacing the tour cities. After convergence in this phase, the training algorithm switches to the constructive phase anew and is continued until all cities are added to the tour. Furthermore, we investigate a relationship between the number of TSP cities and the number of cities to be added in each constructive phase. CONN was tested on nine sets of benchmark TSPs from TSPLIB to demonstrate its performance and efficiency. It performed better than several typical Neural networks (NNs), including KNIES_TSP_Local, KNIES_TSP_Global, Budinich's SOM, Co-Adaptive Net, and multivalued Hopfield network as wall as computationally comparable variants of the simulated annealing algorithm, in terms of both CPU time and accuracy. Furthermore, CONN converged considerably faster than expanding SOM and evolved integrated SOM and generated shorter tours compared to KNIES_DECOMPOSE. Although CONN is not yet comparable in terms of accuracy with some sophisticated computationally intensive algorithms, it converges significantly faster than they do. Generally speaking, CONN provides the best compromise between CPU time and accuracy among currently reported NNs for TSP.

A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes.

PubMed

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.

Efficiency of quantum vs. classical annealing in nonconvex learning problems

PubMed Central

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

Particle Swarm Optimization algorithms for geophysical inversion, practical hints

NASA Astrophysics Data System (ADS)

Garcia Gonzalo, E.; Fernandez Martinez, J.; Fernandez Alvarez, J.; Kuzma, H.; Menendez Perez, C.

2008-12-01

PSO is a stochastic optimization technique that has been successfully used in many different engineering fields. PSO algorithm can be physically interpreted as a stochastic damped mass-spring system (Fernandez Martinez and Garcia Gonzalo 2008). Based on this analogy we present a whole family of PSO algorithms and their respective first order and second order stability regions. Their performance is also checked using synthetic functions (Rosenbrock and Griewank) showing a degree of ill-posedness similar to that found in many geophysical inverse problems. Finally, we present the application of these algorithms to the analysis of a Vertical Electrical Sounding inverse problem associated to a seawater intrusion in a coastal aquifer in South Spain. We analyze the role of PSO parameters (inertia, local and global accelerations and discretization step), both in convergence curves and in the a posteriori sampling of the depth of an intrusion. Comparison is made with binary genetic algorithms and simulated annealing. As result of this analysis, practical hints are given to select the correct algorithm and to tune the corresponding PSO parameters. Fernandez Martinez, J.L., Garcia Gonzalo, E., 2008a. The generalized PSO: a new door to PSO evolution. Journal of Artificial Evolution and Applications. DOI:10.1155/2008/861275.

A soft computing-based approach to optimise queuing-inventory control problem

NASA Astrophysics Data System (ADS)

Alaghebandha, Mohammad; Hajipour, Vahid

2015-04-01

In this paper, a multi-product continuous review inventory control problem within batch arrival queuing approach (MQr/M/1) is developed to find the optimal quantities of maximum inventory. The objective function is to minimise summation of ordering, holding and shortage costs under warehouse space, service level and expected lost-sales shortage cost constraints from retailer and warehouse viewpoints. Since the proposed model is Non-deterministic Polynomial-time hard, an efficient imperialist competitive algorithm (ICA) is proposed to solve the model. To justify proposed ICA, both ganetic algorithm and simulated annealing algorithm are utilised. In order to determine the best value of algorithm parameters that result in a better solution, a fine-tuning procedure is executed. Finally, the performance of the proposed ICA is analysed using some numerical illustrations.

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.

Classical Optimal Control for Energy Minimization Based On Diffeomorphic Modulation under Observable-Response-Preserving Homotopy.

PubMed

Soley, Micheline B; Markmann, Andreas; Batista, Victor S

2018-06-12

We introduce the so-called "Classical Optimal Control Optimization" (COCO) method for global energy minimization based on the implementation of the diffeomorphic modulation under observable-response-preserving homotopy (DMORPH) gradient algorithm. A probe particle with time-dependent mass m( t;β) and dipole μ( r, t;β) is evolved classically on the potential energy surface V( r) coupled to an electric field E( t;β), as described by the time-dependent density of states represented on a grid, or otherwise as a linear combination of Gaussians generated by the k-means clustering algorithm. Control parameters β defining m( t;β), μ( r, t;β), and E( t;β) are optimized by following the gradients of the energy with respect to β, adapting them to steer the particle toward the global minimum energy configuration. We find that the resulting COCO algorithm is capable of resolving near-degenerate states separated by large energy barriers and successfully locates the global minima of golf potentials on flat and rugged surfaces, previously explored for testing quantum annealing methodologies and the quantum optimal control optimization (QuOCO) method. Preliminary results show successful energy minimization of multidimensional Lennard-Jones clusters. Beyond the analysis of energy minimization in the specific model systems investigated, we anticipate COCO should be valuable for solving minimization problems in general, including optimization of parameters in applications to machine learning and molecular structure determination.

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.

Spin-the-bottle Sort and Annealing Sort: Oblivious Sorting via Round-robin Random Comparisons

PubMed Central

Goodrich, Michael T.

2013-01-01

We study sorting algorithms based on randomized round-robin comparisons. Specifically, we study Spin-the-bottle sort, where comparisons are unrestricted, and Annealing sort, where comparisons are restricted to a distance bounded by a temperature parameter. Both algorithms are simple, randomized, data-oblivious sorting algorithms, which are useful in privacy-preserving computations, but, as we show, Annealing sort is much more efficient. We show that there is an input permutation that causes Spin-the-bottle sort to require Ω(n2 log n) expected time in order to succeed, and that in O(n2 log n) time this algorithm succeeds with high probability for any input. We also show there is a specification of Annealing sort that runs in O(n log n) time and succeeds with very high probability. PMID:24550575

Direct aperture optimization: a turnkey solution for step-and-shoot IMRT.

PubMed

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.

Optimization of the computational load of a hypercube supercomputer onboard a mobile robot.

PubMed

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.

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.

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.

Efficient mapping algorithms for scheduling robot inverse dynamics computation on a multiprocessor system

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.

Optimum gradient material for a functionally graded dental implant using metaheuristic algorithms.

PubMed

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.

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.

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

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.

Optimizing Adversary Training and the Structure of the Navy Adversary Fleet

DTIC Science & Technology

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

2-D Ultrasound Sparse Arrays Multidepth Radiation Optimization Using Simulated Annealing and Spiral-Array Inspired Energy Functions.

PubMed

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.

Optimal marker placement in hadrontherapy: intelligent optimization strategies with augmented Lagrangian pattern search.

PubMed

Altomare, Cristina; Guglielmann, Raffaella; Riboldi, Marco; Bellazzi, Riccardo; Baroni, Guido

2015-02-01

In high precision photon radiotherapy and in hadrontherapy, it is crucial to minimize the occurrence of geometrical deviations with respect to the treatment plan in each treatment session. To this end, point-based infrared (IR) optical tracking for patient set-up quality assessment is performed. Such tracking depends on external fiducial points placement. The main purpose of our work is to propose a new algorithm based on simulated annealing and augmented Lagrangian pattern search (SAPS), which is able to take into account prior knowledge, such as spatial constraints, during the optimization process. The SAPS algorithm was tested on data related to head and neck and pelvic cancer patients, and that were fitted with external surface markers for IR optical tracking applied for patient set-up preliminary correction. The integrated algorithm was tested considering optimality measures obtained with Computed Tomography (CT) images (i.e. the ratio between the so-called target registration error and fiducial registration error, TRE/FRE) and assessing the marker spatial distribution. Comparison has been performed with randomly selected marker configuration and with the GETS algorithm (Genetic Evolutionary Taboo Search), also taking into account the presence of organs at risk. The results obtained with SAPS highlight improvements with respect to the other approaches: (i) TRE/FRE ratio decreases; (ii) marker distribution satisfies both marker visibility and spatial constraints. We have also investigated how the TRE/FRE ratio is influenced by the number of markers, obtaining significant TRE/FRE reduction with respect to the random configurations, when a high number of markers is used. The SAPS algorithm is a valuable strategy for fiducial configuration optimization in IR optical tracking applied for patient set-up error detection and correction in radiation therapy, showing that taking into account prior knowledge is valuable in this optimization process. Further work will be focused on the computational optimization of the SAPS algorithm toward fast point-of-care applications. Copyright © 2014 Elsevier Inc. All rights reserved.

An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming

PubMed Central

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

An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming.

PubMed

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.

Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods †

PubMed Central

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

Dissipative environment may improve the quantum annealing performances of the ferromagnetic p -spin model

NASA Astrophysics Data System (ADS)

Passarelli, G.; De Filippis, G.; Cataudella, V.; Lucignano, P.

2018-02-01

We investigate the quantum annealing of the ferromagnetic p -spin model in a dissipative environment (p =5 and p =7 ). This model, in the large-p limit, codifies Grover's algorithm for searching in an unsorted database [L. K. Grover, Proceedings of the 28th Annual ACM Symposium on Theory of Computing (ACM, New York, 1996), pp. 212-219]. The dissipative environment is described by a phonon bath in thermal equilibrium at finite temperature. The dynamics is studied in the framework of a Lindblad master equation for the reduced density matrix describing only the spins. Exploiting the symmetries of our model Hamiltonian, we can describe many spins and extrapolate expected trends for large N and p . While at weak system-bath coupling the dissipative environment has detrimental effects on the annealing results, we show that in the intermediate-coupling regime, the phonon bath seems to speed up the annealing at low temperatures. This improvement in the performance is likely not due to thermal fluctuation but rather arises from a correlated spin-bath state and persists even at zero temperature. This result may pave the way to a new scenario in which, by appropriately engineering the system-bath coupling, one may optimize quantum annealing performances below either the purely quantum or the classical limit.

Solving a Higgs optimization problem with quantum annealing for machine learning.

PubMed

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.

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.

Temperature Scaling Law for Quantum Annealing Optimizers.

PubMed

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.

A Simplified Algorithm for Statistical Investigation of Damage Spreading

NASA Astrophysics Data System (ADS)

Gecow, Andrzej

2009-04-01

On the way to simulating adaptive evolution of complex system describing a living object or human developed project, a fitness should be defined on node states or network external outputs. Feedbacks lead to circular attractors of these states or outputs which make it difficult to define a fitness. The main statistical effects of adaptive condition are the result of small change tendency and to appear, they only need a statistically correct size of damage initiated by evolutionary change of system. This observation allows to cut loops of feedbacks and in effect to obtain a particular statistically correct state instead of a long circular attractor which in the quenched model is expected for chaotic network with feedback. Defining fitness on such states is simple. We calculate only damaged nodes and only once. Such an algorithm is optimal for investigation of damage spreading i.e. statistical connections of structural parameters of initial change with the size of effected damage. It is a reversed-annealed method—function and states (signals) may be randomly substituted but connections are important and are preserved. The small damages important for adaptive evolution are correctly depicted in comparison to Derrida annealed approximation which expects equilibrium levels for large networks. The algorithm indicates these levels correctly. The relevant program in Pascal, which executes the algorithm for a wide range of parameters, can be obtained from the author.

Microseismic event location using global optimization algorithms: An integrated and automated workflow

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.

Energy minimization in medical image analysis: Methodologies and applications.

PubMed

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.

Heuristics for Multiobjective Optimization of Two-Sided Assembly Line Systems

PubMed Central

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

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.

Perspective: Stochastic magnetic devices for cognitive computing

NASA Astrophysics Data System (ADS)

Roy, Kaushik; Sengupta, Abhronil; Shim, Yong

2018-06-01

Stochastic switching of nanomagnets can potentially enable probabilistic cognitive hardware consisting of noisy neural and synaptic components. Furthermore, computational paradigms inspired from the Ising computing model require stochasticity for achieving near-optimality in solutions to various types of combinatorial optimization problems such as the Graph Coloring Problem or the Travelling Salesman Problem. Achieving optimal solutions in such problems are computationally exhaustive and requires natural annealing to arrive at the near-optimal solutions. Stochastic switching of devices also finds use in applications involving Deep Belief Networks and Bayesian Inference. In this article, we provide a multi-disciplinary perspective across the stack of devices, circuits, and algorithms to illustrate how the stochastic switching dynamics of spintronic devices in the presence of thermal noise can provide a direct mapping to the computational units of such probabilistic intelligent systems.

An Evaluation of a Modified Simulated Annealing Algorithm for Various Formulations

DTIC Science & Technology

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

Free Energy Minimization by Simulated Annealing with Applications to Lithospheric Slabs and Mantle Plumes

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.

Multiscale approach to contour fitting for MR images

NASA Astrophysics Data System (ADS)

Rueckert, Daniel; Burger, Peter

1996-04-01

We present a new multiscale contour fitting process which combines information about the image and the contour of the object at different levels of scale. The algorithm is based on energy minimizing deformable models but avoids some of the problems associated with these models. The segmentation algorithm starts by constructing a linear scale-space of an image through convolution of the original image with a Gaussian kernel at different levels of scale, where the scale corresponds to the standard deviation of the Gaussian kernel. At high levels of scale large scale features of the objects are preserved while small scale features, like object details as well as noise, are suppressed. In order to maximize the accuracy of the segmentation, the contour of the object of interest is then tracked in scale-space from coarse to fine scales. We propose a hybrid multi-temperature simulated annealing optimization to minimize the energy of the deformable model. At high levels of scale the SA optimization is started at high temperatures, enabling the SA optimization to find a global optimal solution. At lower levels of scale the SA optimization is started at lower temperatures (at the lowest level the temperature is close to 0). This enforces a more deterministic behavior of the SA optimization at lower scales and leads to an increasingly local optimization as high energy barriers cannot be crossed. The performance and robustness of the algorithm have been tested on spin-echo MR images of the cardiovascular system. The task was to segment the ascending and descending aorta in 15 datasets of different individuals in order to measure regional aortic compliance. The results show that the algorithm is able to provide more accurate segmentation results than the classic contour fitting process and is at the same time very robust to noise and initialization.

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

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.

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.

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.

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

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.

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.

A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing.

PubMed

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.

A self-organizing neural network for the traveling salesman problem that is competitive with simulated annealing.

PubMed

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.

Development and demonstration of an on-board mission planner for helicopters

NASA Technical Reports Server (NTRS)

Deutsch, Owen L.; Desai, Mukund

1988-01-01

Mission management tasks can be distributed within a planning hierarchy, where each level of the hierarchy addresses a scope of action, and associated time scale or planning horizon, and requirements for plan generation response time. The current work is focused on the far-field planning subproblem, with a scope and planning horizon encompassing the entire mission and with a response time required to be about two minutes. The far-feld planning problem is posed as a constrained optimization problem and algorithms and structural organizations are proposed for the solution. Algorithms are implemented in a developmental environment, and performance is assessed with respect to optimality and feasibility for the intended application and in comparison with alternative algorithms. This is done for the three major components of far-field planning: goal planning, waypoint path planning, and timeline management. It appears feasible to meet performance requirements on a 10 Mips flyable processor (dedicated to far-field planning) using a heuristically-guided simulated annealing technique for the goal planner, a modified A* search for the waypoint path planner, and a speed scheduling technique developed for this project.

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.

Conformational space annealing scheme in the inverse design of functional materials

NASA Astrophysics Data System (ADS)

Kim, Sunghyun; Lee, In-Ho; Lee, Jooyoung; Oh, Young Jun; Chang, Kee Joo

2015-03-01

Recently, the so-called inverse method has drawn much attention, in which specific electronic properties are initially assigned and target materials are subsequently searched. In this work, we develop a new scheme for the inverse design of functional materials, in which the conformational space annealing (CSA) algorithm for global optimization is combined with first-principles density functional calculations. To implement the CSA, we need a series of ingredients, (i) an objective function to minimize, (ii) a 'distance' measure between two conformations, (iii) a local enthalpy minimizer of a given conformation, (iv) ways to combine two parent conformations to generate a daughter one, (v) a special conformation update scheme, and (vi) an annealing method in the 'distance' parameter axis. We show the results of applications for searching for Si crystals with direct band gaps and the lowest-enthalpy phase of boron at a finite pressure and discuss the efficiency of the present scheme. This work is supported by the National Research Foundation of Korea (NRF) under Grant No. NRF-2005-0093845 and by Samsung Science and Technology Foundation under Grant No. SSTFBA1401-08.

A thermally driven differential mutation approach for the structural optimization of large atomic systems

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.

Social Milieu Oriented Routing: A New Dimension to Enhance Network Security in WSNs.

PubMed

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.

Ising Processing Units: Potential and Challenges for Discrete Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coffrin, Carleton James; Nagarajan, Harsha; Bent, Russell Whitford

The recent emergence of novel computational devices, such as adiabatic quantum computers, CMOS annealers, and optical parametric oscillators, presents new opportunities for hybrid-optimization algorithms that leverage these kinds of specialized hardware. In this work, we propose the idea of an Ising processing unit as a computational abstraction for these emerging tools. Challenges involved in using and bench- marking these devices are presented, and open-source software tools are proposed to address some of these challenges. The proposed benchmarking tools and methodology are demonstrated by conducting a baseline study of established solution methods to a D-Wave 2X adiabatic quantum computer, one examplemore » of a commercially available Ising processing unit.« less

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.

Tunneling and speedup in quantum optimization for permutation-symmetric problems

DOE PAGES

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

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

Algorithms for optimizing cross-overs in DNA shuffling.

PubMed

He, Lu; Friedman, Alan M; Bailey-Kellogg, Chris

2012-03-21

DNA shuffling generates combinatorial libraries of chimeric genes by stochastically recombining parent genes. The resulting libraries are subjected to large-scale genetic selection or screening to identify those chimeras with favorable properties (e.g., enhanced stability or enzymatic activity). While DNA shuffling has been applied quite successfully, it is limited by its homology-dependent, stochastic nature. Consequently, it is used only with parents of sufficient overall sequence identity, and provides no control over the resulting chimeric library. This paper presents efficient methods to extend the scope of DNA shuffling to handle significantly more diverse parents and to generate more predictable, optimized libraries. Our CODNS (cross-over optimization for DNA shuffling) approach employs polynomial-time dynamic programming algorithms to select codons for the parental amino acids, allowing for zero or a fixed number of conservative substitutions. We first present efficient algorithms to optimize the local sequence identity or the nearest-neighbor approximation of the change in free energy upon annealing, objectives that were previously optimized by computationally-expensive integer programming methods. We then present efficient algorithms for more powerful objectives that seek to localize and enhance the frequency of recombination by producing "runs" of common nucleotides either overall or according to the sequence diversity of the resulting chimeras. We demonstrate the effectiveness of CODNS in choosing codons and allocating substitutions to promote recombination between parents targeted in earlier studies: two GAR transformylases (41% amino acid sequence identity), two very distantly related DNA polymerases, Pol X and β (15%), and beta-lactamases of varying identity (26-47%). Our methods provide the protein engineer with a new approach to DNA shuffling that supports substantially more diverse parents, is more deterministic, and generates more predictable and more diverse chimeric libraries.

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.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gecow, Andrzej

On the way to simulating adaptive evolution of complex system describing a living object or human developed project, a fitness should be defined on node states or network external outputs. Feedbacks lead to circular attractors of these states or outputs which make it difficult to define a fitness. The main statistical effects of adaptive condition are the result of small change tendency and to appear, they only need a statistically correct size of damage initiated by evolutionary change of system. This observation allows to cut loops of feedbacks and in effect to obtain a particular statistically correct state instead ofmore » a long circular attractor which in the quenched model is expected for chaotic network with feedback. Defining fitness on such states is simple. We calculate only damaged nodes and only once. Such an algorithm is optimal for investigation of damage spreading i.e. statistical connections of structural parameters of initial change with the size of effected damage. It is a reversed-annealed method--function and states (signals) may be randomly substituted but connections are important and are preserved. The small damages important for adaptive evolution are correctly depicted in comparison to Derrida annealed approximation which expects equilibrium levels for large networks. The algorithm indicates these levels correctly. The relevant program in Pascal, which executes the algorithm for a wide range of parameters, can be obtained from the author.« less

Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

PubMed Central

Zheng, Ming; Wu, Jia-nan; Huang, Yan-xin; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang

2012-01-01

Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms. PMID:23226565

Information theory-based algorithm for in silico prediction of PCR products with whole genomic sequences as templates.

PubMed

Cao, Youfang; Wang, Lianjie; Xu, Kexue; Kou, Chunhai; Zhang, Yulei; Wei, Guifang; He, Junjian; Wang, Yunfang; Zhao, Liping

2005-07-26

A new algorithm for assessing similarity between primer and template has been developed based on the hypothesis that annealing of primer to template is an information transfer process. Primer sequence is converted to a vector of the full potential hydrogen numbers (3 for G or C, 2 for A or T), while template sequence is converted to a vector of the actual hydrogen bond numbers formed after primer annealing. The former is considered as source information and the latter destination information. An information coefficient is calculated as a measure for fidelity of this information transfer process and thus a measure of similarity between primer and potential annealing site on template. Successful prediction of PCR products from whole genomic sequences with a computer program based on the algorithm demonstrated the potential of this new algorithm in areas like in silico PCR and gene finding.

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.

The universal scissor component: Optimization of a reconfigurable component for deployable scissor structures

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.

RNA secondary structure prediction using soft computing.

PubMed

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.

A FODO racetrack ring for nuSTORM: design and optimization

DOE PAGES

Liu, A.; Bross, A.; Neuffer, D.

2017-07-17

Here, 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 themore » arc 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

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.

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.

Zero-temperature quantum annealing bottlenecks in the spin-glass phase.

PubMed

Knysh, Sergey

2016-08-05

A promising approach to solving hard binary optimization problems is quantum adiabatic annealing in a transverse magnetic field. An instantaneous ground state-initially a symmetric superposition of all possible assignments of N qubits-is closely tracked as it becomes more and more localized near the global minimum of the classical energy. Regions where the energy gap to excited states is small (for instance at the phase transition) are the algorithm's bottlenecks. Here I show how for large problems the complexity becomes dominated by O(log N) bottlenecks inside the spin-glass phase, where the gap scales as a stretched exponential. For smaller N, only the gap at the critical point is relevant, where it scales polynomially, as long as the phase transition is second order. This phenomenon is demonstrated rigorously for the two-pattern Gaussian Hopfield model. Qualitative comparison with the Sherrington-Kirkpatrick model leads to similar conclusions.

Annealed Importance Sampling for Neural Mass Models

PubMed Central

Penny, Will; Sengupta, Biswa

2016-01-01

Neural Mass Models provide a compact description of the dynamical activity of cell populations in neocortical regions. Moreover, models of regional activity can be connected together into networks, and inferences made about the strength of connections, using M/EEG data and Bayesian inference. To date, however, Bayesian methods have been largely restricted to the Variational Laplace (VL) algorithm which assumes that the posterior distribution is Gaussian and finds model parameters that are only locally optimal. This paper explores the use of Annealed Importance Sampling (AIS) to address these restrictions. We implement AIS using proposals derived from Langevin Monte Carlo (LMC) which uses local gradient and curvature information for efficient exploration of parameter space. In terms of the estimation of Bayes factors, VL and AIS agree about which model is best but report different degrees of belief. Additionally, AIS finds better model parameters and we find evidence of non-Gaussianity in their posterior distribution. PMID:26942606

Available Transfer Capability Determination Using Hybrid Evolutionary Algorithm

NASA Astrophysics Data System (ADS)

Jirapong, Peeraool; Ongsakul, Weerakorn

2008-10-01

This paper proposes a new hybrid evolutionary algorithm (HEA) based on evolutionary programming (EP), tabu search (TS), and simulated annealing (SA) to determine the available transfer capability (ATC) of power transactions between different control areas in deregulated power systems. The optimal power flow (OPF)-based ATC determination is used to evaluate the feasible maximum ATC value within real and reactive power generation limits, line thermal limits, voltage limits, and voltage and angle stability limits. The HEA approach simultaneously searches for real power generations except slack bus in a source area, real power loads in a sink area, and generation bus voltages to solve the OPF-based ATC problem. Test results on the modified IEEE 24-bus reliability test system (RTS) indicate that ATC determination by the HEA could enhance ATC far more than those from EP, TS, hybrid TS/SA, and improved EP (IEP) algorithms, leading to an efficient utilization of the existing transmission system.

An impatient evolutionary algorithm with probabilistic tabu search for unified solution of some NP-hard problems in graph and set theory via clique finding.

PubMed

Guturu, Parthasarathy; Dantu, Ram

2008-06-01

Many graph- and set-theoretic problems, because of their tremendous application potential and theoretical appeal, have been well investigated by the researchers in complexity theory and were found to be NP-hard. Since the combinatorial complexity of these problems does not permit exhaustive searches for optimal solutions, only near-optimal solutions can be explored using either various problem-specific heuristic strategies or metaheuristic global-optimization methods, such as simulated annealing, genetic algorithms, etc. In this paper, we propose a unified evolutionary algorithm (EA) to the problems of maximum clique finding, maximum independent set, minimum vertex cover, subgraph and double subgraph isomorphism, set packing, set partitioning, and set cover. In the proposed approach, we first map these problems onto the maximum clique-finding problem (MCP), which is later solved using an evolutionary strategy. The proposed impatient EA with probabilistic tabu search (IEA-PTS) for the MCP integrates the best features of earlier successful approaches with a number of new heuristics that we developed to yield a performance that advances the state of the art in EAs for the exploration of the maximum cliques in a graph. Results of experimentation with the 37 DIMACS benchmark graphs and comparative analyses with six state-of-the-art algorithms, including two from the smaller EA community and four from the larger metaheuristics community, indicate that the IEA-PTS outperforms the EAs with respect to a Pareto-lexicographic ranking criterion and offers competitive performance on some graph instances when individually compared to the other heuristic algorithms. It has also successfully set a new benchmark on one graph instance. On another benchmark suite called Benchmarks with Hidden Optimal Solutions, IEA-PTS ranks second, after a very recent algorithm called COVER, among its peers that have experimented with this suite.

Airport Flight Departure Delay Model on Improved BN Structure Learning

NASA Astrophysics Data System (ADS)

Cao, Weidong; Fang, Xiangnong

An high score prior genetic simulated annealing Bayesian network structure learning algorithm (HSPGSA) by combining genetic algorithm(GA) with simulated annealing algorithm(SAA) is developed. The new algorithm provides not only with strong global search capability of GA, but also with strong local hill climb search capability of SAA. The structure with the highest score is prior selected. In the mean time, structures with lower score are also could be choice. It can avoid efficiently prematurity problem by higher score individual wrong direct growing population. Algorithm is applied to flight departure delays analysis in a large hub airport. Based on the flight data a BN model is created. Experiments show that parameters learning can reflect departure delay.

Performance Comparison of Systematic Methods for Rigorous Definition of Coarse-Grained Sites of Large Biomolecules.

PubMed

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.

A Globally Optimal Particle Tracking Technique for Stereo Imaging Velocimetry Experiments

NASA Technical Reports Server (NTRS)

McDowell, Mark

2008-01-01

An important phase of any Stereo Imaging Velocimetry experiment is particle tracking. Particle tracking seeks to identify and characterize the motion of individual particles entrained in a fluid or air experiment. We analyze a cylindrical chamber filled with water and seeded with density-matched particles. In every four-frame sequence, we identify a particle track by assigning a unique track label for each camera image. The conventional approach to particle tracking is to use an exhaustive tree-search method utilizing greedy algorithms to reduce search times. However, these types of algorithms are not optimal due to a cascade effect of incorrect decisions upon adjacent tracks. We examine the use of a guided evolutionary neural net with simulated annealing to arrive at a globally optimal assignment of tracks. The net is guided both by the minimization of the search space through the use of prior limiting assumptions about valid tracks and by a strategy which seeks to avoid high-energy intermediate states which can trap the net in a local minimum. A stochastic search algorithm is used in place of back-propagation of error to further reduce the chance of being trapped in an energy well. Global optimization is achieved by minimizing an objective function, which includes both track smoothness and particle-image utilization parameters. In this paper we describe our model and present our experimental results. We compare our results with a nonoptimizing, predictive tracker and obtain an average increase in valid track yield of 27 percent

A robust hybrid fuzzy-simulated annealing-intelligent water drops approach for tuning a distribution static compensator nonlinear controller in a distribution system

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.

Overlapped Fourier coding for optical aberration removal

PubMed Central

Horstmeyer, Roarke; Ou, Xiaoze; Chung, Jaebum; Zheng, Guoan; Yang, Changhuei

2014-01-01

We present an imaging procedure that simultaneously optimizes a camera’s resolution and retrieves a sample’s phase over a sequence of snapshots. The technique, termed overlapped Fourier coding (OFC), first digitally pans a small aperture across a camera’s pupil plane with a spatial light modulator. At each aperture location, a unique image is acquired. The OFC algorithm then fuses these low-resolution images into a full-resolution estimate of the complex optical field incident upon the detector. Simultaneously, the algorithm utilizes redundancies within the acquired dataset to computationally estimate and remove unknown optical aberrations and system misalignments via simulated annealing. The result is an imaging system that can computationally overcome its optical imperfections to offer enhanced resolution, at the expense of taking multiple snapshots over time. PMID:25321982

Mixed Transportation Network Design under a Sustainable Development Perspective

PubMed Central

Qin, Jin; Ni, Ling-lin; Shi, Feng

2013-01-01

A mixed transportation network design problem considering sustainable development was studied in this paper. Based on the discretization of continuous link-grade decision variables, a bilevel programming model was proposed to describe the problem, in which sustainability factors, including vehicle exhaust emissions, land-use scale, link load, and financial budget, are considered. The objective of the model is to minimize the total amount of resources exploited under the premise of meeting all the construction goals. A heuristic algorithm, which combined the simulated annealing and path-based gradient projection algorithm, was developed to solve the model. The numerical example shows that the transportation network optimized with the method above not only significantly alleviates the congestion on the link, but also reduces vehicle exhaust emissions within the network by up to 41.56%. PMID:23476142

Mixed transportation network design under a sustainable development perspective.

PubMed

Qin, Jin; Ni, Ling-lin; Shi, Feng

2013-01-01

A mixed transportation network design problem considering sustainable development was studied in this paper. Based on the discretization of continuous link-grade decision variables, a bilevel programming model was proposed to describe the problem, in which sustainability factors, including vehicle exhaust emissions, land-use scale, link load, and financial budget, are considered. The objective of the model is to minimize the total amount of resources exploited under the premise of meeting all the construction goals. A heuristic algorithm, which combined the simulated annealing and path-based gradient projection algorithm, was developed to solve the model. The numerical example shows that the transportation network optimized with the method above not only significantly alleviates the congestion on the link, but also reduces vehicle exhaust emissions within the network by up to 41.56%.

Optimization Via Open System Quantum Annealing

DTIC Science & Technology

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

Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach.

PubMed

Cakar, Tarik; Koker, Rasit

2015-01-01

A particle swarm optimization algorithm (PSO) has been used to solve the single machine total weighted tardiness problem (SMTWT) with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA) and simulated annealing (SA) have been used. In the subhybrid system (GA and SA), GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR) has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system.

Heuristic approach to image registration

NASA Astrophysics Data System (ADS)

Gertner, Izidor; Maslov, Igor V.

2000-08-01

Image registration, i.e. correct mapping of images obtained from different sensor readings onto common reference frame, is a critical part of multi-sensor ATR/AOR systems based on readings from different types of sensors. In order to fuse two different sensor readings of the same object, the readings have to be put into a common coordinate system. This task can be formulated as optimization problem in a space of all possible affine transformations of an image. In this paper, a combination of heuristic methods is explored to register gray- scale images. The modification of Genetic Algorithm is used as the first step in global search for optimal transformation. It covers the entire search space with (randomly or heuristically) scattered probe points and helps significantly reduce the search space to a subspace of potentially most successful transformations. Due to its discrete character, however, Genetic Algorithm in general can not converge while coming close to the optimum. Its termination point can be specified either as some predefined number of generations or as achievement of a certain acceptable convergence level. To refine the search, potential optimal subspaces are searched using more delicate and efficient for local search Taboo and Simulated Annealing methods.

A Simple Label Switching Algorithm for Semisupervised Structural SVMs.

PubMed

Balamurugan, P; Shevade, Shirish; Sundararajan, S

2015-10-01

In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector machines with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labeled and unlabeled examples, along with the domain constraints. We propose a simple optimization approach that alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective label switching method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching and avoiding poor local minima, which are not very useful. The algorithm is simple and easy to implement. Further, it is suitable for any structured output learning problem where exact inference is available. Experiments on benchmark sequence labeling data sets and a natural language parsing data set show that the proposed approach, though simple, achieves comparable generalization performance.

A clustering method of Chinese medicine prescriptions based on modified firefly algorithm.

PubMed

Yuan, Feng; Liu, Hong; Chen, Shou-Qiang; Xu, Liang

2016-12-01

This paper is aimed to study the clustering method for Chinese medicine (CM) medical cases. The traditional K-means clustering algorithm had shortcomings such as dependence of results on the selection of initial value, trapping in local optimum when processing prescriptions form CM medical cases. Therefore, a new clustering method based on the collaboration of firefly algorithm and simulated annealing algorithm was proposed. This algorithm dynamically determined the iteration of firefly algorithm and simulates sampling of annealing algorithm by fitness changes, and increased the diversity of swarm through expansion of the scope of the sudden jump, thereby effectively avoiding premature problem. The results from confirmatory experiments for CM medical cases suggested that, comparing with traditional K-means clustering algorithms, this method was greatly improved in the individual diversity and the obtained clustering results, the computing results from this method had a certain reference value for cluster analysis on CM prescriptions.

On the Critical Behaviour, Crossover Point and Complexity of the Exact Cover Problem

NASA Technical Reports Server (NTRS)

Morris, Robin D.; Smelyanskiy, Vadim N.; Shumow, Daniel; Koga, Dennis (Technical Monitor)

2003-01-01

Research into quantum algorithms for NP-complete problems has rekindled interest in the detailed study a broad class of combinatorial problems. A recent paper applied the quantum adiabatic evolution algorithm to the Exact Cover problem for 3-sets (EC3), and provided an empirical evidence that the algorithm was polynomial. In this paper we provide a detailed study of the characteristics of the exact cover problem. We present the annealing approximation applied to EC3, which gives an over-estimate of the phase transition point. We also identify empirically the phase transition point. We also study the complexity of two classical algorithms on this problem: Davis-Putnam and Simulated Annealing. For these algorithms, EC3 is significantly easier than 3-SAT.

A study of optimization techniques in HDR brachytherapy for the prostate

NASA Astrophysics Data System (ADS)

Pokharel, Ghana Shyam

Several studies carried out thus far are in favor of dose escalation to the prostate gland to have better local control of the disease. But optimal way of delivery of higher doses of radiation therapy to the prostate without hurting neighboring critical structures is still debatable. In this study, we proposed that real time high dose rate (HDR) brachytherapy with highly efficient and effective optimization could be an alternative means of precise delivery of such higher doses. This approach of delivery eliminates the critical issues such as treatment setup uncertainties and target localization as in external beam radiation therapy. Likewise, dosimetry in HDR brachytherapy is not influenced by organ edema and potential source migration as in permanent interstitial implants. Moreover, the recent report of radiobiological parameters further strengthen the argument of using hypofractionated HDR brachytherapy for the management of prostate cancer. Firstly, we studied the essential features and requirements of real time HDR brachytherapy treatment planning system. Automating catheter reconstruction with fast editing tools, fast yet accurate dose engine, robust and fast optimization and evaluation engine are some of the essential requirements for such procedures. Moreover, in most of the cases we performed, treatment plan optimization took significant amount of time of overall procedure. So, making treatment plan optimization automatic or semi-automatic with sufficient speed and accuracy was the goal of the remaining part of the project. Secondly, we studied the role of optimization function and constraints in overall quality of optimized plan. We have studied the gradient based deterministic algorithm with dose volume histogram (DVH) and more conventional variance based objective functions for optimization. In this optimization strategy, the relative weight of particular objective in aggregate objective function signifies its importance with respect to other objectives. Based on our study, DVH based objective function performed better than traditional variance based objective function in creating a clinically acceptable plan when executed under identical conditions. Thirdly, we studied the multiobjective optimization strategy using both DVH and variance based objective functions. The optimization strategy was to create several Pareto optimal solutions by scanning the clinically relevant part of the Pareto front. This strategy was adopted to decouple optimization from decision such that user could select final solution from the pool of alternative solutions based on his/her clinical goals. The overall quality of treatment plan improved using this approach compared to traditional class solution approach. In fact, the final optimized plan selected using decision engine with DVH based objective was comparable to typical clinical plan created by an experienced physicist. Next, we studied the hybrid technique comprising both stochastic and deterministic algorithm to optimize both dwell positions and dwell times. The simulated annealing algorithm was used to find optimal catheter distribution and the DVH based algorithm was used to optimize 3D dose distribution for given catheter distribution. This unique treatment planning and optimization tool was capable of producing clinically acceptable highly reproducible treatment plans in clinically reasonable time. As this algorithm was able to create clinically acceptable plans within clinically reasonable time automatically, it is really appealing for real time procedures. Next, we studied the feasibility of multiobjective optimization using evolutionary algorithm for real time HDR brachytherapy for the prostate. The algorithm with properly tuned algorithm specific parameters was able to create clinically acceptable plans within clinically reasonable time. However, the algorithm was let to run just for limited number of generations not considered optimal, in general, for such algorithms. This was done to keep time window desirable for real time procedures. Therefore, it requires further study with improved conditions to realize the full potential of the algorithm.

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

Distributed Function Mining for Gene Expression Programming Based on Fast Reduction.

PubMed

Deng, Song; Yue, Dong; Yang, Le-chan; Fu, Xiong; Feng, Ya-zhou

2016-01-01

For high-dimensional and massive data sets, traditional centralized gene expression programming (GEP) or improved algorithms lead to increased run-time and decreased prediction accuracy. To solve this problem, this paper proposes a new improved algorithm called distributed function mining for gene expression programming based on fast reduction (DFMGEP-FR). In DFMGEP-FR, fast attribution reduction in binary search algorithms (FAR-BSA) is proposed to quickly find the optimal attribution set, and the function consistency replacement algorithm is given to solve integration of the local function model. Thorough comparative experiments for DFMGEP-FR, centralized GEP and the parallel gene expression programming algorithm based on simulated annealing (parallel GEPSA) are included in this paper. For the waveform, mushroom, connect-4 and musk datasets, the comparative results show that the average time-consumption of DFMGEP-FR drops by 89.09%%, 88.85%, 85.79% and 93.06%, respectively, in contrast to centralized GEP and by 12.5%, 8.42%, 9.62% and 13.75%, respectively, compared with parallel GEPSA. Six well-studied UCI test data sets demonstrate the efficiency and capability of our proposed DFMGEP-FR algorithm for distributed function mining.

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.

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

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).

Distributed genetic algorithms for the floorplan design problem

NASA Technical Reports Server (NTRS)

Cohoon, James P.; Hegde, Shailesh U.; Martin, Worthy N.; Richards, Dana S.

1991-01-01

Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried.

Deterministic annealing for density estimation by multivariate normal mixtures

NASA Astrophysics Data System (ADS)

Kloppenburg, Martin; Tavan, Paul

1997-03-01

An approach to maximum-likelihood density estimation by mixtures of multivariate normal distributions for large high-dimensional data sets is presented. Conventionally that problem is tackled by notoriously unstable expectation-maximization (EM) algorithms. We remove these instabilities by the introduction of soft constraints, enabling deterministic annealing. Our developments are motivated by the proof that algorithmically stable fuzzy clustering methods that are derived from statistical physics analogs are special cases of EM procedures.

Shuffling cross-validation-bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography.

PubMed

Zarei, Kobra; Atabati, Morteza; Ahmadi, Monire

2017-05-04

Bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross-validation-BA (CV-BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV-BA. The results showed that shuffling CV-BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV-BA-MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV-BA-SVM method were obtained as 0.0704 and 0.9922, respectively.

Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering

PubMed Central

Zheng, Ming; Sun, Ying; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang

2012-01-01

Background Gravitation field algorithm (GFA) is a new optimization algorithm which is based on an imitation of natural phenomena. GFA can do well both for searching global minimum and multi-minima in computational biology. But GFA needs to be improved for increasing efficiency, and modified for applying to some discrete data problems in system biology. Method An improved GFA called IGFA was proposed in this paper. Two parts were improved in IGFA. The first one is the rule of random division, which is a reasonable strategy and makes running time shorter. The other one is rotation factor, which can improve the accuracy of IGFA. And to apply IGFA to the hierarchical clustering, the initial part and the movement operator were modified. Results Two kinds of experiments were used to test IGFA. And IGFA was applied to hierarchical clustering. The global minimum experiment was used with IGFA, GFA, GA (genetic algorithm) and SA (simulated annealing). Multi-minima experiment was used with IGFA and GFA. The two experiments results were compared with each other and proved the efficiency of IGFA. IGFA is better than GFA both in accuracy and running time. For the hierarchical clustering, IGFA is used to optimize the smallest distance of genes pairs, and the results were compared with GA and SA, singular-linkage clustering, UPGMA. The efficiency of IGFA is proved. PMID:23173043

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.

Prediction-based Dynamic Energy Management in Wireless Sensor Networks

PubMed Central

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.

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.

Metaheuristic Algorithms for Convolution Neural Network

PubMed Central

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

Metaheuristic Algorithms for Convolution Neural Network.

PubMed

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).

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.

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

Comparison of a model vapor deposited glass films to equilibrium glass films

NASA Astrophysics Data System (ADS)

Flenner, Elijah; Berthier, Ludovic; Charbonneau, Patrick; Zamponi, Francesco

Vapor deposition of particles onto a substrate held at around 85% of the glass transition temperature can create glasses with increased density, enthalpy, kinetic stability, and mechanical stability compared to an ordinary glass created by cooling. It is estimated that an ordinary glass would need to age thousands of years to reach the kinetic stability of a vapor deposited glass, and a natural question is how close to the equilibrium is the vapor deposited glass. To understand the process, algorithms akin to vapor deposition are used to create simulated glasses that have a higher kinetic stability than their annealed counterpart, although these glasses may not be well equilibrated either. Here we use novel models optimized for a swap Monte Carlo algorithm in order to create equilibrium glass films and compare their properties with those of glasses obtained from vapor deposition algorithms. This approach allows us to directly assess the non-equilibrium nature of vapor-deposited ultrastable glasses. Simons Collaboration on Cracking the Glass Problem and NSF Grant No. DMR 1608086.

Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms.

PubMed

Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza

2012-05-01

Effective abnormality detection and diagnosis in Magnetic Resonance Images (MRIs) requires a robust segmentation strategy. Since manual segmentation is a time-consuming task which engages valuable human resources, automatic MRI segmentations received an enormous amount of attention. For this goal, various techniques have been applied. However, Markov Random Field (MRF) based algorithms have produced reasonable results in noisy images compared to other methods. MRF seeks a label field which minimizes an energy function. The traditional minimization method, simulated annealing (SA), uses Monte Carlo simulation to access the minimum solution with heavy computation burden. For this reason, MRFs are rarely used in real time processing environments. This paper proposed a novel method based on MRF and a hybrid of social algorithms that contain an ant colony optimization (ACO) and a Gossiping algorithm which can be used for segmenting single and multispectral MRIs in real time environments. Combining ACO with the Gossiping algorithm helps find the better path using neighborhood information. Therefore, this interaction causes the algorithm to converge to an optimum solution faster. Several experiments on phantom and real images were performed. Results indicate that the proposed algorithm outperforms the traditional MRF and hybrid of MRF-ACO in speed and accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

Design Time Optimization for Hardware Watermarking Protection of HDL Designs

PubMed Central

Castillo, E.; Morales, D. P.; García, A.; Parrilla, L.; Todorovich, E.; Meyer-Baese, U.

2015-01-01

HDL-level design offers important advantages for the application of watermarking to IP cores, but its complexity also requires tools automating these watermarking algorithms. A new tool for signature distribution through combinational logic is proposed in this work. IPP@HDL, a previously proposed high-level watermarking technique, has been employed for evaluating the tool. IPP@HDL relies on spreading the bits of a digital signature at the HDL design level using combinational logic included within the original system. The development of this new tool for the signature distribution has not only extended and eased the applicability of this IPP technique, but it has also improved the signature hosting process itself. Three algorithms were studied in order to develop this automated tool. The selection of a cost function determines the best hosting solutions in terms of area and performance penalties on the IP core to protect. An 1D-DWT core and MD5 and SHA1 digital signatures were used in order to illustrate the benefits of the new tool and its optimization related to the extraction logic resources. Among the proposed algorithms, the alternative based on simulated annealing reduces the additional resources while maintaining an acceptable computation time and also saving designer effort and time. PMID:25861681

A Genetic Algorithm for the Generation of Packetization Masks for Robust Image Communication

PubMed Central

Zapata-Quiñones, Katherine; Duran-Faundez, Cristian; Gutiérrez, Gilberto; Lecuire, Vincent; Arredondo-Flores, Christopher; Jara-Lipán, Hugo

2017-01-01

Image interleaving has proven to be an effective solution to provide the robustness of image communication systems when resource limitations make reliable protocols unsuitable (e.g., in wireless camera sensor networks); however, the search for optimal interleaving patterns is scarcely tackled in the literature. In 2008, Rombaut et al. presented an interesting approach introducing a packetization mask generator based in Simulated Annealing (SA), including a cost function, which allows assessing the suitability of a packetization pattern, avoiding extensive simulations. In this work, we present a complementary study about the non-trivial problem of generating optimal packetization patterns. We propose a genetic algorithm, as an alternative to the cited work, adopting the mentioned cost function, then comparing it to the SA approach and a torus automorphism interleaver. In addition, we engage the validation of the cost function and provide results attempting to conclude about its implication in the quality of reconstructed images. Several scenarios based on visual sensor networks applications were tested in a computer application. Results in terms of the selected cost function and image quality metric PSNR show that our algorithm presents similar results to the other approaches. Finally, we discuss the obtained results and comment about open research challenges. PMID:28452934

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.

Evolutionary squeaky wheel optimization: a new framework for analysis.

PubMed

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.

Verification and rectification of the physical analogy of simulated annealing for the solution of the traveling salesman problem.

PubMed

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.

Signatures of a globally optimal searching strategy in the three-dimensional foraging flights of bumblebees

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.

Annealed Importance Sampling Reversible Jump MCMC algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karagiannis, Georgios; Andrieu, Christophe

2013-03-20

It will soon be 20 years since reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithms have been proposed. They have significantly extended the scope of Markov chain Monte Carlo simulation methods, offering the promise to be able to routinely tackle transdimensional sampling problems, as encountered in Bayesian model selection problems for example, in a principled and flexible fashion. Their practical efficient implementation, however, still remains a challenge. A particular difficulty encountered in practice is in the choice of the dimension matching variables (both their nature and their distribution) and the reversible transformations which allow one to define the one-to-one mappingsmore » underpinning the design of these algorithms. Indeed, even seemingly sensible choices can lead to algorithms with very poor performance. The focus of this paper is the development and performance evaluation of a method, annealed importance sampling RJ-MCMC (aisRJ), which addresses this problem by mitigating the sensitivity of RJ-MCMC algorithms to the aforementioned poor design. As we shall see the algorithm can be understood as being an “exact approximation” of an idealized MCMC algorithm that would sample from the model probabilities directly in a model selection set-up. Such an idealized algorithm may have good theoretical convergence properties, but typically cannot be implemented, and our algorithms can approximate the performance of such idealized algorithms to an arbitrary degree while not introducing any bias for any degree of approximation. Our approach combines the dimension matching ideas of RJ-MCMC with annealed importance sampling and its Markov chain Monte Carlo implementation. We illustrate the performance of the algorithm with numerical simulations which indicate that, although the approach may at first appear computationally involved, it is in fact competitive.« less

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.

Robust quantum optimizer with full connectivity.

PubMed

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.

Quantum annealing with all-to-all connected nonlinear oscillators

PubMed Central

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

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.

Development of a Tool for an Efficient Calibration of CORSIM Models

DOT National Transportation Integrated Search

2014-08-01

This project proposes a Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of ...

Stochastic optimization of broadband reflecting photonic structures.

PubMed

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.

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.

Constructing Optimal Coarse-Grained Sites of Huge Biomolecules by Fluctuation Maximization.

PubMed

Li, Min; Zhang, John Zenghui; Xia, Fei

2016-04-12

Coarse-grained (CG) models are valuable tools for the study of functions of large biomolecules on large length and time scales. The definition of CG representations for huge biomolecules is always a formidable challenge. In this work, we propose a new method called fluctuation maximization coarse-graining (FM-CG) to construct the CG sites of biomolecules. The defined residual in FM-CG converges to a maximal value as the number of CG sites increases, allowing an optimal CG model to be rigorously defined on the basis of the maximum. More importantly, we developed a robust algorithm called stepwise local iterative optimization (SLIO) to accelerate the process of coarse-graining large biomolecules. By means of the efficient SLIO algorithm, the computational cost of coarse-graining large biomolecules is reduced to within the time scale of seconds, which is far lower than that of conventional simulated annealing. The coarse-graining of two huge systems, chaperonin GroEL and lengsin, indicates that our new methods can coarse-grain huge biomolecular systems with up to 10,000 residues within the time scale of minutes. The further parametrization of CG sites derived from FM-CG allows us to construct the corresponding CG models for studies of the functions of huge biomolecular systems.

Selection of optimal oligonucleotide probes for microarrays usingmultiple criteria, global alignment and parameter estimation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Xingyuan; He, Zhili; Zhou, Jizhong

2005-10-30

The oligonucleotide specificity for microarray hybridizationcan be predicted by its sequence identity to non-targets, continuousstretch to non-targets, and/or binding free energy to non-targets. Mostcurrently available programs only use one or two of these criteria, whichmay choose 'false' specific oligonucleotides or miss 'true' optimalprobes in a considerable proportion. We have developed a software tool,called CommOligo using new algorithms and all three criteria forselection of optimal oligonucleotide probes. A series of filters,including sequence identity, free energy, continuous stretch, GC content,self-annealing, distance to the 3'-untranslated region (3'-UTR) andmelting temperature (Tm), are used to check each possibleoligonucleotide. A sequence identity is calculated based onmore » gapped globalalignments. A traversal algorithm is used to generate alignments for freeenergy calculation. The optimal Tm interval is determined based on probecandidates that have passed all other filters. Final probes are pickedusing a combination of user-configurable piece-wise linear functions andan iterative process. The thresholds for identity, stretch and freeenergy filters are automatically determined from experimental data by anaccessory software tool, CommOligo_PE (CommOligo Parameter Estimator).The program was used to design probes for both whole-genome and highlyhomologous sequence data. CommOligo and CommOligo_PE are freely availableto academic users upon request.« less

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.

Computational Multiqubit Tunnelling in Programmable Quantum Annealers

DTIC Science & Technology

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

Energy harvesting from sea waves with consideration of airy and JONSWAP theory and optimization of energy harvester parameters

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.

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.

Real-time terminal area trajectory planning for runway independent aircraft

NASA Astrophysics Data System (ADS)

Xue, Min

The increasing demand for commercial air transportation results in delays due to traffic queues that form bottlenecks along final approach and departure corridors. In urban areas, it is often infeasible to build new runways, and regardless of automation upgrades traffic must remain separated to avoid the wakes of previous aircraft. Vertical or short takeoff and landing aircraft as Runway Independent Aircraft (RIA) can increase passenger throughput at major urban airports via the use of vertiports or stub runways. The concept of simultaneous non-interfering (SNI) operations has been proposed to reduce traffic delays by creating approach and departure corridors that do not intersect existing fixed-wing routes. However, SNI trajectories open new routes that may overfly noise-sensitive areas, and RIA may generate more noise than traditional jet aircraft, particularly on approach. In this dissertation, we develop efficient SNI noise abatement procedures applicable to RIA. First, we introduce a methodology based on modified approximated cell-decomposition and Dijkstra's search algorithm to optimize longitudinal plane (2-D) RIA trajectories over a cost function that minimizes noise, time, and fuel use. Then, we extend the trajectory optimization model to 3-D with a k-ary tree as the discrete search space. We incorporate geography information system (GIS) data, specifically population, into our objective function, and focus on a practical case study: the design of SNI RIA approach procedures to Baltimore-Washington International airport. Because solutions were represented as trim state sequences, we incorporated smooth transition between segments to enable more realistic cost estimates. Due to the significant computational complexity, we investigated alternative more efficient optimization techniques applicable to our nonlinear, non-convex, heavily constrained, and discontinuous objective function. Comparing genetic algorithm (GA) and adaptive simulated annealing (ASA) with our original Dijkstra's algorithm, ASA is identified as the most efficient algorithm for terminal area trajectory optimization. The effects of design parameter discretization are analyzed, with results indicating a SNI procedure with 3-4 segments effectively balances simplicity with cost minimization. Finally, pilot control commands were implemented and generated via optimization-base inverse simulation to validate execution of the optimal approach trajectories.

Quantum Heterogeneous Computing for Satellite Positioning Optimization

NASA Astrophysics Data System (ADS)

Bass, G.; Kumar, V.; Dulny, J., III

2016-12-01

Hard optimization problems occur in many fields of academic study and practical situations. We present results in which quantum heterogeneous computing is used to solve a real-world optimization problem: satellite positioning. Optimization problems like this can scale very rapidly with problem size, and become unsolvable with traditional brute-force methods. Typically, such problems have been approximately solved with heuristic approaches; however, these methods can take a long time to calculate and are not guaranteed to find optimal solutions. Quantum computing offers the possibility of producing significant speed-up and improved solution quality. There are now commercially available quantum annealing (QA) devices that are designed to solve difficult optimization problems. These devices have 1000+ quantum bits, but they have significant hardware size and connectivity limitations. We present a novel heterogeneous computing stack that combines QA and classical machine learning and allows the use of QA on problems larger than the quantum hardware could solve in isolation. We begin by analyzing the satellite positioning problem with a heuristic solver, the genetic algorithm. The classical computer's comparatively large available memory can explore the full problem space and converge to a solution relatively close to the true optimum. The QA device can then evolve directly to the optimal solution within this more limited space. Preliminary experiments, using the Quantum Monte Carlo (QMC) algorithm to simulate QA hardware, have produced promising results. Working with problem instances with known global minima, we find a solution within 8% in a matter of seconds, and within 5% in a few minutes. Future studies include replacing QMC with commercially available quantum hardware and exploring more problem sets and model parameters. Our results have important implications for how heterogeneous quantum computing can be used to solve difficult optimization problems in any field.

Correlation Equations for Condensing Heat Exchangers Based on an Algorithmic Performance-Data Classification

NASA Astrophysics Data System (ADS)

Pacheco-Vega, Arturo

2016-09-01

In this work a new set of correlation equations is developed and introduced to accurately describe the thermal performance of compact heat exchangers with possible condensation. The feasible operating conditions for the thermal system correspond to dry- surface, dropwise condensation, and film condensation. Using a prescribed form for each condition, a global regression analysis for the best-fit correlation to experimental data is carried out with a simulated annealing optimization technique. The experimental data were taken from the literature and algorithmically classified into three groups -related to the possible operating conditions- with a previously-introduced Gaussian-mixture-based methodology. Prior to their use in the analysis, the correct data classification was assessed and confirmed via artificial neural networks. Predictions from the correlations obtained for the different conditions are within the uncertainty of the experiments and substantially more accurate than those commonly used.

Improving the magnetoelectric performance of Metglas/PZT laminates by annealing in a magnetic field.

PubMed

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 .

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.

Field development planning using simulated annealing - optimal economic well scheduling and placement

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

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.

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.

Elimination of Bimodal Size in InAs/GaAs Quantum Dots for Preparation of 1.3-μm Quantum Dot Lasers.

PubMed

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.

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.

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.

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.

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°.

Robust quantum optimizer with full connectivity

PubMed Central

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

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.

A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems

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.

A synthetic biology approach to the development of transcriptional regulatory models and custom enhancer design☆,☆☆

PubMed Central

Martinez, Carlos A.; Barr, Kenneth; Kim, Ah-Ram; Reinitz, John

2013-01-01

Synthetic biology offers novel opportunities for elucidating transcriptional regulatory mechanisms and enhancer logic. Complex cis-regulatory sequences—like the ones driving expression of the Drosophila even-skipped gene—have proven difficult to design from existing knowledge, presumably due to the large number of protein-protein interactions needed to drive the correct expression patterns of genes in multicellular organisms. This work discusses two novel computational methods for the custom design of enhancers that employ a sophisticated, empirically validated transcriptional model, optimization algorithms, and synthetic biology. These synthetic elements have both utilitarian and academic value, including improving existing regulatory models as well as evolutionary questions. The first method involves the use of simulated annealing to explore the sequence space for synthetic enhancers whose expression output fit a given search criterion. The second method uses a novel optimization algorithm to find functionally accessible pathways between two enhancer sequences. These paths describe a set of mutations wherein the predicted expression pattern does not significantly vary at any point along the path. Both methods rely on a predictive mathematical framework that maps the enhancer sequence space to functional output. PMID:23732772

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.

Preparation, optimization and property of PVA-HA/PAA composite hydrogel.

PubMed

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.

Solving the patient zero inverse problem by using generalized simulated annealing

NASA Astrophysics Data System (ADS)

Menin, Olavo H.; Bauch, Chris T.

2018-01-01

Identifying patient zero - the initially infected source of a given outbreak - is an important step in epidemiological investigations of both existing and emerging infectious diseases. Here, the use of the Generalized Simulated Annealing algorithm (GSA) to solve the inverse problem of finding the source of an outbreak is studied. The classical disease natural histories susceptible-infected (SI), susceptible-infected-susceptible (SIS), susceptible-infected-recovered (SIR) and susceptible-infected-recovered-susceptible (SIRS) in a regular lattice are addressed. Both the position of patient zero and its time of infection are considered unknown. The algorithm performance with respect to the generalization parameter q˜v and the fraction ρ of infected nodes for whom infection was ascertained is assessed. Numerical experiments show the algorithm is able to retrieve the epidemic source with good accuracy, even when ρ is small, but present no evidence to support that GSA performs better than its classical version. Our results suggest that simulated annealing could be a helpful tool for identifying patient zero in an outbreak where not all cases can be ascertained.

A quantum annealing architecture with all-to-all connectivity from local interactions.

PubMed

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.

A quantum annealing architecture with all-to-all connectivity from local interactions

PubMed Central

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

Training Scalable Restricted Boltzmann Machines Using a Quantum Annealer

NASA Astrophysics Data System (ADS)

Kumar, V.; Bass, G.; Dulny, J., III

2016-12-01

Machine learning and the optimization involved therein is of critical importance for commercial and military applications. Due to the computational complexity of many-variable optimization, the conventional approach is to employ meta-heuristic techniques to find suboptimal solutions. Quantum Annealing (QA) hardware offers a completely novel approach with the potential to obtain significantly better solutions with large speed-ups compared to traditional computing. In this presentation, we describe our development of new machine learning algorithms tailored for QA hardware. We are training restricted Boltzmann machines (RBMs) using QA hardware on large, high-dimensional commercial datasets. Traditional optimization heuristics such as contrastive divergence and other closely related techniques are slow to converge, especially on large datasets. Recent studies have indicated that QA hardware when used as a sampler provides better training performance compared to conventional approaches. Most of these studies have been limited to moderately-sized datasets due to the hardware restrictions imposed by exisitng QA devices, which make it difficult to solve real-world problems at scale. In this work we develop novel strategies to circumvent this issue. We discuss scale-up techniques such as enhanced embedding and partitioned RBMs which allow large commercial datasets to be learned using QA hardware. We present our initial results obtained by training an RBM as an autoencoder on an image dataset. The results obtained so far indicate that the convergence rates can be improved significantly by increasing RBM network connectivity. These ideas can be readily applied to generalized Boltzmann machines and we are currently investigating this in an ongoing project.

Quantum versus simulated annealing in wireless interference network optimization.

PubMed

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.

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.

Quantum versus simulated annealing in wireless interference network optimization

PubMed Central

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

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.

A Bootstrap Metropolis-Hastings Algorithm for Bayesian Analysis of Big Data.

PubMed

Liang, Faming; Kim, Jinsu; Song, Qifan

2016-01-01

Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their computer-intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. In this paper, we propose the so-called bootstrap Metropolis-Hastings (BMH) algorithm, which provides a general framework for how to tame powerful MCMC methods to be used for big data analysis; that is to replace the full data log-likelihood by a Monte Carlo average of the log-likelihoods that are calculated in parallel from multiple bootstrap samples. The BMH algorithm possesses an embarrassingly parallel structure and avoids repeated scans of the full dataset in iterations, and is thus feasible for big data problems. Compared to the popular divide-and-combine method, BMH can be generally more efficient as it can asymptotically integrate the whole data information into a single simulation run. The BMH algorithm is very flexible. Like the Metropolis-Hastings algorithm, it can serve as a basic building block for developing advanced MCMC algorithms that are feasible for big data problems. This is illustrated in the paper by the tempering BMH algorithm, which can be viewed as a combination of parallel tempering and the BMH algorithm. BMH can also be used for model selection and optimization by combining with reversible jump MCMC and simulated annealing, respectively.

A Bootstrap Metropolis–Hastings Algorithm for Bayesian Analysis of Big Data

PubMed Central

Kim, Jinsu; Song, Qifan

2016-01-01

Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their computer-intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. In this paper, we propose the so-called bootstrap Metropolis-Hastings (BMH) algorithm, which provides a general framework for how to tame powerful MCMC methods to be used for big data analysis; that is to replace the full data log-likelihood by a Monte Carlo average of the log-likelihoods that are calculated in parallel from multiple bootstrap samples. The BMH algorithm possesses an embarrassingly parallel structure and avoids repeated scans of the full dataset in iterations, and is thus feasible for big data problems. Compared to the popular divide-and-combine method, BMH can be generally more efficient as it can asymptotically integrate the whole data information into a single simulation run. The BMH algorithm is very flexible. Like the Metropolis-Hastings algorithm, it can serve as a basic building block for developing advanced MCMC algorithms that are feasible for big data problems. This is illustrated in the paper by the tempering BMH algorithm, which can be viewed as a combination of parallel tempering and the BMH algorithm. BMH can also be used for model selection and optimization by combining with reversible jump MCMC and simulated annealing, respectively. PMID:29033469

Designing a practical system for spectral imaging of skylight.

PubMed

López-Alvarez, Miguel A; Hernández-Andrés, Javier; Romero, Javier; Lee, Raymond L

2005-09-20

In earlier work [J. Opt. Soc. Am. A 21, 13-23 (2004)], we showed that a combination of linear models and optimum Gaussian sensors obtained by an exhaustive search can recover daylight spectra reliably from broadband sensor data. Thus our algorithm and sensors could be used to design an accurate, relatively inexpensive system for spectral imaging of daylight. Here we improve our simulation of the multispectral system by (1) considering the different kinds of noise inherent in electronic devices such as change-coupled devices (CCDs) or complementary metal-oxide semiconductors (CMOS) and (2) extending our research to a different kind of natural illumination, skylight. Because exhaustive searches are expensive computationally, here we switch to a simulated annealing algorithm to define the optimum sensors for recovering skylight spectra. The annealing algorithm requires us to minimize a single cost function, and so we develop one that calculates both the spectral and colorimetric similarity of any pair of skylight spectra. We show that the simulated annealing algorithm yields results similar to the exhaustive search but with much less computational effort. Our technique lets us study the properties of optimum sensors in the presence of noise, one side effect of which is that adding more sensors may not improve the spectral recovery.

Protein structure modeling for CASP10 by multiple layers of global optimization.

PubMed

Joo, Keehyoung; Lee, Juyong; Sim, Sangjin; Lee, Sun Young; Lee, Kiho; Heo, Seungryong; Lee, In-Ho; Lee, Sung Jong; Lee, Jooyoung

2014-02-01

In the template-based modeling (TBM) category of CASP10 experiment, we introduced a new protocol called protein modeling system (PMS) to generate accurate protein structures in terms of side-chains as well as backbone trace. In the new protocol, a global optimization algorithm, called conformational space annealing (CSA), is applied to the three layers of TBM procedure: multiple sequence-structure alignment, 3D chain building, and side-chain re-modeling. For 3D chain building, we developed a new energy function which includes new distance restraint terms of Lorentzian type (derived from multiple templates), and new energy terms that combine (physical) energy terms such as dynamic fragment assembly (DFA) energy, DFIRE statistical potential energy, hydrogen bonding term, etc. These physical energy terms are expected to guide the structure modeling especially for loop regions where no template structures are available. In addition, we developed a new quality assessment method based on random forest machine learning algorithm to screen templates, multiple alignments, and final models. For TBM targets of CASP10, we find that, due to the combination of three stages of CSA global optimizations and quality assessment, the modeling accuracy of PMS improves at each additional stage of the protocol. It is especially noteworthy that the side-chains of the final PMS models are far more accurate than the models in the intermediate steps. Copyright © 2013 Wiley Periodicals, Inc.

Optimization of a large-scale microseismic monitoring network in northern Switzerland

NASA Astrophysics Data System (ADS)

Kraft, Toni; Mignan, Arnaud; Giardini, Domenico

2013-10-01

We have developed a network optimization method for regional-scale microseismic monitoring networks and applied it to optimize the densification of the existing seismic network in northeastern Switzerland. The new network will build the backbone of a 10-yr study on the neotectonic activity of this area that will help to better constrain the seismic hazard imposed on nuclear power plants and waste repository sites. This task defined the requirements regarding location precision (0.5 km in epicentre and 2 km in source depth) and detection capability [magnitude of completeness Mc = 1.0 (ML)]. The goal of the optimization was to find the geometry and size of the network that met these requirements. Existing stations in Switzerland, Germany and Austria were considered in the optimization procedure. We based the optimization on the simulated annealing approach proposed by Hardt & Scherbaum, which aims to minimize the volume of the error ellipsoid of the linearized earthquake location problem (D-criterion). We have extended their algorithm to: calculate traveltimes of seismic body waves using a finite difference ray tracer and the 3-D velocity model of Switzerland, calculate seismic body-wave amplitudes at arbitrary stations assuming the Brune source model and using scaling and attenuation relations recently derived for Switzerland, and estimate the noise level at arbitrary locations within Switzerland using a first-order ambient seismic noise model based on 14 land-use classes defined by the EU-project CORINE and open GIS data. We calculated optimized geometries for networks with 10-35 added stations and tested the stability of the optimization result by repeated runs with changing initial conditions. Further, we estimated the attainable magnitude of completeness (Mc) for the different sized optimal networks using the Bayesian Magnitude of Completeness (BMC) method introduced by Mignan et al. The algorithm developed in this study is also applicable to smaller optimization problems, for example, small local monitoring networks. Possible applications are volcano monitoring, the surveillance of induced seismicity associated with geotechnical operations and many more. Our algorithm is especially useful to optimize networks in populated areas with heterogeneous noise conditions and if complex velocity structures or existing stations have to be considered.

An Early Quantum Computing Proposal

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Stephen Russell; Alexander, Francis Joseph; Barros, Kipton Marcos

The D-Wave 2X is the third generation of quantum processing created by D-Wave. NASA (with Google and USRA) and Lockheed Martin (with USC), both own D-Wave systems. Los Alamos National Laboratory (LANL) purchased a D-Wave 2X in November 2015. The D-Wave 2X processor contains (nominally) 1152 quantum bits (or qubits) and is designed to specifically perform quantum annealing, which is a well-known method for finding a global minimum of an optimization problem. This methodology is based on direct execution of a quantum evolution in experimental quantum hardware. While this can be a powerful method for solving particular kinds of problems,more » it also means that the D-Wave 2X processor is not a general computing processor and cannot be programmed to perform a wide variety of tasks. It is a highly specialized processor, well beyond what NNSA currently thinks of as an “advanced architecture.”A D-Wave is best described as a quantum optimizer. That is, it uses quantum superposition to find the lowest energy state of a system by repeated doses of power and settling stages. The D-Wave produces multiple solutions to any suitably formulated problem, one of which is the lowest energy state solution (global minimum). Mapping problems onto the D-Wave requires defining an objective function to be minimized and then encoding that function in the Hamiltonian of the D-Wave system. The quantum annealing method is then used to find the lowest energy configuration of the Hamiltonian using the current D-Wave Two, two-level, quantum processor. This is not always an easy thing to do, and the D-Wave Two has significant limitations that restrict problem sizes that can be run and algorithmic choices that can be made. Furthermore, as more people are exploring this technology, it has become clear that it is very difficult to come up with general approaches to optimization that can both utilize the D-Wave and that can do better than highly developed algorithms on conventional computers for specific applications. These are all fundamental challenges that must be overcome for the D-Wave, or similar, quantum computing technology to be broadly applicable.« less

Adaptation of the CVT algorithm for catheter optimization in high dose rate brachytherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poulin, Eric; Fekete, Charles-Antoine Collins; Beaulieu, Luc

2013-11-15

Purpose: An innovative, simple, and fast method to optimize the number and position of catheters is presented for prostate and breast high dose rate (HDR) brachytherapy, both for arbitrary templates or template-free implants (such as robotic templates).Methods: Eight clinical cases were chosen randomly from a bank of patients, previously treated in our clinic to test our method. The 2D Centroidal Voronoi Tessellations (CVT) algorithm was adapted to distribute catheters uniformly in space, within the maximum external contour of the planning target volume. The catheters optimization procedure includes the inverse planning simulated annealing algorithm (IPSA). Complete treatment plans can then bemore » generated from the algorithm for different number of catheters. The best plan is chosen from different dosimetry criteria and will automatically provide the number of catheters and their positions. After the CVT algorithm parameters were optimized for speed and dosimetric results, it was validated against prostate clinical cases, using clinically relevant dose parameters. The robustness to implantation error was also evaluated. Finally, the efficiency of the method was tested in breast interstitial HDR brachytherapy cases.Results: The effect of the number and locations of the catheters on prostate cancer patients was studied. Treatment plans with a better or equivalent dose distributions could be obtained with fewer catheters. A better or equal prostate V100 was obtained down to 12 catheters. Plans with nine or less catheters would not be clinically acceptable in terms of prostate V100 and D90. Implantation errors up to 3 mm were acceptable since no statistical difference was found when compared to 0 mm error (p > 0.05). No significant difference in dosimetric indices was observed for the different combination of parameters within the CVT algorithm. A linear relation was found between the number of random points and the optimization time of the CVT algorithm. Because the computation time decrease with the number of points and that no effects were observed on the dosimetric indices when varying the number of sampling points and the number of iterations, they were respectively fixed to 2500 and to 100. The computation time to obtain ten complete treatments plans ranging from 9 to 18 catheters, with the corresponding dosimetric indices, was 90 s. However, 93% of the computation time is used by a research version of IPSA. For the breast, on average, the Radiation Therapy Oncology Group recommendations would be satisfied down to 12 catheters. Plans with nine or less catheters would not be clinically acceptable in terms of V100, dose homogeneity index, and D90.Conclusions: The authors have devised a simple, fast and efficient method to optimize the number and position of catheters in interstitial HDR brachytherapy. The method was shown to be robust for both prostate and breast HDR brachytherapy. More importantly, the computation time of the algorithm is acceptable for clinical use. Ultimately, this catheter optimization algorithm could be coupled with a 3D ultrasound system to allow real-time guidance and planning in HDR brachytherapy.« less

Epidemic spreading in annealed directed networks: susceptible-infected-susceptible model and contact process.

PubMed

Kwon, Sungchul; Kim, Yup

2013-01-01

We investigate epidemic spreading in annealed directed scale-free networks with the in-degree (k) distribution P(in)(k)~k(-γ(in)) and the out-degree (ℓ) distribution, P(out)(ℓ)~ℓ(-γ(out)). The correlation of each node on the networks is controlled by the probability r(0≤r≤1) in two different algorithms, the so-called k and ℓ algorithms. For r=1, the k algorithm gives =, whereas the ℓ algorithm gives =<ℓ(2)>. For r=0, =<ℓ> for both algorithms. As the prototype of epidemic spreading, the susceptible-infected-susceptible model and contact process on the networks are analyzed using the heterogeneous mean-field theory and Monte Carlo simulations. The directedness of links and the correlation of the network are found to play important roles in the spreading, so that critical behaviors of both models are distinct from those on undirected scale-free networks.

Enhanced magnetic refrigeration properties in Mn-rich Ni-Mn-Sn ribbons by optimal annealing

PubMed Central

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

An implementation of differential evolution algorithm for inversion of geoelectrical data

NASA Astrophysics Data System (ADS)

Balkaya, Çağlayan

2013-11-01

Differential evolution (DE), a population-based evolutionary algorithm (EA) has been implemented to invert self-potential (SP) and vertical electrical sounding (VES) data sets. The algorithm uses three operators including mutation, crossover and selection similar to genetic algorithm (GA). Mutation is the most important operator for the success of DE. Three commonly used mutation strategies including DE/best/1 (strategy 1), DE/rand/1 (strategy 2) and DE/rand-to-best/1 (strategy 3) were applied together with a binomial type crossover. Evolution cycle of DE was realized without boundary constraints. For the test studies performed with SP data, in addition to both noise-free and noisy synthetic data sets two field data sets observed over the sulfide ore body in the Malachite mine (Colorado) and over the ore bodies in the Neem-Ka Thana cooper belt (India) were considered. VES test studies were carried out using synthetically produced resistivity data representing a three-layered earth model and a field data set example from Gökçeada (Turkey), which displays a seawater infiltration problem. Mutation strategies mentioned above were also extensively tested on both synthetic and field data sets in consideration. Of these, strategy 1 was found to be the most effective strategy for the parameter estimation by providing less computational cost together with a good accuracy. The solutions obtained by DE for the synthetic cases of SP were quite consistent with particle swarm optimization (PSO) which is a more widely used population-based optimization algorithm than DE in geophysics. Estimated parameters of SP and VES data were also compared with those obtained from Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing (SA) without cooling to clarify uncertainties in the solutions. Comparison to the M-H algorithm shows that DE performs a fast approximate posterior sampling for the case of low-dimensional inverse geophysical problems.

Optimization of digital droplet polymerase chain reaction for quantification of genetically modified organisms

PubMed Central

Gerdes, Lars; Iwobi, Azuka; Busch, Ulrich; Pecoraro, Sven

2016-01-01

Digital PCR in droplets (ddPCR) is an emerging method for more and more applications in DNA (and RNA) analysis. Special requirements when establishing ddPCR for analysis of genetically modified organisms (GMO) in a laboratory include the choice between validated official qPCR methods and the optimization of these assays for a ddPCR format. Differentiation between droplets with positive reaction and negative droplets, that is setting of an appropriate threshold, can be crucial for a correct measurement. This holds true in particular when independent transgene and plant-specific reference gene copy numbers have to be combined to determine the content of GM material in a sample. Droplets which show fluorescent units ranging between those of explicit positive and negative droplets are called ‘rain’. Signals of such droplets can hinder analysis and the correct setting of a threshold. In this manuscript, a computer-based algorithm has been carefully designed to evaluate assay performance and facilitate objective criteria for assay optimization. Optimized assays in return minimize the impact of rain on ddPCR analysis. We developed an Excel based ‘experience matrix’ that reflects the assay parameters of GMO ddPCR tests performed in our laboratory. Parameters considered include singleplex/duplex ddPCR, assay volume, thermal cycler, probe manufacturer, oligonucleotide concentration, annealing/elongation temperature, and a droplet separation evaluation. We additionally propose an objective droplet separation value which is based on both absolute fluorescence signal distance of positive and negative droplet populations and the variation within these droplet populations. The proposed performance classification in the experience matrix can be used for a rating of different assays for the same GMO target, thus enabling employment of the best suited assay parameters. Main optimization parameters include annealing/extension temperature and oligonucleotide concentrations. The droplet separation value allows for easy and reproducible assay performance evaluation. The combination of separation value with the experience matrix simplifies the choice of adequate assay parameters for a given GMO event. PMID:27077048

Optimization of digital droplet polymerase chain reaction for quantification of genetically modified organisms.

PubMed

Gerdes, Lars; Iwobi, Azuka; Busch, Ulrich; Pecoraro, Sven

2016-03-01

Digital PCR in droplets (ddPCR) is an emerging method for more and more applications in DNA (and RNA) analysis. Special requirements when establishing ddPCR for analysis of genetically modified organisms (GMO) in a laboratory include the choice between validated official qPCR methods and the optimization of these assays for a ddPCR format. Differentiation between droplets with positive reaction and negative droplets, that is setting of an appropriate threshold, can be crucial for a correct measurement. This holds true in particular when independent transgene and plant-specific reference gene copy numbers have to be combined to determine the content of GM material in a sample. Droplets which show fluorescent units ranging between those of explicit positive and negative droplets are called 'rain'. Signals of such droplets can hinder analysis and the correct setting of a threshold. In this manuscript, a computer-based algorithm has been carefully designed to evaluate assay performance and facilitate objective criteria for assay optimization. Optimized assays in return minimize the impact of rain on ddPCR analysis. We developed an Excel based 'experience matrix' that reflects the assay parameters of GMO ddPCR tests performed in our laboratory. Parameters considered include singleplex/duplex ddPCR, assay volume, thermal cycler, probe manufacturer, oligonucleotide concentration, annealing/elongation temperature, and a droplet separation evaluation. We additionally propose an objective droplet separation value which is based on both absolute fluorescence signal distance of positive and negative droplet populations and the variation within these droplet populations. The proposed performance classification in the experience matrix can be used for a rating of different assays for the same GMO target, thus enabling employment of the best suited assay parameters. Main optimization parameters include annealing/extension temperature and oligonucleotide concentrations. The droplet separation value allows for easy and reproducible assay performance evaluation. The combination of separation value with the experience matrix simplifies the choice of adequate assay parameters for a given GMO event.

Cyclical Annealing Technique To Enhance Reliability of Amorphous Metal Oxide Thin Film Transistors.

PubMed

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.

Direct Immersion Annealing of Thin Block Copolymer Films.

PubMed

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.

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

Stochastic Simulation of Actin Dynamics Reveals the Role of Annealing and Fragmentation

PubMed Central

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

Patient-specific Monte Carlo-based dose-kernel approach for inverse planning in afterloading brachytherapy.

PubMed

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.

Improved amorphous/crystalline silicon interface passivation for heterojunction solar cells by low-temperature chemical vapor deposition and post-annealing treatment.

PubMed

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.

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

Room-temperature annealing effects on the basal-plane resistivity of optimally doped YBa2Cu3O7-δ single crystals

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.

Multicycle rapid thermal annealing optimization of Mg-implanted GaN: Evolution of surface, optical, and structural properties

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.

Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment.

PubMed

Abdullahi, Mohammed; Ngadi, Md Asri

2016-01-01

Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.

Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment

PubMed Central

Abdullahi, Mohammed; Ngadi, Md Asri

2016-01-01

Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan. PMID:27348127

Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance.

PubMed

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.

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.

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.

A random-key encoded harmony search approach for energy-efficient production scheduling with shared resources

NASA Astrophysics Data System (ADS)

Garcia-Santiago, C. A.; Del Ser, J.; Upton, C.; Quilligan, F.; Gil-Lopez, S.; Salcedo-Sanz, S.

2015-11-01

When seeking near-optimal solutions for complex scheduling problems, meta-heuristics demonstrate good performance with affordable computational effort. This has resulted in a gravitation towards these approaches when researching industrial use-cases such as energy-efficient production planning. However, much of the previous research makes assumptions about softer constraints that affect planning strategies and about how human planners interact with the algorithm in a live production environment. This article describes a job-shop problem that focuses on minimizing energy consumption across a production facility of shared resources. The application scenario is based on real facilities made available by the Irish Center for Manufacturing Research. The formulated problem is tackled via harmony search heuristics with random keys encoding. Simulation results are compared to a genetic algorithm, a simulated annealing approach and a first-come-first-served scheduling. The superior performance obtained by the proposed scheduler paves the way towards its practical implementation over industrial production chains.

The Local Minima Problem in Hierarchical Classes Analysis: An Evaluation of a Simulated Annealing Algorithm and Various Multistart Procedures

ERIC Educational Resources Information Center

Ceulemans, Eva; Van Mechelen, Iven; Leenen, Iwin

2007-01-01

Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data. To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed. Extensive simulation studies showed that these algorithms succeed quite…

Effect of laser processing on physical properties of (Ba0.85Ca0.15Ti0.9Zr0.1O3) lead-free thick films fabricated by the electrophoretic deposition

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.

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.

A New Artificial Neural Network Enhanced by the Shuffled Complex Evolution Optimization with Principal Component Analysis (SP-UCI) for Water Resources Management

NASA Astrophysics Data System (ADS)

Hayatbini, N.; Faridzad, M.; Yang, T.; Akbari Asanjan, A.; Gao, X.; Sorooshian, S.

2016-12-01

The Artificial Neural Networks (ANNs) are useful in many fields, including water resources engineering and management. However, due to the non-linear and chaotic characteristics associated with natural processes and human decision making, the use of ANNs in real-world applications is still limited, and its performance needs to be further improved for a broader practical use. The commonly used Back-Propagation (BP) scheme and gradient-based optimization in training the ANNs have already found to be problematic in some cases. The BP scheme and gradient-based optimization methods are associated with the risk of premature convergence, stuck in local optimums, and the searching is highly dependent on initial conditions. Therefore, as an alternative to BP and gradient-based searching scheme, we propose an effective and efficient global searching method, termed the Shuffled Complex Evolutionary Global optimization algorithm with Principal Component Analysis (SP-UCI), to train the ANN connectivity weights. Large number of real-world datasets are tested with the SP-UCI-based ANN, as well as various popular Evolutionary Algorithms (EAs)-enhanced ANNs, i.e., Particle Swarm Optimization (PSO)-, Genetic Algorithm (GA)-, Simulated Annealing (SA)-, and Differential Evolution (DE)-enhanced ANNs. Results show that SP-UCI-enhanced ANN is generally superior over other EA-enhanced ANNs with regard to the convergence and computational performance. In addition, we carried out a case study for hydropower scheduling in the Trinity Lake in the western U.S. In this case study, multiple climate indices are used as predictors for the SP-UCI-enhanced ANN. The reservoir inflows and hydropower releases are predicted up to sub-seasonal to seasonal scale. Results show that SP-UCI-enhanced ANN is able to achieve better statistics than other EAs-based ANN, which implies the usefulness and powerfulness of proposed SP-UCI-enhanced ANN for reservoir operation, water resources engineering and management. The SP-UCI-enhanced ANN is universally applicable to many other regression and prediction problems, and it has a good potential to be an alternative to the classical BP scheme and gradient-based optimization methods.

Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges

NASA Astrophysics Data System (ADS)

Ketabchi, Hamed; Ataie-Ashtiani, Behzad

2015-01-01

This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied on the application of limited and particular EAs, mainly genetic algorithm (GA) and its variants, to a number of specific problems. The exclusive investigation of these problems is often not the representation of the variety of feasible processes may be occurred in coastal aquifers. In this study, eight EAs are evaluated for CGMPs. The considered EAs are: GA, continuous ant colony optimization (CACO), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony optimization (ABC), harmony search (HS), shuffled complex evolution (SCE), and simplex simulated annealing (SIMPSA). The first application of PSO, ABC, HS, and SCE in CGMPs is reported here. Moreover, the four benchmark problems with different degree of difficulty and variety are considered to address the important issues of groundwater resources in coastal regions. Hence, the wide ranges of popular objective functions and constraints with the number of decision variables ranging from 4 to 15 are included. These benchmark problems are applied in the combined simulation-optimization model to examine the optimization scenarios. Some preliminary experiments are performed to select the most efficient parameters values for EAs to set a fair comparison. The specific capabilities of each EA toward CGMPs in terms of results quality and required computational time are compared. The evaluation of the results highlights EA's applicability in CGMPs, besides the remarkable strengths and weaknesses of them. The comparisons show that SCE, CACO, and PSO yield superior solutions among the EAs according to the quality of solutions whereas ABC presents the poor performance. CACO provides the better solutions (up to 17%) than the worst EA (ABC) for the problem with the highest decision variables and more complexity. In terms of computational time, PSO and SIMPSA are the fastest. SCE needs the highest computational time, even up to four times in comparison to the fastest EAs. CACO and PSO can be recommended for application in CGMPs, in terms of both abovementioned criteria.

Multicompare tests of the performance of different metaheuristics in EEG dipole source localization.

PubMed

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.

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.

Design of focused and restrained subsets from extremely large virtual libraries.

PubMed

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).

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.

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.

A location selection policy of live virtual machine migration for power saving and load balancing.

PubMed

Zhao, Jia; Ding, Yan; Xu, Gaochao; Hu, Liang; Dong, Yushuang; Fu, Xiaodong

2013-01-01

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy) of live VM migration for power saving and load balancing. We propose a novel approach MOGA-LS, which is a heuristic and self-adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). This paper has presented the specific design and implementation of MOGA-LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA) idea into MOGA-LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long-term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA-LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

A Location Selection Policy of Live Virtual Machine Migration for Power Saving and Load Balancing

PubMed Central

Xu, Gaochao; Hu, Liang; Dong, Yushuang; Fu, Xiaodong

2013-01-01

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy) of live VM migration for power saving and load balancing. We propose a novel approach MOGA-LS, which is a heuristic and self-adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). This paper has presented the specific design and implementation of MOGA-LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA) idea into MOGA-LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long-term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA-LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high-effective and meaningful. PMID:24348165

Propeller sheet cavitation noise source modeling and inversion

NASA Astrophysics Data System (ADS)

Lee, Keunhwa; Lee, Jaehyuk; Kim, Dongho; Kim, Kyungseop; Seong, Woojae

2014-02-01

Propeller sheet cavitation is the main contributor to high level of noise and vibration in the after body of a ship. Full measurement of the cavitation-induced hull pressure over the entire surface of the affected area is desired but not practical. Therefore, using a few measurements on the outer hull above the propeller in a cavitation tunnel, empirical or semi-empirical techniques based on physical model have been used to predict the hull-induced pressure (or hull-induced force). In this paper, with the analytic source model for sheet cavitation, a multi-parameter inversion scheme to find the positions of noise sources and their strengths is suggested. The inversion is posed as a nonlinear optimization problem, which is solved by the optimization algorithm based on the adaptive simplex simulated annealing algorithm. Then, the resulting hull pressure can be modeled with boundary element method from the inverted cavitation noise sources. The suggested approach is applied to the hull pressure data measured in a cavitation tunnel of the Samsung Heavy Industry. Two monopole sources are adequate to model the propeller sheet cavitation noise. The inverted source information is reasonable with the cavitation dynamics of the propeller and the modeled hull pressure shows good agreement with cavitation tunnel experimental data.

High Mobility Flexible Amorphous IGZO Thin-Film Transistors with a Low Thermal Budget Ultra-Violet Pulsed Light Process.

PubMed

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.

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.

Selecting materialized views using random algorithm

NASA Astrophysics Data System (ADS)

Zhou, Lijuan; Hao, Zhongxiao; Liu, Chi

2007-04-01

The data warehouse is a repository of information collected from multiple possibly heterogeneous autonomous distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The selection of the materialized views is one of the most important decisions in designing a data warehouse. Materialized views are stored in the data warehouse for the purpose of efficiently implementing on-line analytical processing queries. The first issue for the user to consider is query response time. So in this paper, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query_cost view_ selection problem. First, cost graph and cost model of query_cost view_ selection problem are presented. Second, the methods for selecting materialized views by using random algorithms are presented. The genetic algorithm is applied to the materialized views selection problem. But with the development of genetic process, the legal solution produced become more and more difficult, so a lot of solutions are eliminated and producing time of the solutions is lengthened in genetic algorithm. Therefore, improved algorithm has been presented in this paper, which is the combination of simulated annealing algorithm and genetic algorithm for the purpose of solving the query cost view selection problem. Finally, in order to test the function and efficiency of our algorithms experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and works better in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.

Annealing Induced Re-crystallization in CH3NH3PbI3−xClx for High Performance Perovskite Solar Cells

PubMed Central

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

Maximizing the nurses' preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

NASA Astrophysics Data System (ADS)

Jafari, Hamed; Salmasi, Nasser

2015-09-01

The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital's demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses' preferences for working shifts and weekends off by considering several important factors such as hospital's policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first, the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49 % compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses.

Microprocessor-based integration of microfluidic control for the implementation of automated sensor monitoring and multithreaded optimization algorithms.

PubMed

Ezra, Elishai; Maor, Idan; Bavli, Danny; Shalom, Itai; Levy, Gahl; Prill, Sebastian; Jaeger, Magnus S; Nahmias, Yaakov

2015-08-01

Microfluidic applications range from combinatorial synthesis to high throughput screening, with platforms integrating analog perfusion components, digitally controlled micro-valves and a range of sensors that demand a variety of communication protocols. Currently, discrete control units are used to regulate and monitor each component, resulting in scattered control interfaces that limit data integration and synchronization. Here, we present a microprocessor-based control unit, utilizing the MS Gadgeteer open framework that integrates all aspects of microfluidics through a high-current electronic circuit that supports and synchronizes digital and analog signals for perfusion components, pressure elements, and arbitrary sensor communication protocols using a plug-and-play interface. The control unit supports an integrated touch screen and TCP/IP interface that provides local and remote control of flow and data acquisition. To establish the ability of our control unit to integrate and synchronize complex microfluidic circuits we developed an equi-pressure combinatorial mixer. We demonstrate the generation of complex perfusion sequences, allowing the automated sampling, washing, and calibrating of an electrochemical lactate sensor continuously monitoring hepatocyte viability following exposure to the pesticide rotenone. Importantly, integration of an optical sensor allowed us to implement automated optimization protocols that require different computational challenges including: prioritized data structures in a genetic algorithm, distributed computational efforts in multiple-hill climbing searches and real-time realization of probabilistic models in simulated annealing. Our system offers a comprehensive solution for establishing optimization protocols and perfusion sequences in complex microfluidic circuits.

Probing for quantum speedup in spin-glass problems with planted solutions

NASA Astrophysics Data System (ADS)

Hen, Itay; Job, Joshua; Albash, Tameem; Rønnow, Troels F.; Troyer, Matthias; Lidar, Daniel A.

2015-10-01

The availability of quantum annealing devices with hundreds of qubits has made the experimental demonstration of a quantum speedup for optimization problems a coveted, albeit elusive goal. Going beyond earlier studies of random Ising problems, here we introduce a method to construct a set of frustrated Ising-model optimization problems with tunable hardness. We study the performance of a D-Wave Two device (DW2) with up to 503 qubits on these problems and compare it to a suite of classical algorithms, including a highly optimized algorithm designed to compete directly with the DW2. The problems are generated around predetermined ground-state configurations, called planted solutions, which makes them particularly suitable for benchmarking purposes. The problem set exhibits properties familiar from constraint satisfaction (SAT) problems, such as a peak in the typical hardness of the problems, determined by a tunable clause density parameter. We bound the hardness regime where the DW2 device either does not or might exhibit a quantum speedup for our problem set. While we do not find evidence for a speedup for the hardest and most frustrated problems in our problem set, we cannot rule out that a speedup might exist for some of the easier, less frustrated problems. Our empirical findings pertain to the specific D-Wave processor and problem set we studied and leave open the possibility that future processors might exhibit a quantum speedup on the same problem set.

Protein structure prediction with local adjust tabu search algorithm

PubMed Central

2014-01-01

Background Protein folding structure prediction is one of the most challenging problems in the bioinformatics domain. Because of the complexity of the realistic protein structure, the simplified structure model and the computational method should be adopted in the research. The AB off-lattice model is one of the simplification models, which only considers two classes of amino acids, hydrophobic (A) residues and hydrophilic (B) residues. Results The main work of this paper is to discuss how to optimize the lowest energy configurations in 2D off-lattice model and 3D off-lattice model by using Fibonacci sequences and real protein sequences. In order to avoid falling into local minimum and faster convergence to the global minimum, we introduce a novel method (SATS) to the protein structure problem, which combines simulated annealing algorithm and tabu search algorithm. Various strategies, such as the new encoding strategy, the adaptive neighborhood generation strategy and the local adjustment strategy, are adopted successfully for high-speed searching the optimal conformation corresponds to the lowest energy of the protein sequences. Experimental results show that some of the results obtained by the improved SATS are better than those reported in previous literatures, and we can sure that the lowest energy folding state for short Fibonacci sequences have been found. Conclusions Although the off-lattice models is not very realistic, they can reflect some important characteristics of the realistic protein. It can be found that 3D off-lattice model is more like native folding structure of the realistic protein than 2D off-lattice model. In addition, compared with some previous researches, the proposed hybrid algorithm can more effectively and more quickly search the spatial folding structure of a protein chain. PMID:25474708

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.

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

Multicycle rapid thermal annealing optimization of Mg-implanted GaN: Evolution of surface, optical, and structural properties

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

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.

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.

Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance

DOE PAGES

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

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

Optimized thin film coatings for passive radiative cooling applications

NASA Astrophysics Data System (ADS)

Naghshine, Babak B.; Saboonchi, Ahmad

2018-03-01

Passive radiative cooling is a very interesting method, which lays on low atmospheric downward radiation within 8-13 μm waveband at dry climates. Various thin film multilayer structures have been investigated in numerous experimental studies, in order to find better coatings to exploit the full potential of this method. However, theoretical works are handful and limited. In this paper, the Simulated Annealing and Genetic Algorithm are used to optimize a thin film multilayer structure for passive radiative cooling applications. Spectral radiative properties are calculated through the matrix formulation. Considering a wide range of materials, 30 high-potential convective shields are suggested. According to the calculations, cooling can be possible even under direct sunlight, using the introduced shields. Moreover, a few water-soluble materials are studied for the first time and the results show that, a KBr substrate coated by a thin CaF2 or polyethylene film can is very close to an ideal coating for passive radiative cooling at night.

Geostatistical modeling of riparian forest microclimate and its implications for sampling

USGS Publications Warehouse

Eskelson, B.N.I.; Anderson, P.D.; Hagar, J.C.; Temesgen, H.

2011-01-01

Predictive models of microclimate under various site conditions in forested headwater stream - riparian areas are poorly developed, and sampling designs for characterizing underlying riparian microclimate gradients are sparse. We used riparian microclimate data collected at eight headwater streams in the Oregon Coast Range to compare ordinary kriging (OK), universal kriging (UK), and kriging with external drift (KED) for point prediction of mean maximum air temperature (Tair). Several topographic and forest structure characteristics were considered as site-specific parameters. Height above stream and distance to stream were the most important covariates in the KED models, which outperformed OK and UK in terms of root mean square error. Sample patterns were optimized based on the kriging variance and the weighted means of shortest distance criterion using the simulated annealing algorithm. The optimized sample patterns outperformed systematic sample patterns in terms of mean kriging variance mainly for small sample sizes. These findings suggest methods for increasing efficiency of microclimate monitoring in riparian areas.

Optimizing Monitoring Designs under Alternative Objectives

DOE PAGES

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

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.

Iterative simulated quenching for designing irregular-spot-array generators.

PubMed

Gillet, J N; Sheng, Y

2000-07-10

We propose a novel, to our knowledge, algorithm of iterative simulated quenching with temperature rescaling for designing diffractive optical elements, based on an analogy between simulated annealing and statistical thermodynamics. The temperature is iteratively rescaled at the end of each quenching process according to ensemble statistics to bring the system back from a frozen imperfect state with a local minimum of energy to a dynamic state in a Boltzmann heat bath in thermal equilibrium at the rescaled temperature. The new algorithm achieves much lower cost function and reconstruction error and higher diffraction efficiency than conventional simulated annealing with a fast exponential cooling schedule and is easy to program. The algorithm is used to design binary-phase generators of large irregular spot arrays. The diffractive phase elements have trapezoidal apertures of varying heights, which fit ideal arbitrary-shaped apertures better than do trapezoidal apertures of fixed heights.

High-Throughput Sequencing of 16S rRNA Gene Amplicons: Effects of Extraction Procedure, Primer Length and Annealing Temperature

PubMed Central

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

Experimental quantum annealing: case study involving the graph isomorphism problem.

PubMed

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.

Experimental quantum annealing: case study involving the graph isomorphism problem

PubMed Central

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

High-throughput sequencing of 16S rRNA gene amplicons: effects of extraction procedure, primer length and annealing temperature.

PubMed

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.

An optimized proportional-derivative controller for the human upper extremity with gravity.

PubMed

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.

Quantum approach to classical statistical mechanics.

PubMed

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.

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

Optimization of PCR Condition: The First Study of High Resolution Melting Technique for Screening of APOA1 Variance.

PubMed

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 .

Optimization of PCR Condition: The First Study of High Resolution Melting Technique for Screening of APOA1 Variance

PubMed Central

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

Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology

PubMed Central

Murakami, Yohei

2014-01-01

Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named “posterior parameter ensemble”. We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor. PMID:25089832

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.

A Comparison of Approaches for Solving Hard Graph-Theoretic Problems

DTIC Science & Technology

2015-05-01

collaborative effort “ Adiabatic Quantum Computing Applications Research” (14-RI-CRADA-02) between the Information Directorate and Lock- 3 Algorithm 3...using Matlab, a quantum annealing approach using the D-Wave computer , and lastly using satisfiability modulo theory (SMT) and corresponding SMT...methods are explored and consist of a parallel computing approach using Matlab, a quantum annealing approach using the D-Wave computer , and lastly using

Prime factorization using quantum annealing and computational algebraic geometry

NASA Astrophysics Data System (ADS)

Dridi, Raouf; Alghassi, Hedayat

2017-02-01

We investigate prime factorization from two perspectives: quantum annealing and computational algebraic geometry, specifically Gröbner bases. We present a novel autonomous algorithm which combines the two approaches and leads to the factorization of all bi-primes up to just over 200000, the largest number factored to date using a quantum processor. We also explain how Gröbner bases can be used to reduce the degree of Hamiltonians.

Decoherence in adiabatic quantum computation

NASA Astrophysics Data System (ADS)

Albash, Tameem; Lidar, Daniel A.

2015-06-01

Recent experiments with increasingly larger numbers of qubits have sparked renewed interest in adiabatic quantum computation, and in particular quantum annealing. A central question that is repeatedly asked is whether quantum features of the evolution can survive over the long time scales used for quantum annealing relative to standard measures of the decoherence time. We reconsider the role of decoherence in adiabatic quantum computation and quantum annealing using the adiabatic quantum master-equation formalism. We restrict ourselves to the weak-coupling and singular-coupling limits, which correspond to decoherence in the energy eigenbasis and in the computational basis, respectively. We demonstrate that decoherence in the instantaneous energy eigenbasis does not necessarily detrimentally affect adiabatic quantum computation, and in particular that a short single-qubit T2 time need not imply adverse consequences for the success of the quantum adiabatic algorithm. We further demonstrate that boundary cancellation methods, designed to improve the fidelity of adiabatic quantum computing in the closed-system setting, remain beneficial in the open-system setting. To address the high computational cost of master-equation simulations, we also demonstrate that a quantum Monte Carlo algorithm that explicitly accounts for a thermal bosonic bath can be used to interpolate between classical and quantum annealing. Our study highlights and clarifies the significantly different role played by decoherence in the adiabatic and circuit models of quantum computing.

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.

Metal Amorphous Nanocomposite (MANC) Alloy Cores with Spatially Tuned Permeability for Advanced Power Magnetics Applications

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.

Metal Amorphous Nanocomposite (MANC) Alloy Cores with Spatially Tuned Permeability for Advanced Power Magnetics Applications

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.

Controlling superconductivity in La 2-xSr xCuO 4+δ by ozone and vacuum annealing

DOE PAGES

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

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

Thermodynamics, kinetics, and catalytic effect of dehydrogenation from MgH2 stepped surfaces and nanocluster: a DFT study

NASA Astrophysics Data System (ADS)

Reich, Jason; Wang, Linlin; Johnson, Duane

2013-03-01

We detail the results of a Density Functional Theory (DFT) based study of hydrogen desorption, including thermodynamics and kinetics with(out) catalytic dopants, on stepped (110) rutile and nanocluster MgH2. We investigate competing configurations (optimal surface and nanoparticle configurations) using simulated annealing with additional converged results at 0 K, necessary for finding the low-energy, doped MgH2 nanostructures. Thermodynamics of hydrogen desorption from unique dopant sites will be shown, as well as activation energies using the Nudged Elastic Band algorithm. To compare to experiment, both stepped structures and nanoclusters are required to understanding and predict the effects of ball milling. We demonstrate how these model systems relate to the intermediary sized structures typically seen in ball milling experiments.

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.

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.

Enhancement of photoluminescence from GaInNAsSb quantum wells upon annealing: improvement of material quality and carrier collection by the quantum well.

PubMed

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.

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.

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking.

PubMed

Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J

2014-01-01

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

A parallel row-based algorithm with error control for standard-cell replacement on a hypercube multiprocessor

NASA Technical Reports Server (NTRS)

Sargent, Jeff Scott

1988-01-01

A new row-based parallel algorithm for standard-cell placement targeted for execution on a hypercube multiprocessor is presented. Key features of this implementation include a dynamic simulated-annealing schedule, row-partitioning of the VLSI chip image, and two novel new approaches to controlling error in parallel cell-placement algorithms; Heuristic Cell-Coloring and Adaptive (Parallel Move) Sequence Control. Heuristic Cell-Coloring identifies sets of noninteracting cells that can be moved repeatedly, and in parallel, with no buildup of error in the placement cost. Adaptive Sequence Control allows multiple parallel cell moves to take place between global cell-position updates. This feedback mechanism is based on an error bound derived analytically from the traditional annealing move-acceptance profile. Placement results are presented for real industry circuits and the performance is summarized of an implementation on the Intel iPSC/2 Hypercube. The runtime of this algorithm is 5 to 16 times faster than a previous program developed for the Hypercube, while producing equivalent quality placement. An integrated place and route program for the Intel iPSC/2 Hypercube is currently being developed.

Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

PubMed Central

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

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.

Asset Allocation to Cover a Region of Piracy

DTIC Science & Technology

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

Prime factorization using quantum annealing and computational algebraic geometry

PubMed Central

Dridi, Raouf; Alghassi, Hedayat

2017-01-01

We investigate prime factorization from two perspectives: quantum annealing and computational algebraic geometry, specifically Gröbner bases. We present a novel autonomous algorithm which combines the two approaches and leads to the factorization of all bi-primes up to just over 200000, the largest number factored to date using a quantum processor. We also explain how Gröbner bases can be used to reduce the degree of Hamiltonians. PMID:28220854

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.

Connection between the conformation and emission properties of poly[2-methoxy-5-(2'-ethyl-hexyloxy)-1,4-phenylene vinylene] single molecules during thermal annealing

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.

A detailed dosimetric comparison between manual and inverse plans in HDR intracavitary/interstitial cervical cancer brachytherapy.

PubMed

Trnková, Petra; Baltas, Dimos; Karabis, Andreas; Stock, Markus; Dimopoulos, Johannes; Georg, Dietmar; Pötter, Richard; Kirisits, Christian

2010-12-01

The purpose of this study was to compare two inverse planning algorithms for cervical cancer brachytherapy and a conventional manual treatment planning according to the MUW (Medical University of Vienna) protocol. For 20 patients, manually optimized, and, inversely optimized treatment plans with Hybrid Inverse treatment Planning and Optimization (HIPO) and with Inverse Planning Simulated Annealing (IPSA) were created. Dosimetric parameters, absolute volumes of normal tissue receiving reference doses, absolute loading times of tandem, ring and interstitial needles, Paddick and COIN conformity indices were evaluated. HIPO was able to achieve a similar dose distribution to manual planning with the restriction of high dose regions. It reduced the loading time of needles and the overall treatment time. The values of both conformity indices were the lowest. IPSA was able to achieve acceptable dosimetric results. However, it overloaded the needles. This resulted in high dose regions located in the normal tissue. The Paddick index for the volume of two times prescribed dose was outstandingly low. HIPO can produce clinically acceptable treatment plans with the elimination of high dose regions in normal tissue. Compared to IPSA, it is an inverse optimization method which takes into account current clinical experience gained from manual treatment planning.

A detailed dosimetric comparison between manual and inverse plans in HDR intracavitary/interstitial cervical cancer brachytherapy

PubMed Central

Baltas, Dimos; Karabis, Andreas; Stock, Markus; Dimopoulos, Johannes; Georg, Dietmar; Pötter, Richard; Kirisits, Christian

2011-01-01

Purpose The purpose of this study was to compare two inverse planning algorithms for cervical cancer brachytherapy and a conventional manual treatment planning according to the MUW (Medical University of Vienna) protocol. Material and methods For 20 patients, manually optimized, and, inversely optimized treatment plans with Hybrid Inverse treatment Planning and Optimization (HIPO) and with Inverse Planning Simulated Annealing (IPSA) were created. Dosimetric parameters, absolute volumes of normal tissue receiving reference doses, absolute loading times of tandem, ring and interstitial needles, Paddick and COIN conformity indices were evaluated. Results HIPO was able to achieve a similar dose distribution to manual planning with the restriction of high dose regions. It reduced the loading time of needles and the overall treatment time. The values of both conformity indices were the lowest. IPSA was able to achieve acceptable dosimetric results. However, it overloaded the needles. This resulted in high dose regions located in the normal tissue. The Paddick index for the volume of two times prescribed dose was outstandingly low. Conclusions HIPO can produce clinically acceptable treatment plans with the elimination of high dose regions in normal tissue. Compared to IPSA, it is an inverse optimization method which takes into account current clinical experience gained from manual treatment planning. PMID:27853479

Bayesian cross-entropy methodology for optimal design of validation experiments

NASA Astrophysics Data System (ADS)

Jiang, X.; Mahadevan, S.

2006-07-01

An important concern in the design of validation experiments is how to incorporate the mathematical model in the design in order to allow conclusive comparisons of model prediction with experimental output in model assessment. The classical experimental design methods are more suitable for phenomena discovery and may result in a subjective, expensive, time-consuming and ineffective design that may adversely impact these comparisons. In this paper, an integrated Bayesian cross-entropy methodology is proposed to perform the optimal design of validation experiments incorporating the computational model. The expected cross entropy, an information-theoretic distance between the distributions of model prediction and experimental observation, is defined as a utility function to measure the similarity of two distributions. A simulated annealing algorithm is used to find optimal values of input variables through minimizing or maximizing the expected cross entropy. The measured data after testing with the optimum input values are used to update the distribution of the experimental output using Bayes theorem. The procedure is repeated to adaptively design the required number of experiments for model assessment, each time ensuring that the experiment provides effective comparison for validation. The methodology is illustrated for the optimal design of validation experiments for a three-leg bolted joint structure and a composite helicopter rotor hub component.

Markov random field model-based edge-directed image interpolation.

PubMed

Li, Min; Nguyen, Truong Q

2008-07-01

This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. In opposite to explicit edge directions, the local edge directions are indicated by length-16 weighting vectors. Implicitly, the weighting vectors are used to formulate geometric regularity (GR) constraint (smoothness along edges and sharpness across edges) and the GR constraint is imposed on the interpolated image through the Markov random field (MRF) model. Furthermore, under the maximum a posteriori-MRF framework, the desired interpolated image corresponds to the minimal energy state of a 2-D random field given the low-resolution image. Simulated annealing methods are used to search for the minimal energy state from the state space. To lower the computational complexity of MRF, a single-pass implementation is designed, which performs nearly as well as the iterative optimization. Simulation results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity. Compared to traditional methods and other edge-directed interpolation methods, the proposed method improves the subjective quality of the interpolated edges while maintaining a high PSNR level.

An efficient and scalable deformable model for virtual reality-based medical applications.

PubMed

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.

Estimating source parameters from deformation data, with an application to the March 1997 earthquake swarm off the Izu Peninsula, Japan

NASA Astrophysics Data System (ADS)

Cervelli, P.; Murray, M. H.; Segall, P.; Aoki, Y.; Kato, T.

2001-06-01

We have applied two Monte Carlo optimization techniques, simulated annealing and random cost, to the inversion of deformation data for fault and magma chamber geometry. These techniques involve an element of randomness that permits them to escape local minima and ultimately converge to the global minimum of misfit space. We have tested the Monte Carlo algorithms on two synthetic data sets. We have also compared them to one another in terms of their efficiency and reliability. We have applied the bootstrap method to estimate confidence intervals for the source parameters, including the correlations inherent in the data. Additionally, we present methods that use the information from the bootstrapping procedure to visualize the correlations between the different model parameters. We have applied these techniques to GPS, tilt, and leveling data from the March 1997 earthquake swarm off of the Izu Peninsula, Japan. Using the two Monte Carlo algorithms, we have inferred two sources, a dike and a fault, that fit the deformation data and the patterns of seismicity and that are consistent with the regional stress field.

Numerical Optimization of Density Functional Tight Binding Models: Application to Molecules Containing Carbon, Hydrogen, Nitrogen, and Oxygen

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krishnapriyan, A.; Yang, P.; Niklasson, A. M. N.

New parametrizations for semiempirical density functional tight binding (DFTB) theory have been developed by the numerical optimization of adjustable parameters to minimize errors in the atomization energy and interatomic forces with respect to ab initio calculated data. Initial guesses for the radial dependences of the Slater- Koster bond integrals and overlap integrals were obtained from minimum basis density functional theory calculations. The radial dependences of the pair potentials and the bond and overlap integrals were represented by simple analytic functions. The adjustable parameters in these functions were optimized by simulated annealing and steepest descent algorithms to minimize the value ofmore » an objective function that quantifies the error between the DFTB model and ab initio calculated data. The accuracy and transferability of the resulting DFTB models for the C, H, N, and O system were assessed by comparing the predicted atomization energies and equilibrium molecular geometries of small molecules that were not included in the training data from DFTB to ab initio data. The DFTB models provide accurate predictions of the properties of hydrocarbons and more complex molecules containing C, H, N, and O.« less

Numerical Optimization of Density Functional Tight Binding Models: Application to Molecules Containing Carbon, Hydrogen, Nitrogen, and Oxygen

DOE PAGES

Krishnapriyan, A.; Yang, P.; Niklasson, A. M. N.; ...

2017-10-17

New parametrizations for semiempirical density functional tight binding (DFTB) theory have been developed by the numerical optimization of adjustable parameters to minimize errors in the atomization energy and interatomic forces with respect to ab initio calculated data. Initial guesses for the radial dependences of the Slater- Koster bond integrals and overlap integrals were obtained from minimum basis density functional theory calculations. The radial dependences of the pair potentials and the bond and overlap integrals were represented by simple analytic functions. The adjustable parameters in these functions were optimized by simulated annealing and steepest descent algorithms to minimize the value ofmore » an objective function that quantifies the error between the DFTB model and ab initio calculated data. The accuracy and transferability of the resulting DFTB models for the C, H, N, and O system were assessed by comparing the predicted atomization energies and equilibrium molecular geometries of small molecules that were not included in the training data from DFTB to ab initio data. The DFTB models provide accurate predictions of the properties of hydrocarbons and more complex molecules containing C, H, N, and O.« less

Detection of Fiber Layer-Up Lamination Order of CFRP Composite Using Thermal-Wave Radar Imaging

NASA Astrophysics Data System (ADS)

Wang, Fei; Liu, Junyan; Liu, Yang; Wang, Yang; Gong, Jinlong

2016-09-01

In this paper, thermal-wave radar imaging (TWRI) is used as a nondestructive inspection method to evaluate carbon-fiber-reinforced-polymer (CFRP) composite. An inverse methodology that combines TWRI with numerical optimization technique is proposed to determine the fiber layer-up lamination sequences of anisotropic CFRP composite. A 7-layer CFRP laminate [0°/45°/90°/0°]_{{s}} is heated by a chirp-modulated Gaussian laser beam, and then finite element method (FEM) is employed to calculate the temperature field of CFRP laminates. The phase based on lock-in correlation between reference chirp signal and the thermal-wave signal is performed to obtain the phase image of TWRI, and the least square method is applied to reconstruct the cost function that minimizes the square of the difference between the phase of TWRI inspection and numerical calculation. A hybrid algorithm that combines the simulation annealing with Nelder-Mead simplex research method is employed to solve the reconstructed cost function and find the global optimal solution of the layer-up sequences of CFRP composite. The result shows the feasibility of estimating the fiber layer-up lamination sequences of CFRP composite with optimal discrete and constraint conditions.

A parallel computing engine for a class of time critical processes.

PubMed

Nabhan, T M; Zomaya, A Y

1997-01-01

This paper focuses on the efficient parallel implementation of systems of numerically intensive nature over loosely coupled multiprocessor architectures. These analytical models are of significant importance to many real-time systems that have to meet severe time constants. A parallel computing engine (PCE) has been developed in this work for the efficient simplification and the near optimal scheduling of numerical models over the different cooperating processors of the parallel computer. First, the analytical system is efficiently coded in its general form. The model is then simplified by using any available information (e.g., constant parameters). A task graph representing the interconnections among the different components (or equations) is generated. The graph can then be compressed to control the computation/communication requirements. The task scheduler employs a graph-based iterative scheme, based on the simulated annealing algorithm, to map the vertices of the task graph onto a Multiple-Instruction-stream Multiple-Data-stream (MIMD) type of architecture. The algorithm uses a nonanalytical cost function that properly considers the computation capability of the processors, the network topology, the communication time, and congestion possibilities. Moreover, the proposed technique is simple, flexible, and computationally viable. The efficiency of the algorithm is demonstrated by two case studies with good results.

Processing-optimised imaging of analog geological models by electrical capacitance tomography

NASA Astrophysics Data System (ADS)

Ortiz Alemán, C.; Espíndola-Carmona, A.; Hernández-Gómez, J. J.; Orozco Del Castillo, MG

2017-06-01

In this work, the electrical capacitance tomography (ECT) technique is applied in monitoring internal deformation of geological analog models, which are used to study structural deformation mechanisms, in particular for simulating migration and emplacement of allochtonous salt bodies. A rectangular ECT sensor was used for internal visualization of analog geologic deformation. The monitoring of analog models consists in the reconstruction of permittivity images from the capacitance measurements obtained by introducing the model inside the ECT sensor. A simulated annealing (SA) algorithm is used as a reconstruction method, and is optimized by taking full advantage of some special features in a linearized version of this inverse approach. As a second part of this work our SA image reconstruction algorithm is applied to synthetic models, where its performance is evaluated in comparison to other commonly used algorithms such as linear back-projection and iterative Landweber methods. Finally, the SA method is applied to visualise two simple geological analog models. Encouraging results were obtained in terms of the quality of the reconstructed images, as interfaces corresponding to main geological units in the analog model were clearly distinguishable in them. We found reliable results quite useful for real time non-invasive monitoring of internal deformation of analog geological models.

An automatic iris occlusion estimation method based on high-dimensional density estimation.

PubMed

Li, Yung-Hui; Savvides, Marios

2013-04-01

Iris masks play an important role in iris recognition. They indicate which part of the iris texture map is useful and which part is occluded or contaminated by noisy image artifacts such as eyelashes, eyelids, eyeglasses frames, and specular reflections. The accuracy of the iris mask is extremely important. The performance of the iris recognition system will decrease dramatically when the iris mask is inaccurate, even when the best recognition algorithm is used. Traditionally, people used the rule-based algorithms to estimate iris masks from iris images. However, the accuracy of the iris masks generated this way is questionable. In this work, we propose to use Figueiredo and Jain's Gaussian Mixture Models (FJ-GMMs) to model the underlying probabilistic distributions of both valid and invalid regions on iris images. We also explored possible features and found that Gabor Filter Bank (GFB) provides the most discriminative information for our goal. Finally, we applied Simulated Annealing (SA) technique to optimize the parameters of GFB in order to achieve the best recognition rate. Experimental results show that the masks generated by the proposed algorithm increase the iris recognition rate on both ICE2 and UBIRIS dataset, verifying the effectiveness and importance of our proposed method for iris occlusion estimation.

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.

Multiobjective optimization of combinatorial libraries.

PubMed

Agrafiotis, D K

2002-01-01

Combinatorial chemistry and high-throughput screening have caused a fundamental shift in the way chemists contemplate experiments. Designing a combinatorial library is a controversial art that involves a heterogeneous mix of chemistry, mathematics, economics, experience, and intuition. Although there seems to be little agreement as to what constitutes an ideal library, one thing is certain: only one property or measure seldom defines the quality of the design. In most real-world applications, a good experiment requires the simultaneous optimization of several, often conflicting, design objectives, some of which may be vague and uncertain. In this paper, we discuss a class of algorithms for subset selection rooted in the principles of multiobjective optimization. Our approach is to employ an objective function that encodes all of the desired selection criteria, and then use a simulated annealing or evolutionary approach to identify the optimal (or a nearly optimal) subset from among the vast number of possibilities. Many design criteria can be accommodated, including diversity, similarity to known actives, predicted activity and/or selectivity determined by quantitative structure-activity relationship (QSAR) models or receptor binding models, enforcement of certain property distributions, reagent cost and availability, and many others. The method is robust, convergent, and extensible, offers the user full control over the relative significance of the various objectives in the final design, and permits the simultaneous selection of compounds from multiple libraries in full- or sparse-array format.

Integrating NOE and RDC using sum-of-squares relaxation for protein structure determination.

PubMed

Khoo, Y; Singer, A; Cowburn, D

2017-07-01

We revisit the problem of protein structure determination from geometrical restraints from NMR, using convex optimization. It is well-known that the NP-hard distance geometry problem of determining atomic positions from pairwise distance restraints can be relaxed into a convex semidefinite program (SDP). However, often the NOE distance restraints are too imprecise and sparse for accurate structure determination. Residual dipolar coupling (RDC) measurements provide additional geometric information on the angles between atom-pair directions and axes of the principal-axis-frame. The optimization problem involving RDC is highly non-convex and requires a good initialization even within the simulated annealing framework. In this paper, we model the protein backbone as an articulated structure composed of rigid units. Determining the rotation of each rigid unit gives the full protein structure. We propose solving the non-convex optimization problems using the sum-of-squares (SOS) hierarchy, a hierarchy of convex relaxations with increasing complexity and approximation power. Unlike classical global optimization approaches, SOS optimization returns a certificate of optimality if the global optimum is found. Based on the SOS method, we proposed two algorithms-RDC-SOS and RDC-NOE-SOS, that have polynomial time complexity in the number of amino-acid residues and run efficiently on a standard desktop. In many instances, the proposed methods exactly recover the solution to the original non-convex optimization problem. To the best of our knowledge this is the first time SOS relaxation is introduced to solve non-convex optimization problems in structural biology. We further introduce a statistical tool, the Cramér-Rao bound (CRB), to provide an information theoretic bound on the highest resolution one can hope to achieve when determining protein structure from noisy measurements using any unbiased estimator. Our simulation results show that when the RDC measurements are corrupted by Gaussian noise of realistic variance, both SOS based algorithms attain the CRB. We successfully apply our method in a divide-and-conquer fashion to determine the structure of ubiquitin from experimental NOE and RDC measurements obtained in two alignment media, achieving more accurate and faster reconstructions compared to the current state of the art.

Multiobjective hyper heuristic scheme for system design and optimization

NASA Astrophysics Data System (ADS)

Rafique, Amer Farhan

2012-11-01

As system design is becoming more and more multifaceted, integrated, and complex, the traditional single objective optimization trends of optimal design are becoming less and less efficient and effective. Single objective optimization methods present a unique optimal solution whereas multiobjective methods present pareto front. The foremost intent is to predict a reasonable distributed pareto-optimal solution set independent of the problem instance through multiobjective scheme. Other objective of application of intended approach is to improve the worthiness of outputs of the complex engineering system design process at the conceptual design phase. The process is automated in order to provide the system designer with the leverage of the possibility of studying and analyzing a large multiple of possible solutions in a short time. This article presents Multiobjective Hyper Heuristic Optimization Scheme based on low level meta-heuristics developed for the application in engineering system design. Herein, we present a stochastic function to manage meta-heuristics (low-level) to augment surety of global optimum solution. Generic Algorithm, Simulated Annealing and Swarm Intelligence are used as low-level meta-heuristics in this study. Performance of the proposed scheme is investigated through a comprehensive empirical analysis yielding acceptable results. One of the primary motives for performing multiobjective optimization is that the current engineering systems require simultaneous optimization of conflicting and multiple. Random decision making makes the implementation of this scheme attractive and easy. Injecting feasible solutions significantly alters the search direction and also adds diversity of population resulting in accomplishment of pre-defined goals set in the proposed scheme.

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.

Inferring genetic interactions via a nonlinear model and an optimization algorithm.

PubMed

Chen, Chung-Ming; Lee, Chih; Chuang, Cheng-Long; Wang, Chia-Chang; Shieh, Grace S

2010-02-26

Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target. An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT. GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.

Extended Information Ratio for Portfolio Optimization Using Simulated Annealing with Constrained Neighborhood

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.

Annealing temperature and environment effects on ZnO nanocrystals embedded in SiO2: a photoluminescence and TEM study

PubMed Central

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

Annealing temperature and environment effects on ZnO nanocrystals embedded in SiO2: a photoluminescence and TEM study.

PubMed

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.

Optimal configuration of power grid sources based on optimal particle swarm algorithm

NASA Astrophysics Data System (ADS)

Wen, Yuanhua

2018-04-01

In order to optimize the distribution problem of power grid sources, an optimized particle swarm optimization algorithm is proposed. First, the concept of multi-objective optimization and the Pareto solution set are enumerated. Then, the performance of the classical genetic algorithm, the classical particle swarm optimization algorithm and the improved particle swarm optimization algorithm are analyzed. The three algorithms are simulated respectively. Compared with the test results of each algorithm, the superiority of the algorithm in convergence and optimization performance is proved, which lays the foundation for subsequent micro-grid power optimization configuration solution.

Identifying differentially expressed genes in cancer patients using a non-parameter Ising model.

PubMed

Li, Xumeng; Feltus, Frank A; Sun, Xiaoqian; Wang, James Z; Luo, Feng

2011-10-01

Identification of genes and pathways involved in diseases and physiological conditions is a major task in systems biology. In this study, we developed a novel non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. We also proposed a simulated annealing algorithm to find the optimal configuration of the Ising model. The Ising model was applied to two breast cancer microarray data sets. The results showed that more cancer-related DE sub-networks and genes were identified by the Ising model than those by the Markov random field model. Furthermore, cross-validation experiments showed that DE genes identified by Ising model can improve classification performance compared with DE genes identified by Markov random field model. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Boltzmann machine for the organization of intelligent machines

NASA Technical Reports Server (NTRS)

Moed, Michael C.; Saridis, George N.

1990-01-01

A three-tier structure consisting of organization, coordination, and execution levels forms the architecture of an intelligent machine using the principle of increasing precision with decreasing intelligence from a hierarchically intelligent control. This system has been formulated as a probabilistic model, where uncertainty and imprecision can be expressed in terms of entropies. The optimal strategy for decision planning and task execution can be found by minimizing the total entropy in the system. The focus is on the design of the organization level as a Boltzmann machine. Since this level is responsible for planning the actions of the machine, the Boltzmann machine is reformulated to use entropy as the cost function to be minimized. Simulated annealing, expanding subinterval random search, and the genetic algorithm are presented as search techniques to efficiently find the desired action sequence and illustrated with numerical examples.

Quantum Adiabatic Optimization and Combinatorial Landscapes

NASA Technical Reports Server (NTRS)

Smelyanskiy, V. N.; Knysh, S.; Morris, R. D.

2003-01-01

In this paper we analyze the performance of the Quantum Adiabatic Evolution (QAE) algorithm on a variant of Satisfiability problem for an ensemble of random graphs parametrized by the ratio of clauses to variables, gamma = M / N. We introduce a set of macroscopic parameters (landscapes) and put forward an ansatz of universality for random bit flips. We then formulate the problem of finding the smallest eigenvalue and the excitation gap as a statistical mechanics problem. We use the so-called annealing approximation with a refinement that a finite set of macroscopic variables (verses only energy) is used, and are able to show the existence of a dynamic threshold gamma = gammad, beyond which QAE should take an exponentially long time to find a solution. We compare the results for extended and simplified sets of landscapes and provide numerical evidence in support of our universality ansatz.

Preparation and magnetic properties of multiferroic CuMnO2 nanoparticles.

PubMed

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.

Application of Taguchi approach to optimize the sol-gel process of the quaternary Cu2ZnSnS4 with good optical properties

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.

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.

TTSA: An Effective Scheduling Approach for Delay Bounded Tasks in Hybrid Clouds.

PubMed

Yuan, Haitao; Bi, Jing; Tan, Wei; Zhou, MengChu; Li, Bo Hu; Li, Jianqiang

2017-11-01

The economy of scale provided by cloud attracts a growing number of organizations and industrial companies to deploy their applications in cloud data centers (CDCs) and to provide services to users around the world. The uncertainty of arriving tasks makes it a big challenge for private CDC to cost-effectively schedule delay bounded tasks without exceeding their delay bounds. Unlike previous studies, this paper takes into account the cost minimization problem for private CDC in hybrid clouds, where the energy price of private CDC and execution price of public clouds both show the temporal diversity. Then, this paper proposes a temporal task scheduling algorithm (TTSA) to effectively dispatch all arriving tasks to private CDC and public clouds. In each iteration of TTSA, the cost minimization problem is modeled as a mixed integer linear program and solved by a hybrid simulated-annealing particle-swarm-optimization. The experimental results demonstrate that compared with the existing methods, the optimal or suboptimal scheduling strategy produced by TTSA can efficiently increase the throughput and reduce the cost of private CDC while meeting the delay bounds of all the tasks.

Visible-infrared micro-spectrometer based on a preaggregated silver nanoparticle monolayer film and an infrared sensor card

NASA Astrophysics Data System (ADS)

Yang, Tao; Peng, Jing-xiao; Ho, Ho-pui; Song, Chun-yuan; Huang, Xiao-li; Zhu, Yong-yuan; Li, Xing-ao; Huang, Wei

2018-01-01

By using a preaggregated silver nanoparticle monolayer film and an infrared sensor card, we demonstrate a miniature spectrometer design that covers a broad wavelength range from visible to infrared with high spectral resolution. The spectral contents of an incident probe beam are reconstructed by solving a matrix equation with a smoothing simulated annealing algorithm. The proposed spectrometer offers significant advantages over current instruments that are based on Fourier transform and grating dispersion, in terms of size, resolution, spectral range, cost and reliability. The spectrometer contains three components, which are used for dispersion, frequency conversion and detection. Disordered silver nanoparticles in dispersion component reduce the fabrication complexity. An infrared sensor card in the conversion component broaden the operational spectral range of the system into visible and infrared bands. Since the CCD used in the detection component provides very large number of intensity measurements, one can reconstruct the final spectrum with high resolution. An additional feature of our algorithm for solving the matrix equation, which is suitable for reconstructing both broadband and narrowband signals, we have adopted a smoothing step based on a simulated annealing algorithm. This algorithm improve the accuracy of the spectral reconstruction.

Recognition of partially occluded threat objects using the annealed Hopefield network

NASA Technical Reports Server (NTRS)

Kim, Jung H.; Yoon, Sung H.; Park, Eui H.; Ntuen, Celestine A.

1992-01-01

Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. The neural network approach is suitable for the problems in the sense that the inherent parallelism of neural networks pursues many hypotheses in parallel resulting in high computation rates. Moreover, they provide a greater degree of robustness or fault tolerance than conventional computers. The annealed Hopfield network which is derived from the mean field annealing (MFA) has been developed to find global solutions of a nonlinear system. In the study, it has been proven that the system temperature of MFA is equivalent to the gain of the sigmoid function of a Hopfield network. In our early work, we developed the hybrid Hopfield network (HHN) for fast and reliable matching. However, HHN doesn't guarantee global solutions and yields false matching under heavily occluded conditions because HHN is dependent on initial states by its nature. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybird Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a neural network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from x-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features.

The dynamical core of the Aeolus 1.0 statistical-dynamical atmosphere model: validation and parameter optimization

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.

Formation mechanism of thermally optimized Ga-doped MgZnO transparent conducting electrodes for GaN-based light-emitting diodes

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.

Effect of diffusion annealing regimes on the structure of Nb3Sn layers in ITER-type bronze-processed wires

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.

CdO thin films based on the annealing temperature differences prepared by sol-gel method and their heterojunction devices

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.

A low thermal impact annealing process for SiO2-embedded Si nanocrystals with optimized interface quality

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.

An effective PSO-based memetic algorithm for flow shop scheduling.

PubMed

Liu, Bo; Wang, Ling; Jin, Yi-Hui

2007-02-01

This paper proposes an effective particle swarm optimization (PSO)-based memetic algorithm (MA) for the permutation flow shop scheduling problem (PFSSP) with the objective to minimize the maximum completion time, which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed PSO-based MA (PSOMA), both PSO-based searching operators and some special local searching operators are designed to balance the exploration and exploitation abilities. In particular, the PSOMA applies the evolutionary searching mechanism of PSO, which is characterized by individual improvement, population cooperation, and competition to effectively perform exploration. On the other hand, the PSOMA utilizes several adaptive local searches to perform exploitation. First, to make PSO suitable for solving PFSSP, a ranked-order value rule based on random key representation is presented to convert the continuous position values of particles to job permutations. Second, to generate an initial swarm with certain quality and diversity, the famous Nawaz-Enscore-Ham (NEH) heuristic is incorporated into the initialization of population. Third, to balance the exploration and exploitation abilities, after the standard PSO-based searching operation, a new local search technique named NEH_1 insertion is probabilistically applied to some good particles selected by using a roulette wheel mechanism with a specified probability. Fourth, to enrich the searching behaviors and to avoid premature convergence, a simulated annealing (SA)-based local search with multiple different neighborhoods is designed and incorporated into the PSOMA. Meanwhile, an effective adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood to be used in SA-based local search. Finally, to further enhance the exploitation ability, a pairwise-based local search is applied after the SA-based search. Simulation results based on benchmarks demonstrate the effectiveness of the PSOMA. Additionally, the effects of some parameters on optimization performances are also discussed.

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.

Optimizer convergence and local minima errors and their clinical importance

NASA Astrophysics Data System (ADS)

Jeraj, Robert; Wu, Chuan; Mackie, Thomas R.

2003-09-01

Two of the errors common in the inverse treatment planning optimization have been investigated. The first error is the optimizer convergence error, which appears because of non-perfect convergence to the global or local solution, usually caused by a non-zero stopping criterion. The second error is the local minima error, which occurs when the objective function is not convex and/or the feasible solution space is not convex. The magnitude of the errors, their relative importance in comparison to other errors as well as their clinical significance in terms of tumour control probability (TCP) and normal tissue complication probability (NTCP) were investigated. Two inherently different optimizers, a stochastic simulated annealing and deterministic gradient method were compared on a clinical example. It was found that for typical optimization the optimizer convergence errors are rather small, especially compared to other convergence errors, e.g., convergence errors due to inaccuracy of the current dose calculation algorithms. This indicates that stopping criteria could often be relaxed leading into optimization speed-ups. The local minima errors were also found to be relatively small and typically in the range of the dose calculation convergence errors. Even for the cases where significantly higher objective function scores were obtained the local minima errors were not significantly higher. Clinical evaluation of the optimizer convergence error showed good correlation between the convergence of the clinical TCP or NTCP measures and convergence of the physical dose distribution. On the other hand, the local minima errors resulted in significantly different TCP or NTCP values (up to a factor of 2) indicating clinical importance of the local minima produced by physical optimization.

Optimizer convergence and local minima errors and their clinical importance.

PubMed

Jeraj, Robert; Wu, Chuan; Mackie, Thomas R

2003-09-07

Two of the errors common in the inverse treatment planning optimization have been investigated. The first error is the optimizer convergence error, which appears because of non-perfect convergence to the global or local solution, usually caused by a non-zero stopping criterion. The second error is the local minima error, which occurs when the objective function is not convex and/or the feasible solution space is not convex. The magnitude of the errors, their relative importance in comparison to other errors as well as their clinical significance in terms of tumour control probability (TCP) and normal tissue complication probability (NTCP) were investigated. Two inherently different optimizers, a stochastic simulated annealing and deterministic gradient method were compared on a clinical example. It was found that for typical optimization the optimizer convergence errors are rather small, especially compared to other convergence errors, e.g., convergence errors due to inaccuracy of the current dose calculation algorithms. This indicates that stopping criteria could often be relaxed leading into optimization speed-ups. The local minima errors were also found to be relatively small and typically in the range of the dose calculation convergence errors. Even for the cases where significantly higher objective function scores were obtained the local minima errors were not significantly higher. Clinical evaluation of the optimizer convergence error showed good correlation between the convergence of the clinical TCP or NTCP measures and convergence of the physical dose distribution. On the other hand, the local minima errors resulted in significantly different TCP or NTCP values (up to a factor of 2) indicating clinical importance of the local minima produced by physical optimization.

Developing an Optimum Protocol for Thermoluminescence Dosimetry with GR-200 Chips using Taguchi Method.

PubMed

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.

Effect of in situ annealing on the structural and electrical properties and infrared photodetection of III-Sb on GaAs using interfacial misfit array

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.

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.

MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.

PubMed

Mitra, Ramkrishna; Bandyopadhyay, Sanghamitra

2011-01-01

Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.

Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series

PubMed Central

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

Extrinsic and intrinsic index finger muscle attachments in an OpenSim upper-extremity model.

PubMed

Lee, Jong Hwa; Asakawa, Deanna S; Dennerlein, Jack T; Jindrich, Devin L

2015-04-01

Musculoskeletal models allow estimation of muscle function during complex tasks. We used objective methods to determine possible attachment locations for index finger muscles in an OpenSim upper-extremity model. Data-driven optimization algorithms, Simulated Annealing and Hook-Jeeves, estimated tendon locations crossing the metacarpophalangeal (MCP), proximal interphalangeal (PIP) and distal interphalangeal (DIP) joints by minimizing the difference between model-estimated and experimentally-measured moment arms. Sensitivity analysis revealed that multiple sets of muscle attachments with similar optimized moment arms are possible, requiring additional assumptions or data to select a single set of values. The most smooth muscle paths were assumed to be biologically reasonable. Estimated tendon attachments resulted in variance accounted for (VAF) between calculated moment arms and measured values of 78% for flex/extension and 81% for ab/adduction at the MCP joint. VAF averaged 67% at the PIP joint and 54% at the DIP joint. VAF values at PIP and DIP joints partially reflected the constant moment arms reported for muscles about these joints. However, all moment arm values found through optimization were non-linear and non-constant. Relationships between moment arms and joint angles were best described with quadratic equations for tendons at the PIP and DIP joints.

Optimization of the Production of Inactivated Clostridium novyi Type B Vaccine Using Computational Intelligence Techniques.

PubMed

Aquino, P L M; Fonseca, F S; Mozzer, O D; Giordano, R C; Sousa, R

2016-07-01

Clostridium novyi causes necrotic hepatitis in sheep and cattle, as well as gas gangrene. The microorganism is strictly anaerobic, fastidious, and difficult to cultivate in industrial scale. C. novyi type B produces alpha and beta toxins, with the alpha toxin being linked to the presence of specific bacteriophages. The main strategy to combat diseases caused by C. novyi is vaccination, employing vaccines produced with toxoids or with toxoids and bacterins. In order to identify culture medium components and concentrations that maximized cell density and alpha toxin production, a neuro-fuzzy algorithm was applied to predict the yields of the fermentation process for production of C. novyi type B, within a global search procedure using the simulated annealing technique. Maximizing cell density and toxin production is a multi-objective optimization problem and could be treated by a Pareto approach. Nevertheless, the approach chosen here was a step-by-step one. The optimum values obtained with this approach were validated in laboratory scale, and the results were used to reload the data matrix for re-parameterization of the neuro-fuzzy model, which was implemented for a final optimization step with regards to the alpha toxin productivity. With this methodology, a threefold increase of alpha toxin could be achieved.

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

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.

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.

Diverse task scheduling for individualized requirements in cloud manufacturing

NASA Astrophysics Data System (ADS)

Zhou, Longfei; Zhang, Lin; Zhao, Chun; Laili, Yuanjun; Xu, Lida

2018-03-01

Cloud manufacturing (CMfg) has emerged as a new manufacturing paradigm that provides ubiquitous, on-demand manufacturing services to customers through network and CMfg platforms. In CMfg system, task scheduling as an important means of finding suitable services for specific manufacturing tasks plays a key role in enhancing the system performance. Customers' requirements in CMfg are highly individualized, which leads to diverse manufacturing tasks in terms of execution flows and users' preferences. We focus on diverse manufacturing tasks and aim to address their scheduling issue in CMfg. First of all, a mathematical model of task scheduling is built based on analysis of the scheduling process in CMfg. To solve this scheduling problem, we propose a scheduling method aiming for diverse tasks, which enables each service demander to obtain desired manufacturing services. The candidate service sets are generated according to subtask directed graphs. An improved genetic algorithm is applied to searching for optimal task scheduling solutions. The effectiveness of the scheduling method proposed is verified by a case study with individualized customers' requirements. The results indicate that the proposed task scheduling method is able to achieve better performance than some usual algorithms such as simulated annealing and pattern search.

Multiphase Simulated Annealing Based on Boltzmann and Bose-Einstein Distribution Applied to Protein Folding Problem.

PubMed

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.

Shape optimization of electrostatically driven microcantilevers using simulated annealing to enhance static travel range

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.

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.

Image size invariant visual cryptography for general access structures subject to display quality constraints.

PubMed

Lee, Kai-Hui; Chiu, Pei-Ling

2013-10-01

Conventional visual cryptography (VC) suffers from a pixel-expansion problem, or an uncontrollable display quality problem for recovered images, and lacks a general approach to construct visual secret sharing schemes for general access structures. We propose a general and systematic approach to address these issues without sophisticated codebook design. This approach can be used for binary secret images in non-computer-aided decryption environments. To avoid pixel expansion, we design a set of column vectors to encrypt secret pixels rather than using the conventional VC-based approach. We begin by formulating a mathematic model for the VC construction problem to find the column vectors for the optimal VC construction, after which we develop a simulated-annealing-based algorithm to solve the problem. The experimental results show that the display quality of the recovered image is superior to that of previous papers.

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.

Thermal phase separation of ZrSiO4 thin films and frequency- dependent electrical characteristics of the Al/ZrSiO4/p-Si/Al MOS capacitors

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.

An efficient algorithm for function optimization: modified stem cells algorithm

NASA Astrophysics Data System (ADS)

Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad Hadi

2013-03-01

In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

Resolving the 180-degree ambiguity in vector magnetic field measurements: The 'minimum' energy solution

NASA Technical Reports Server (NTRS)

Metcalf, Thomas R.

1994-01-01

I present a robust algorithm that resolves the 180-deg ambiguity in measurements of the solar vector magnetic field. The technique simultaneously minimizes both the divergence of the magnetic field and the electric current density using a simulated annealing algorithm. This results in the field orientation with approximately minimum free energy. The technique is well-founded physically and is simple to implement.

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.

Soil hydraulic material properties and layered architecture from time-lapse GPR

NASA Astrophysics Data System (ADS)

Jaumann, Stefan; Roth, Kurt

2018-04-01

Quantitative knowledge of the subsurface material distribution and its effective soil hydraulic material properties is essential to predict soil water movement. Ground-penetrating radar (GPR) is a noninvasive and nondestructive geophysical measurement method that is suitable to monitor hydraulic processes. Previous studies showed that the GPR signal from a fluctuating groundwater table is sensitive to the soil water characteristic and the hydraulic conductivity function. In this work, we show that the GPR signal originating from both the subsurface architecture and the fluctuating groundwater table is suitable to estimate the position of layers within the subsurface architecture together with the associated effective soil hydraulic material properties with inversion methods. To that end, we parameterize the subsurface architecture, solve the Richards equation, convert the resulting water content to relative permittivity with the complex refractive index model (CRIM), and solve Maxwell's equations numerically. In order to analyze the GPR signal, we implemented a new heuristic algorithm that detects relevant signals in the radargram (events) and extracts the corresponding signal travel time and amplitude. This algorithm is applied to simulated as well as measured radargrams and the detected events are associated automatically. Using events instead of the full wave regularizes the inversion focussing on the relevant measurement signal. For optimization, we use a global-local approach with preconditioning. Starting from an ensemble of initial parameter sets drawn with a Latin hypercube algorithm, we sequentially couple a simulated annealing algorithm with a Levenberg-Marquardt algorithm. The method is applied to synthetic as well as measured data from the ASSESS test site. We show that the method yields reasonable estimates for the position of the layers as well as for the soil hydraulic material properties by comparing the results to references derived from ground truth data as well as from time domain reflectometry (TDR).

OptFlux: an open-source software platform for in silico metabolic engineering.

PubMed

Rocha, Isabel; Maia, Paulo; Evangelista, Pedro; Vilaça, Paulo; Soares, Simão; Pinto, José P; Nielsen, Jens; Patil, Kiran R; Ferreira, Eugénio C; Rocha, Miguel

2010-04-19

Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications. OptFlux is an open-source and modular software aimed at being the reference computational application in the field. It is the first tool to incorporate strain optimization tasks, i.e., the identification of Metabolic Engineering targets, using Evolutionary Algorithms/Simulated Annealing metaheuristics or the previously proposed OptKnock algorithm. It also allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes. OptFlux also contemplates several methods for model simplification and other pre-processing operations aimed at reducing the search space for optimization algorithms. The software supports importing/exporting to several flat file formats and it is compatible with the SBML standard. OptFlux has a visualization module that allows the analysis of the model structure that is compatible with the layout information of Cell Designer, allowing the superimposition of simulation results with the model graph. The OptFlux software is freely available, together with documentation and other resources, thus bridging the gap from research in strain optimization algorithms and the final users. It is a valuable platform for researchers in the field that have available a number of useful tools. Its open-source nature invites contributions by all those interested in making their methods available for the community. Given its plug-in based architecture it can be extended with new functionalities. Currently, several plug-ins are being developed, including network topology analysis tools and the integration with Boolean network based regulatory models.

OptFlux: an open-source software platform for in silico metabolic engineering

PubMed Central

2010-01-01

Background Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications. Results OptFlux is an open-source and modular software aimed at being the reference computational application in the field. It is the first tool to incorporate strain optimization tasks, i.e., the identification of Metabolic Engineering targets, using Evolutionary Algorithms/Simulated Annealing metaheuristics or the previously proposed OptKnock algorithm. It also allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes. OptFlux also contemplates several methods for model simplification and other pre-processing operations aimed at reducing the search space for optimization algorithms. The software supports importing/exporting to several flat file formats and it is compatible with the SBML standard. OptFlux has a visualization module that allows the analysis of the model structure that is compatible with the layout information of Cell Designer, allowing the superimposition of simulation results with the model graph. Conclusions The OptFlux software is freely available, together with documentation and other resources, thus bridging the gap from research in strain optimization algorithms and the final users. It is a valuable platform for researchers in the field that have available a number of useful tools. Its open-source nature invites contributions by all those interested in making their methods available for the community. Given its plug-in based architecture it can be extended with new functionalities. Currently, several plug-ins are being developed, including network topology analysis tools and the integration with Boolean network based regulatory models. PMID:20403172

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.

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.

Tuning of deep level emission in highly oriented electrodeposited ZnO nanorods by post growth annealing treatments

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

Photo-response behavior of organic transistors based on thermally annealed semiconducting diketopyrrolopyrrole core

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.

Using Mathematical Algorithms to Modify Glomerular Filtration Rate Estimation Equations

PubMed Central

Zhu, Bei; Wu, Jianqing; Zhu, Jin; Zhao, Weihong

2013-01-01

Background The equations provide a rapid and low-cost method of evaluating glomerular filtration rate (GFR). Previous studies indicated that the Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease-Epidemiology (CKD-EPI) and MacIsaac equations need further modification for application in Chinese population. Thus, this study was designed to modify the three equations, and compare the diagnostic accuracy of the equations modified before and after. Methodology With the use of 99 mTc-DTPA renal dynamic imaging as the reference GFR (rGFR), the MDRD, CKD-EPI and MacIsaac equations were modified by two mathematical algorithms: the hill-climbing and the simulated-annealing algorithms. Results A total of 703 Chinese subjects were recruited, with the average rGFR 77.14±25.93 ml/min. The entire modification process was based on a random sample of 80% of subjects in each GFR level as a training sample set, the rest of 20% of subjects as a validation sample set. After modification, the three equations performed significant improvement in slop, intercept, correlated coefficient, root mean square error (RMSE), total deviation index (TDI), and the proportion of estimated GFR (eGFR) within 10% and 30% deviation of rGFR (P10 and P30). Of the three modified equations, the modified CKD-EPI equation showed the best accuracy. Conclusions Mathematical algorithms could be a considerable tool to modify the GFR equations. Accuracy of all the three modified equations was significantly improved in which the modified CKD-EPI equation could be the optimal one. PMID:23472113

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

DOE PAGES

Molzahn, Daniel K.; Dorfler, Florian K.; Sandberg, Henrik; ...

2017-07-25

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. Here, this paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Molzahn, Daniel K.; Dorfler, Florian K.; Sandberg, Henrik

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. Here, this paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

Comparative evaluation of two dose optimization methods for image-guided, highly-conformal, tandem and ovoids cervix brachytherapy planning

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.

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

NASA Astrophysics Data System (ADS)

Xiao, Xiaoxu

2018-04-01

In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

An improved marriage in honey bees optimization algorithm for single objective unconstrained optimization.

PubMed

Celik, Yuksel; Ulker, Erkan

2013-01-01

Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.

Optimization of the optical and electrical properties of electron beam evaporated aluminum-doped zinc oxide films for opto-electronic applications

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.

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.

The influence of annealing temperature on the interface and photovoltaic properties of CdS/CdSe quantum dots sensitized ZnO nanorods solar cells.

PubMed

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.

Valence-band electronic structure evolution of graphene oxide upon thermal annealing for optoelectronics

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

Valence-band electronic structure evolution of graphene oxide upon thermal annealing for optoelectronics

DOE PAGES

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

SU-F-19A-03: Dosimetric Advantages in Critical Structure Dose Sparing by Using a Multichannel Cylinder in High Dose Rate Brachytherapy to Treat Vaginal Cuff Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Syh, J; Syh, J; Patel, B

2014-06-15

Purpose: The multichannel cylindrical vaginal applicator is a variation of traditional single channel cylindrical vaginal applicator. The multichannel applicator has additional peripheral channels that provide more flexibility in the planning process. The dosimetric advantage is to reduce dose to adjacent organ at risk (OAR) such as bladder and rectum while maintaining target coverage with the dose optimization from additional channels. Methods: Vaginal HDR brachytherapy plans are all CT based. CT images were acquired in 2 mm thickness to keep integrity of cylinder contouring. The CTV of 5mm Rind with prescribed treatment length was reconstructed from 5mm expansion of inserted cylinder.more » The goal was 95% of CTV covered by 95% of prescribed dose in both single channel planning (SCP)and multichannel planning (MCP) before proceeding any further optimization for dose reduction to critical structures with emphasis on D2cc and V2Gy . Results: This study demonstrated noticeable dose reduction to OAR was apparent in multichannel plans. The D2cc of the rectum and bladder were showing the reduced dose for multichannel versus single channel. The V2Gy of the rectum was 93.72% and 83.79% (p=0.007) for single channel and multichannel respectively (Figure 1 and Table 1). To assure adequate coverage to target while reducing the dose to the OAR without any compromise is the main goal in using multichannel vaginal applicator in HDR brachytherapy. Conclusion: Multichannel plans were optimized using anatomical based inverse optimization algorithm of inverse planning simulation annealing. The optimization solution of the algorithm was to improve the clinical target volume dose coverage while reducing the dose to critical organs such as bladder, rectum and bowels. The comparison between SCP and MCP demonstrated MCP is superior to SCP where the dwell positions were based on geometric array only. It concluded that MCP is preferable and is able to provide certain features superior to SCP.« less

Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

NASA Astrophysics Data System (ADS)

Anam, S.

2017-10-01

Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.