Flower pollination algorithm: A novel approach for multiobjective optimization
NASA Astrophysics Data System (ADS)
Yang, Xin-She; Karamanoglu, Mehmet; He, Xingshi
2014-09-01
Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.
Raghunathan, Shriram; Gupta, Sumeet K; Markandeya, Himanshu S; Roy, Kaushik; Irazoqui, Pedro P
2010-10-30
Implantable neural prostheses that deliver focal electrical stimulation upon demand are rapidly emerging as an alternate therapy for roughly a third of the epileptic patient population that is medically refractory. Seizure detection algorithms enable feedback mechanisms to provide focally and temporally specific intervention. Real-time feasibility and computational complexity often limit most reported detection algorithms to implementations using computers for bedside monitoring or external devices communicating with the implanted electrodes. A comparison of algorithms based on detection efficacy does not present a complete picture of the feasibility of the algorithm with limited computational power, as is the case with most battery-powered applications. We present a two-dimensional design optimization approach that takes into account both detection efficacy and hardware cost in evaluating algorithms for their feasibility in an implantable application. Detection features are first compared for their ability to detect electrographic seizures from micro-electrode data recorded from kainate-treated rats. Circuit models are then used to estimate the dynamic and leakage power consumption of the compared features. A score is assigned based on detection efficacy and the hardware cost for each of the features, then plotted on a two-dimensional design space. An optimal combination of compared features is used to construct an algorithm that provides maximal detection efficacy per unit hardware cost. The methods presented in this paper would facilitate the development of a common platform to benchmark seizure detection algorithms for comparison and feasibility analysis in the next generation of implantable neuroprosthetic devices to treat epilepsy.
A genetic algorithm approach in interface and surface structure optimization
Zhang, Jian
2010-01-01
The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the material structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.
NASA Astrophysics Data System (ADS)
Igeta, Hideki; Hasegawa, Mikio
Chaotic dynamics have been effectively applied to improve various heuristic algorithms for combinatorial optimization problems in many studies. Currently, the most used chaotic optimization scheme is to drive heuristic solution search algorithms applicable to large-scale problems by chaotic neurodynamics including the tabu effect of the tabu search. Alternatively, meta-heuristic algorithms are used for combinatorial optimization by combining a neighboring solution search algorithm, such as tabu, gradient, or other search method, with a global search algorithm, such as genetic algorithms (GA), ant colony optimization (ACO), or others. In these hybrid approaches, the ACO has effectively optimized the solution of many benchmark problems in the quadratic assignment problem library. In this paper, we propose a novel hybrid method that combines the effective chaotic search algorithm that has better performance than the tabu search and global search algorithms such as ACO and GA. Our results show that the proposed chaotic hybrid algorithm has better performance than the conventional chaotic search and conventional hybrid algorithms. In addition, we show that chaotic search algorithm combined with ACO has better performance than when combined with GA.
NASA Astrophysics Data System (ADS)
Le-Duc, Thang; Ho-Huu, Vinh; Nguyen-Thoi, Trung; Nguyen-Quoc, Hung
2016-12-01
In recent years, various types of magnetorheological brakes (MRBs) have been proposed and optimized by different optimization algorithms that are integrated in commercial software such as ANSYS and Comsol Multiphysics. However, many of these optimization algorithms often possess some noteworthy shortcomings such as the trap of solutions at local extremes, or the limited number of design variables or the difficulty of dealing with discrete design variables. Thus, to overcome these limitations and develop an efficient computation tool for optimal design of the MRBs, an optimization procedure that combines differential evolution (DE), a gradient-free global optimization method with finite element analysis (FEA) is proposed in this paper. The proposed approach is then applied to the optimal design of MRBs with different configurations including conventional MRBs and MRBs with coils placed on the side housings. Moreover, to approach a real-life design, some necessary design variables of MRBs are considered as discrete variables in the optimization process. The obtained optimal design results are compared with those of available optimal designs in the literature. The results reveal that the proposed method outperforms some traditional approaches.
Pal, Partha S; Kar, R; Mandal, D; Ghoshal, S P
2015-11-01
This paper presents an efficient approach to identify different stable and practically useful Hammerstein models as well as unstable nonlinear process along with its stable closed loop counterpart with the help of an evolutionary algorithm as Colliding Bodies Optimization (CBO) optimization algorithm. The performance measures of the CBO based optimization approach such as precision, accuracy are justified with the minimum output mean square value (MSE) which signifies that the amount of bias and variance in the output domain are also the least. It is also observed that the optimization of output MSE in the presence of outliers has resulted in a very close estimation of the output parameters consistently, which also justifies the effective general applicability of the CBO algorithm towards the system identification problem and also establishes the practical usefulness of the applied approach. Optimum values of the MSEs, computational times and statistical information of the MSEs are all found to be the superior as compared with those of the other existing similar types of stochastic algorithms based approaches reported in different recent literature, which establish the robustness and efficiency of the applied CBO based identification scheme.
Evaluation of multi-algorithm optimization approach in multi-objective rainfall-runoff calibration
NASA Astrophysics Data System (ADS)
Shafii, M.; de Smedt, F.
2009-04-01
Calibration of rainfall-runoff models is one of the issues in which hydrologists have been interested over past decades. Because of the multi-objective nature of rainfall-runoff calibration, and due to advances in computational power, population-based optimization techniques are becoming increasingly popular to be applied for multi-objective calibration schemes. Over past recent years, such methods have shown to be powerful search methods for this purpose, especially when there are a large number of calibration parameters. However, application of these methods is always criticised based on the fact that it is not possible to develop a single algorithm which is always efficient for different problems. Therefore, more recent efforts have been focused towards development of simultaneous multiple optimization algorithms to overcome this drawback. This paper involves one of the most recent population-based multi-algorithm approaches, named AMALGAM, for application to multi-objective rainfall-runoff calibration in a distributed hydrological model, WetSpa. This algorithm merges the strengths of different optimization algorithms and it, thus, has proven to be more efficient than other methods. In order to evaluate this issue, comparison between results of this paper and those previously reported using a normal multi-objective evolutionary algorithm would be the next step of this study.
Vertical and lateral flight optimization algorithm and missed approach cost calculation
NASA Astrophysics Data System (ADS)
Murrieta Mendoza, Alejandro
Flight trajectory optimization is being looked as a way of reducing flight costs, fuel burned and emissions generated by the fuel consumption. The objective of this work is to find the optimal trajectory between two points. To find the optimal trajectory, the parameters of weight, cost index, initial coordinates, and meteorological conditions along the route are provided to the algorithm. This algorithm finds the trajectory where the global cost is the most economical. The global cost is a compromise between fuel burned and flight time, this is determined using a cost index that assigns a cost in terms of fuel to the flight time. The optimization is achieved by calculating a candidate optimal cruise trajectory profile from all the combinations available in the aircraft performance database. With this cruise candidate profile, more cruises profiles are calculated taken into account the climb and descend costs. During cruise, step climbs are evaluated to optimize the trajectory. The different trajectories are compared and the most economical one is defined as the optimal vertical navigation profile. From the optimal vertical navigation profile, different lateral routes are tested. Taking advantage of the meteorological influence, the algorithm looks for the lateral navigation trajectory where the global cost is the most economical. That route is then selected as the optimal lateral navigation profile. The meteorological data was obtained from environment Canada. The new way of obtaining data from the grid from environment Canada proposed in this work resulted in an important computation time reduction compared against other methods such as bilinear interpolation. The algorithm developed here was evaluated in two different aircraft: the Lockheed L-1011 and the Sukhoi Russian regional jet. The algorithm was developed in MATLAB, and the validation was performed using Flight-Sim by Presagis and the FMS CMA-9000 by CMC Electronics -- Esterline. At the end of this work a
Gálvez, Akemi; Iglesias, Andrés
2013-01-01
Fitting spline curves to data points is a very important issue in many applied fields. It is also challenging, because these curves typically depend on many continuous variables in a highly interrelated nonlinear way. In general, it is not possible to compute these parameters analytically, so the problem is formulated as a continuous nonlinear optimization problem, for which traditional optimization techniques usually fail. This paper presents a new bioinspired method to tackle this issue. In this method, optimization is performed through a combination of two techniques. Firstly, we apply the indirect approach to the knots, in which they are not initially the subject of optimization but precomputed with a coarse approximation scheme. Secondly, a powerful bioinspired metaheuristic technique, the firefly algorithm, is applied to optimization of data parameterization; then, the knot vector is refined by using De Boor's method, thus yielding a better approximation to the optimal knot vector. This scheme converts the original nonlinear continuous optimization problem into a convex optimization problem, solved by singular value decomposition. Our method is applied to some illustrative real-world examples from the CAD/CAM field. Our experimental results show that the proposed scheme can solve the original continuous nonlinear optimization problem very efficiently. PMID:24376380
Moghri, Mehdi; Omidi, Mostafa; Farahnakian, Masoud
2014-01-01
During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite. PMID:24578636
Bogle, Lee B; Boyd, Jeff J; McLaughlin, Kyle A
2010-03-01
As winter backcountry activity increases, so does exposure to avalanche danger. A complicated situation arises when multiple victims are caught in an avalanche and where medical and other rescue demands overwhelm resources in the field. These mass casualty incidents carry a high risk of morbidity and mortality, and there is no recommended approach to patient care specific to this setting other than basic first aid principles. The literature is limited with regard to triaging systems applicable to avalanche incidents. In conjunction with the development of an electronic avalanche rescue training module by the Canadian Avalanche Association, we have designed the Avalanche Survival Optimizing Rescue Triage algorithm to address the triaging of multiple avalanche victims to optimize survival and disposition decisions.
Optimal management of substrates in anaerobic co-digestion: An ant colony algorithm approach.
Verdaguer, Marta; Molinos-Senante, María; Poch, Manel
2016-04-01
Sewage sludge (SWS) is inevitably produced in urban wastewater treatment plants (WWTPs). The treatment of SWS on site at small WWTPs is not economical; therefore, the SWS is typically transported to an alternative SWS treatment center. There is increased interest in the use of anaerobic digestion (AnD) with co-digestion as an SWS treatment alternative. Although the availability of different co-substrates has been ignored in most of the previous studies, it is an essential issue for the optimization of AnD co-digestion. In a pioneering approach, this paper applies an Ant-Colony-Optimization (ACO) algorithm that maximizes the generation of biogas through AnD co-digestion in order to optimize the discharge of organic waste from different waste sources in real-time. An empirical application is developed based on a virtual case study that involves organic waste from urban WWTPs and agrifood activities. The results illustrate the dominate role of toxicity levels in selecting contributions to the AnD input. The methodology and case study proposed in this paper demonstrate the usefulness of the ACO approach in supporting a decision process that contributes to improving the sustainability of organic waste and SWS management.
NASA Astrophysics Data System (ADS)
Subramanian, Nithya
Optimization under uncertainty accounts for design variables and external parameters or factors with probabilistic distributions instead of fixed deterministic values; it enables problem formulations that might maximize or minimize an expected value while satisfying constraints using probabilities. For discrete optimization under uncertainty, a Monte Carlo Sampling (MCS) approach enables high-accuracy estimation of expectations but it also results in high computational expense. The Genetic Algorithm (GA) with a Population-Based Sampling (PBS) technique enables optimization under uncertainty with discrete variables at a lower computational expense than using Monte Carlo sampling for every fitness evaluation. Population-Based Sampling uses fewer samples in the exploratory phase of the GA and a larger number of samples when `good designs' start emerging over the generations. This sampling technique therefore reduces the computational effort spent on `poor designs' found in the initial phase of the algorithm. Parallel computation evaluates the expected value of the objective and constraints in parallel to facilitate reduced wall-clock time. A customized stopping criterion is also developed for the GA with Population-Based Sampling. The stopping criterion requires that the design with the minimum expected fitness value to have at least 99% constraint satisfaction and to have accumulated at least 10,000 samples. The average change in expected fitness values in the last ten consecutive generations is also monitored. The optimization of composite laminates using ply orientation angle as a discrete variable provides an example to demonstrate further developments of the GA with Population-Based Sampling for discrete optimization under uncertainty. The focus problem aims to reduce the expected weight of the composite laminate while treating the laminate's fiber volume fraction and externally applied loads as uncertain quantities following normal distributions. Construction of
Balima, O.; Favennec, Y.; Rousse, D.
2013-10-15
Highlights: •New strategies to improve the accuracy of the reconstruction through mesh and finite element parameterization. •Use of gradient filtering through an alternative inner product within the adjoint method. •An integral form of the cost function is used to make the reconstruction compatible with all finite element formulations, continuous and discontinuous. •Gradient-based algorithm with the adjoint method is used for the reconstruction. -- Abstract: Optical tomography is mathematically treated as a non-linear inverse problem where the optical properties of the probed medium are recovered through the minimization of the errors between the experimental measurements and their predictions with a numerical model at the locations of the detectors. According to the ill-posed behavior of the inverse problem, some regularization tools must be performed and the Tikhonov penalization type is the most commonly used in optical tomography applications. This paper introduces an optimized approach for optical tomography reconstruction with the finite element method. An integral form of the cost function is used to take into account the surfaces of the detectors and make the reconstruction compatible with all finite element formulations, continuous and discontinuous. Through a gradient-based algorithm where the adjoint method is used to compute the gradient of the cost function, an alternative inner product is employed for preconditioning the reconstruction algorithm. Moreover, appropriate re-parameterization of the optical properties is performed. These regularization strategies are compared with the classical Tikhonov penalization one. It is shown that both the re-parameterization and the use of the Sobolev cost function gradient are efficient for solving such an ill-posed inverse problem.
A data mining approach to optimize pellets manufacturing process based on a decision tree algorithm.
Ronowicz, Joanna; Thommes, Markus; Kleinebudde, Peter; Krysiński, Jerzy
2015-06-20
The present study is focused on the thorough analysis of cause-effect relationships between pellet formulation characteristics (pellet composition as well as process parameters) and the selected quality attribute of the final product. The shape using the aspect ratio value expressed the quality of pellets. A data matrix for chemometric analysis consisted of 224 pellet formulations performed by means of eight different active pharmaceutical ingredients and several various excipients, using different extrusion/spheronization process conditions. The data set contained 14 input variables (both formulation and process variables) and one output variable (pellet aspect ratio). A tree regression algorithm consistent with the Quality by Design concept was applied to obtain deeper understanding and knowledge of formulation and process parameters affecting the final pellet sphericity. The clear interpretable set of decision rules were generated. The spehronization speed, spheronization time, number of holes and water content of extrudate have been recognized as the key factors influencing pellet aspect ratio. The most spherical pellets were achieved by using a large number of holes during extrusion, a high spheronizer speed and longer time of spheronization. The described data mining approach enhances knowledge about pelletization process and simultaneously facilitates searching for the optimal process conditions which are necessary to achieve ideal spherical pellets, resulting in good flow characteristics. This data mining approach can be taken into consideration by industrial formulation scientists to support rational decision making in the field of pellets technology.
Castellano, T.; De Palma, L.; Laneve, D.; Strippoli, V.; Cuccovilllo, A.; Prudenzano, F.; Dimiccoli, V.; Losito, O.; Prisco, R.
2015-07-01
A homemade computer code for designing a Side- Coupled Linear Accelerator (SCL) is written. It integrates a simplified model of SCL tanks with the Particle Swarm Optimization (PSO) algorithm. The computer code main aim is to obtain useful guidelines for the design of Linear Accelerator (LINAC) resonant cavities. The design procedure, assisted via the aforesaid approach seems very promising, allowing future improvements towards the optimization of actual accelerating geometries. (authors)
NASA Astrophysics Data System (ADS)
Abed, Azher M.; Abed, Issa Ahmed; Majdi, Hasan Sh.; Al-Shamani, Ali Najah; Sopian, K.
2016-12-01
This study proposes a new procedure for optimal design of shell and tube heat exchangers. The electromagnetism-like algorithm is applied to save on heat exchanger capital cost and designing a compact, high performance heat exchanger with effective use of the allowable pressure drop (cost of the pump). An optimization algorithm is then utilized to determine the optimal values of both geometric design parameters and maximum allowable pressure drop by pursuing the minimization of a total cost function. A computer code is developed for the optimal shell and tube heat exchangers. Different test cases are solved to demonstrate the effectiveness and ability of the proposed algorithm. Results are also compared with those obtained by other approaches available in the literature. The comparisons indicate that a proposed design procedure can be successfully applied in the optimal design of shell and tube heat exchangers. In particular, in the examined cases a reduction of total costs up to 30, 29, and 56.15 % compared with the original design and up to 18, 5.5 and 7.4 % compared with other approaches for case study 1, 2 and 3 respectively, are observed. In this work, economic optimization resulting from the proposed design procedure are relevant especially when the size/volume is critical for high performance and compact unit, moderate volume and cost are needed.
Ahirwal, M K; Kumar, Anil; Singh, G K
2013-01-01
This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.
A Global Approach to the Optimal Trajectory Based on an Improved Ant Colony Algorithm for Cold Spray
NASA Astrophysics Data System (ADS)
Cai, Zhenhua; Chen, Tingyang; Zeng, Chunnian; Guo, Xueping; Lian, Huijuan; Zheng, You; Wei, Xiaoxu
2016-12-01
This paper is concerned with finding a global approach to obtain the shortest complete coverage trajectory on complex surfaces for cold spray applications. A slicing algorithm is employed to decompose the free-form complex surface into several small pieces of simple topological type. The problem of finding the optimal arrangement of the pieces is translated into a generalized traveling salesman problem (GTSP). Owing to its high searching capability and convergence performance, an improved ant colony algorithm is then used to solve the GTSP. Through off-line simulation, a robot trajectory is generated based on the optimized result. The approach is applied to coat real components with a complex surface by using the cold spray system with copper as the spraying material.
NASA Astrophysics Data System (ADS)
Lai, Xide; Chen, Xiaoming; Zhang, Xiang; Lei, Mingchuan
2016-11-01
This paper presents an approach to automatic hydraulic optimization of hydraulic machine's blade system combining a blade geometric modeller and parametric generator with automatic CFD solution procedure and multi-objective genetic algorithm. In order to evaluate a plurality of design options and quickly estimate the blade system's hydraulic performance, the approximate model which is able to substitute for the original inside optimization loop has been employed in the hydraulic optimization of blade by using function approximation. As the approximate model is constructed through the database samples containing a set of blade geometries and their resulted hydraulic performances, it can ensure to correctly imitate the real blade's performances predicted by the original model. As hydraulic machine designers are accustomed to do design with 2D blade profiles on stream surface that are then stacked to 3D blade geometric model in the form of NURBS surfaces, geometric variables to be optimized were defined by a series profiles on stream surfaces. The approach depends on the cooperation between a genetic algorithm, a database and user defined objective functions and constraints which comprises hydraulic performances, structural and geometric constraint functions. Example covering optimization design of a mixed-flow pump impeller is presented.
NASA Astrophysics Data System (ADS)
Benard, N.; Pons-Prats, J.; Periaux, J.; Bugeda, G.; Braud, P.; Bonnet, J. P.; Moreau, E.
2016-02-01
The potential benefits of active flow control are no more debated. Among many others applications, flow control provides an effective mean for manipulating turbulent separated flows. Here, a nonthermal surface plasma discharge (dielectric barrier discharge) is installed at the step corner of a backward-facing step ( U 0 = 15 m/s, Re h = 30,000, Re θ = 1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step (objective function #1) and also to measure the wall pressure fluctuation coefficients (objective function #2). An autonomous multi-variable optimization by genetic algorithm is implemented in an experiment for optimizing simultaneously the voltage amplitude, the burst frequency and the duty cycle of the high-voltage signal producing the surface plasma discharge. The single-objective optimization problems concern alternatively the minimization of the objective function #1 and the maximization of the objective function #2. The present paper demonstrates that when coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm can find the optimum forcing conditions in only a few generations. At the end of the iterative search process, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing process. The objective function #2 is maximized for an actuation at half the shear layer mode. In this specific forcing mode, time-resolved PIV shows that the vortex pairing is reduced and that the strong fluctuations of the wall pressure coefficients result from the periodic passages of flow structures whose size corresponds to the height of the step model.
NASA Astrophysics Data System (ADS)
Hashemi-Dezaki, Hamed; Mohammadalizadeh-Shabestary, Masoud; Askarian-Abyaneh, Hossein; Rezaei-Jegarluei, Mohammad
2014-01-01
In electrical distribution systems, a great amount of power are wasting across the lines, also nowadays power factors, voltage profiles and total harmonic distortions (THDs) of most loads are not as would be desired. So these important parameters of a system play highly important role in wasting money and energy, and besides both consumers and sources are suffering from a high rate of distortions and even instabilities. Active power filters (APFs) are innovative ideas for solving of this adversity which have recently used instantaneous reactive power theory. In this paper, a novel method is proposed to optimize the allocation of APFs. The introduced method is based on the instantaneous reactive power theory in vectorial representation. By use of this representation, it is possible to asses different compensation strategies. Also, APFs proper placement in the system plays a crucial role in either reducing the losses costs and power quality improvement. To optimize the APFs placement, a new objective function has been defined on the basis of five terms: total losses, power factor, voltage profile, THD and cost. Genetic algorithm has been used to solve the optimization problem. The results of applying this method to a distribution network illustrate the method advantages.
NASA Astrophysics Data System (ADS)
Kumar, Vijay M.; Murthy, ANN; Chandrashekara, K.
2012-05-01
The production planning problem of flexible manufacturing system (FMS) concerns with decisions that have to be made before an FMS begins to produce parts according to a given production plan during an upcoming planning horizon. The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to the relevant machines are made. Such problems are not only combinatorial optimization problems, but also happen to be non-deterministic polynomial-time-hard, making it difficult to obtain satisfactory solutions using traditional optimization techniques. In this paper, an attempt has been made to address the machine loading problem with objectives of minimization of system unbalance and maximization of throughput simultaneously while satisfying the system constraints related to available machining time and tool slot designing and using a meta-hybrid heuristic technique based on genetic algorithm and particle swarm optimization. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as throughput and system utilization.
Algorithms for bilevel optimization
NASA Technical Reports Server (NTRS)
Alexandrov, Natalia; Dennis, J. E., Jr.
1994-01-01
General multilevel nonlinear optimization problems arise in design of complex systems and can be used as a means of regularization for multi-criteria optimization problems. Here, for clarity in displaying our ideas, we restrict ourselves to general bi-level optimization problems, and we present two solution approaches. Both approaches use a trust-region globalization strategy, and they can be easily extended to handle the general multilevel problem. We make no convexity assumptions, but we do assume that the problem has a nondegenerate feasible set. We consider necessary optimality conditions for the bi-level problem formulations and discuss results that can be extended to obtain multilevel optimization formulations with constraints at each level.
Algorithms for optimal redundancy allocation
Vandenkieboom, J.; Youngblood, R.
1993-01-01
Heuristic and exact methods for solving the redundancy allocation problem are compared to an approach based on genetic algorithms. The various methods are applied to the bridge problem, which has been used as a benchmark in earlier work on optimization methods. Comparisons are presented in terms of the best configuration found by each method, and the computation effort which was necessary in order to find it.
Zheng, Ying; Yeh, Chen-Wei; Yang, Chi-Da; Jang, Shi-Shang; Chu, I-Ming
2007-08-31
Biological information generated by high-throughput technology has made systems approach feasible for many biological problems. By this approach, optimization of metabolic pathway has been successfully applied in the amino acid production. However, in this technique, gene modifications of metabolic control architecture as well as enzyme expression levels are coupled and result in a mixed integer nonlinear programming problem. Furthermore, the stoichiometric complexity of metabolic pathway, along with strong nonlinear behaviour of the regulatory kinetic models, directs a highly rugged contour in the whole optimization problem. There may exist local optimal solutions wherein the same level of production through different flux distributions compared with global optimum. The purpose of this work is to develop a novel stochastic optimization approach-information guided genetic algorithm (IGA) to discover the local optima with different levels of modification of the regulatory loop and production rates. The novelties of this work include the information theory, local search, and clustering analysis to discover the local optima which have physical meaning among the qualified solutions.
An Optimal Class Association Rule Algorithm
NASA Astrophysics Data System (ADS)
Jean Claude, Turiho; Sheng, Yang; Chuang, Li; Kaia, Xie
Classification and association rule mining algorithms are two important aspects of data mining. Class association rule mining algorithm is a promising approach for it involves the use of association rule mining algorithm to discover classification rules. This paper introduces an optimal class association rule mining algorithm known as OCARA. It uses optimal association rule mining algorithm and the rule set is sorted by priority of rules resulting into a more accurate classifier. It outperforms the C4.5, CBA, RMR on UCI eight data sets, which is proved by experimental results.
An optimal structural design algorithm using optimality criteria
NASA Technical Reports Server (NTRS)
Taylor, J. E.; Rossow, M. P.
1976-01-01
An algorithm for optimal design is given which incorporates several of the desirable features of both mathematical programming and optimality criteria, while avoiding some of the undesirable features. The algorithm proceeds by approaching the optimal solution through the solutions of an associated set of constrained optimal design problems. The solutions of the constrained problems are recognized at each stage through the application of optimality criteria based on energy concepts. Two examples are described in which the optimal member size and layout of a truss is predicted, given the joint locations and loads.
Belwin Edward, J; Rajasekar, N; Sathiyasekar, K; Senthilnathan, N; Sarjila, R
2013-09-01
Obtaining optimal power flow solution is a strenuous task for any power system engineer. The inclusion of FACTS devices in the power system network adds to its complexity. The dual objective of OPF with fuel cost minimization along with FACTS device location for IEEE 30 bus is considered and solved using proposed Enhanced Bacterial Foraging algorithm (EBFA). The conventional Bacterial Foraging Algorithm (BFA) has the difficulty of optimal parameter selection. Hence, in this paper, BFA is enhanced by including Nelder-Mead (NM) algorithm for better performance. A MATLAB code for EBFA is developed and the problem of optimal power flow with inclusion of FACTS devices is solved. After several run with different initial values, it is found that the inclusion of FACTS devices such as SVC and TCSC in the network reduces the generation cost along with increased voltage stability limits. It is also observed that, the proposed algorithm requires lesser computational time compared to earlier proposed algorithms.
An Algorithmic Framework for Multiobjective Optimization
Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.
2013-01-01
Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795
Aerodynamic Shape Optimization using an Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
Hoist, Terry L.; Pulliam, Thomas H.
2003-01-01
A method for aerodynamic shape optimization based on an evolutionary algorithm approach is presented and demonstrated. Results are presented for a number of model problems to access the effect of algorithm parameters on convergence efficiency and reliability. A transonic viscous airfoil optimization problem-both single and two-objective variations is used as the basis for a preliminary comparison with an adjoint-gradient optimizer. The evolutionary algorithm is coupled with a transonic full potential flow solver and is used to optimize the inviscid flow about transonic wings including multi-objective and multi-discipline solutions that lead to the generation of pareto fronts. The results indicate that the evolutionary algorithm approach is easy to implement, flexible in application and extremely reliable.
Aerodynamic Shape Optimization using an Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
2003-01-01
A method for aerodynamic shape optimization based on an evolutionary algorithm approach is presented and demonstrated. Results are presented for a number of model problems to access the effect of algorithm parameters on convergence efficiency and reliability. A transonic viscous airfoil optimization problem, both single and two-objective variations, is used as the basis for a preliminary comparison with an adjoint-gradient optimizer. The evolutionary algorithm is coupled with a transonic full potential flow solver and is used to optimize the inviscid flow about transonic wings including multi-objective and multi-discipline solutions that lead to the generation of pareto fronts. The results indicate that the evolutionary algorithm approach is easy to implement, flexible in application and extremely reliable.
A Comprehensive Review of Swarm Optimization Algorithms
2015-01-01
Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. PMID:25992655
A comprehensive review of swarm optimization algorithms.
Ab Wahab, Mohd Nadhir; Nefti-Meziani, Samia; Atyabi, Adham
2015-01-01
Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.
Adaptive Cuckoo Search Algorithm for Unconstrained Optimization
2014-01-01
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases. PMID:25298971
Adaptive cuckoo search algorithm for unconstrained optimization.
Ong, Pauline
2014-01-01
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.
Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems
NASA Astrophysics Data System (ADS)
Xu, Yuechun; Cui, Zhihua; Zeng, Jianchao
Nonlinear programming problem is one important branch in operational research, and has been successfully applied to various real-life problems. In this paper, a new approach called Social emotional optimization algorithm (SEOA) is used to solve this problem which is a new swarm intelligent technique by simulating the human behavior guided by emotion. Simulation results show that the social emotional optimization algorithm proposed in this paper is effective and efficiency for the nonlinear constrained programming problems.
Rafique, Rashid; Kumar, Sandeep; Luo, Yiqi; Kiely, Gerard; Asrar, Ghassem R.
2015-02-01
he accurate calibration of complex biogeochemical models is essential for the robust estimation of soil greenhouse gases (GHG) as well as other environmental conditions and parameters that are used in research and policy decisions. DayCent is a popular biogeochemical model used both nationally and internationally for this purpose. Despite DayCent’s popularity, its complex parameter estimation is often based on experts’ knowledge which is somewhat subjective. In this study we used the inverse modelling parameter estimation software (PEST), to calibrate the DayCent model based on sensitivity and identifi- ability analysis. Using previously published N2 O and crop yield data as a basis of our calibration approach, we found that half of the 140 parameters used in this study were the primary drivers of calibration dif- ferences (i.e. the most sensitive) and the remaining parameters could not be identified given the data set and parameter ranges we used in this study. The post calibration results showed improvement over the pre-calibration parameter set based on, a decrease in residual differences 79% for N2O fluxes and 84% for crop yield, and an increase in coefficient of determination 63% for N2O fluxes and 72% for corn yield. The results of our study suggest that future studies need to better characterize germination tem- perature, number of degree-days and temperature dependency of plant growth; these processes were highly sensitive and could not be adequately constrained by the data used in our study. Furthermore, the sensitivity and identifiability analysis was helpful in providing deeper insight for important processes and associated parameters that can lead to further improvement in calibration of DayCent model.
An algorithm for online optimization of accelerators
Huang, Xiaobiao; Corbett, Jeff; Safranek, James; Wu, Juhao
2013-10-01
We developed a general algorithm for online optimization of accelerator performance, i.e., online tuning, using the performance measure as the objective function. This method, named robust conjugate direction search (RCDS), combines the conjugate direction set approach of Powell's method with a robust line optimizer which considers the random noise in bracketing the minimum and uses parabolic fit of data points that uniformly sample the bracketed zone. Moreover, it is much more robust against noise than traditional algorithms and is therefore suitable for online application. Simulation and experimental studies have been carried out to demonstrate the strength of the new algorithm.
A novel bee swarm optimization algorithm for numerical function optimization
NASA Astrophysics Data System (ADS)
Akbari, Reza; Mohammadi, Alireza; Ziarati, Koorush
2010-10-01
The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel algorithm called bee swarm optimization, or BSO, and its two extensions for improving its performance are presented. The BSO is a population based optimization technique which is inspired from foraging behavior of honey bees. The proposed approach provides different patterns which are used by the bees to adjust their flying trajectories. As the first extension, the BSO algorithm introduces different approaches such as repulsion factor and penalizing fitness (RP) to mitigate the stagnation problem. Second, to maintain efficiently the balance between exploration and exploitation, time-varying weights (TVW) are introduced into the BSO algorithm. The proposed algorithm (BSO) and its two extensions (BSO-RP and BSO-RPTVW) are compared with existing algorithms which are based on intelligent behavior of honey bees, on a set of well known numerical test functions. The experimental results show that the BSO algorithms are effective and robust; produce excellent results, and outperform other algorithms investigated in this consideration.
An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization.
Bechikh, Slim; Chaabani, Abir; Ben Said, Lamjed
2015-10-01
Recently, a new metaheuristic called chemical reaction optimization was proposed. This search algorithm, inspired by chemical reactions launched during collisions, inherits several features from other metaheuristics such as simulated annealing and particle swarm optimization. This fact has made it, nowadays, one of the most powerful search algorithms in solving mono-objective optimization problems. In this paper, we propose a multiobjective variant of chemical reaction optimization, called nondominated sorting chemical reaction optimization, in an attempt to exploit chemical reaction optimization features in tackling problems involving multiple conflicting criteria. Since our approach is based on nondominated sorting, one of the main contributions of this paper is the proposal of a new quasi-linear average time complexity quick nondominated sorting algorithm; thereby making our multiobjective algorithm efficient from a computational cost viewpoint. The experimental comparisons against several other multiobjective algorithms on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of this multiobjective version in providing a well-converged and well-diversified approximation of the Pareto front.
Multilevel algorithms for nonlinear optimization
NASA Technical Reports Server (NTRS)
Alexandrov, Natalia; Dennis, J. E., Jr.
1994-01-01
Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that naturally occur in blocks. We propose a class of multilevel optimization methods motivated by the structure and number of constraints and by the expense of the derivative computations for MDO. The algorithms are an extension to the nonlinear programming problem of the successful class of local Brown-Brent algorithms for nonlinear equations. Our extensions allow the user to partition constraints into arbitrary blocks to fit the application, and they separately process each block and the objective function, restricted to certain subspaces. The methods use trust regions as a globalization strategy, and they have been shown to be globally convergent under reasonable assumptions. The multilevel algorithms can be applied to all classes of MDO formulations. Multilevel algorithms for solving nonlinear systems of equations are a special case of the multilevel optimization methods. In this case, they can be viewed as a trust-region globalization of the Brown-Brent class.
A Cuckoo Search Algorithm for Multimodal Optimization
2014-01-01
Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration. PMID:25147850
A cuckoo search algorithm for multimodal optimization.
Cuevas, Erik; Reyna-Orta, Adolfo
2014-01-01
Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration.
NASA Astrophysics Data System (ADS)
Aiyoshi, Eitaro; Masuda, Kazuaki
On the basis of market fundamentalism, new types of social systems with the market mechanism such as electricity trading markets and carbon dioxide (CO2) emission trading markets have been developed. However, there are few textbooks in science and technology which present the explanation that Lagrange multipliers can be interpreted as market prices. This tutorial paper explains that (1) the steepest descent method for dual problems in optimization, and (2) Gauss-Seidel method for solving the stationary conditions of Lagrange problems with market principles, can formulate the mechanism of market pricing, which works even in the information-oriented modern society. The authors expect readers to acquire basic knowledge on optimization theory and algorithms related to economics and to utilize them for designing the mechanism of more complicated markets.
Moonchai, Sompop; Madlhoo, Weeranuch; Jariyachavalit, Kanidtha; Shimizu, Hiroshi; Shioya, Suteaki; Chauvatcharin, Somchai
2005-11-01
The effect of pH and temperature on cell growth and bacteriocin production in Lactococcus lactis C7 was investigated in order to optimize the production of bacteriocin. The study showed that the bacteriocin production was growth-associated, but declined after reaching the maximum titer. The decrease of bacteriocin was caused by a cell-bound protease. Maximum bacteriocin titer was obtained at pH 5.5 and at 22 degrees C. In order to obtain a global optimized solution for production of bacteriocin, the optimal temperature for bacteriocin production was further studied. Mathematical models were developed for cell growth, substrate consumption, lactic acid production and bacteriocin production. A Differential Evolution algorithm was used both to estimate the model parameters from the experimental data and to compute a temperature profile for maximizing the final bacteriocin titer and bacteriocin productivity. This simulation showed that maximum bacteriocin production was obtained at the optimal temperature profile, starting at 30 degrees C and terminating at 22 degrees C, which was validated by experiment. This temperature profile yielded 20% higher maximum bacteriocin productivity than that obtained at a constant temperature of 22 degrees C, although the total amount of bacteriocin obtained was slightly decreased.
Genetic Algorithm for Optimization: Preprocessor and Algorithm
NASA Technical Reports Server (NTRS)
Sen, S. K.; Shaykhian, Gholam A.
2006-01-01
Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.
2014-01-01
This paper aims to present an experimental investigation for optimum tribological behavior (wear depth and coefficient of friction) of electroless Ni-P-Cu coatings based on four process parameters using artificial bee colony algorithm. Experiments are carried out by utilizing the combination of three coating process parameters, namely, nickel sulphate, sodium hypophosphite, and copper sulphate, and the fourth parameter is postdeposition heat treatment temperature. The design of experiment is based on the Taguchi L27 experimental design. After coating, measurement of wear and coefficient of friction of each heat-treated sample is done using a multitribotester apparatus with block-on-roller arrangement. Both friction and wear are found to increase with increase of source of nickel concentration and decrease with increase of source of copper concentration. Artificial bee colony algorithm is successfully employed to optimize the multiresponse objective function for both wear depth and coefficient of friction. It is found that, within the operating range, a lower value of nickel concentration, medium value of hypophosphite concentration, higher value of copper concentration, and higher value of heat treatment temperature are suitable for having minimum wear and coefficient of friction. The surface morphology, phase transformation behavior, and composition of coatings are also studied with the help of scanning electron microscopy, X-ray diffraction analysis, and energy dispersed X-ray analysis, respectively. PMID:27382630
Roy, Supriyo; Sahoo, Prasanta
2014-01-01
This paper aims to present an experimental investigation for optimum tribological behavior (wear depth and coefficient of friction) of electroless Ni-P-Cu coatings based on four process parameters using artificial bee colony algorithm. Experiments are carried out by utilizing the combination of three coating process parameters, namely, nickel sulphate, sodium hypophosphite, and copper sulphate, and the fourth parameter is postdeposition heat treatment temperature. The design of experiment is based on the Taguchi L27 experimental design. After coating, measurement of wear and coefficient of friction of each heat-treated sample is done using a multitribotester apparatus with block-on-roller arrangement. Both friction and wear are found to increase with increase of source of nickel concentration and decrease with increase of source of copper concentration. Artificial bee colony algorithm is successfully employed to optimize the multiresponse objective function for both wear depth and coefficient of friction. It is found that, within the operating range, a lower value of nickel concentration, medium value of hypophosphite concentration, higher value of copper concentration, and higher value of heat treatment temperature are suitable for having minimum wear and coefficient of friction. The surface morphology, phase transformation behavior, and composition of coatings are also studied with the help of scanning electron microscopy, X-ray diffraction analysis, and energy dispersed X-ray analysis, respectively.
Raab, David; Graf, Marcus; Notka, Frank; Schödl, Thomas
2010-01-01
One of the main advantages of de novo gene synthesis is the fact that it frees the researcher from any limitations imposed by the use of natural templates. To make the most out of this opportunity, efficient algorithms are needed to calculate a coding sequence, combining different requirements, such as adapted codon usage or avoidance of restriction sites, in the best possible way. We present an algorithm where a “variation window” covering several amino acid positions slides along the coding sequence. Candidate sequences are built comprising the already optimized part of the complete sequence and all possible combinations of synonymous codons representing the amino acids within the window. The candidate sequences are assessed with a quality function, and the first codon of the best candidates’ variation window is fixed. Subsequently the window is shifted by one codon position. As an example of a freely accessible software implementing the algorithm, we present the Mr. Gene web-application. Additionally two experimental applications of the algorithm are shown. PMID:21189842
Combinatorial Multiobjective Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Crossley, William A.; Martin. Eric T.
2002-01-01
The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.
A reliable algorithm for optimal control synthesis
NASA Technical Reports Server (NTRS)
Vansteenwyk, Brett; Ly, Uy-Loi
1992-01-01
In recent years, powerful design tools for linear time-invariant multivariable control systems have been developed based on direct parameter optimization. In this report, an algorithm for reliable optimal control synthesis using parameter optimization is presented. Specifically, a robust numerical algorithm is developed for the evaluation of the H(sup 2)-like cost functional and its gradients with respect to the controller design parameters. The method is specifically designed to handle defective degenerate systems and is based on the well-known Pade series approximation of the matrix exponential. Numerical test problems in control synthesis for simple mechanical systems and for a flexible structure with densely packed modes illustrate positively the reliability of this method when compared to a method based on diagonalization. Several types of cost functions have been considered: a cost function for robust control consisting of a linear combination of quadratic objectives for deterministic and random disturbances, and one representing an upper bound on the quadratic objective for worst case initial conditions. Finally, a framework for multivariable control synthesis has been developed combining the concept of closed-loop transfer recovery with numerical parameter optimization. The procedure enables designers to synthesize not only observer-based controllers but also controllers of arbitrary order and structure. Numerical design solutions rely heavily on the robust algorithm due to the high order of the synthesis model and the presence of near-overlapping modes. The design approach is successfully applied to the design of a high-bandwidth control system for a rotorcraft.
Wind farm optimization using evolutionary algorithms
NASA Astrophysics Data System (ADS)
Ituarte-Villarreal, Carlos M.
In recent years, the wind power industry has focused its efforts on solving the Wind Farm Layout Optimization (WFLO) problem. Wind resource assessment is a pivotal step in optimizing the wind-farm design and siting and, in determining whether a project is economically feasible or not. In the present work, three (3) different optimization methods are proposed for the solution of the WFLO: (i) A modified Viral System Algorithm applied to the optimization of the proper location of the components in a wind-farm to maximize the energy output given a stated wind environment of the site. The optimization problem is formulated as the minimization of energy cost per unit produced and applies a penalization for the lack of system reliability. The viral system algorithm utilized in this research solves three (3) well-known problems in the wind-energy literature; (ii) a new multiple objective evolutionary algorithm to obtain optimal placement of wind turbines while considering the power output, cost, and reliability of the system. The algorithm presented is based on evolutionary computation and the objective functions considered are the maximization of power output, the minimization of wind farm cost and the maximization of system reliability. The final solution to this multiple objective problem is presented as a set of Pareto solutions and, (iii) A hybrid viral-based optimization algorithm adapted to find the proper component configuration for a wind farm with the introduction of the universal generating function (UGF) analytical approach to discretize the different operating or mechanical levels of the wind turbines in addition to the various wind speed states. The proposed methodology considers the specific probability functions of the wind resource to describe their proper behaviors to account for the stochastic comportment of the renewable energy components, aiming to increase their power output and the reliability of these systems. The developed heuristic considers a
Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.
2004-01-01
A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.
Optimal Pid Controller Design Using Adaptive Vurpso Algorithm
NASA Astrophysics Data System (ADS)
Zirkohi, Majid Moradi
2015-04-01
The purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.
Transonic Wing Shape Optimization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
2002-01-01
A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.
Intervals in evolutionary algorithms for global optimization
Patil, R.B.
1995-05-01
Optimization is of central concern to a number of disciplines. Interval Arithmetic methods for global optimization provide us with (guaranteed) verified results. These methods are mainly restricted to the classes of objective functions that are twice differentiable and use a simple strategy of eliminating a splitting larger regions of search space in the global optimization process. An efficient approach that combines the efficient strategy from Interval Global Optimization Methods and robustness of the Evolutionary Algorithms is proposed. In the proposed approach, search begins with randomly created interval vectors with interval widths equal to the whole domain. Before the beginning of the evolutionary process, fitness of these interval parameter vectors is defined by evaluating the objective function at the center of the initial interval vectors. In the subsequent evolutionary process the local optimization process returns an estimate of the bounds of the objective function over the interval vectors. Though these bounds may not be correct at the beginning due to large interval widths and complicated function properties, the process of reducing interval widths over time and a selection approach similar to simulated annealing helps in estimating reasonably correct bounds as the population evolves. The interval parameter vectors at these estimated bounds (local optima) are then subjected to crossover and mutation operators. This evolutionary process continues for predetermined number of generations in the search of the global optimum.
Optimal Multistage Algorithm for Adjoint Computation
Aupy, Guillaume; Herrmann, Julien; Hovland, Paul; Robert, Yves
2016-01-01
We reexamine the work of Stumm and Walther on multistage algorithms for adjoint computation. We provide an optimal algorithm for this problem when there are two levels of checkpoints, in memory and on disk. Previously, optimal algorithms for adjoint computations were known only for a single level of checkpoints with no writing and reading costs; a well-known example is the binomial checkpointing algorithm of Griewank and Walther. Stumm and Walther extended that binomial checkpointing algorithm to the case of two levels of checkpoints, but they did not provide any optimality results. We bridge the gap by designing the first optimal algorithm in this context. We experimentally compare our optimal algorithm with that of Stumm and Walther to assess the difference in performance.
Optical flow optimization using parallel genetic algorithm
NASA Astrophysics Data System (ADS)
Zavala-Romero, Olmo; Botella, Guillermo; Meyer-Bäse, Anke; Meyer Base, Uwe
2011-06-01
A new approach to optimize the parameters of a gradient-based optical flow model using a parallel genetic algorithm (GA) is proposed. The main characteristics of the optical flow algorithm are its bio-inspiration and robustness against contrast, static patterns and noise, besides working consistently with several optical illusions where other algorithms fail. This model depends on many parameters which conform the number of channels, the orientations required, the length and shape of the kernel functions used in the convolution stage, among many more. The GA is used to find a set of parameters which improve the accuracy of the optical flow on inputs where the ground-truth data is available. This set of parameters helps to understand which of them are better suited for each type of inputs and can be used to estimate the parameters of the optical flow algorithm when used with videos that share similar characteristics. The proposed implementation takes into account the embarrassingly parallel nature of the GA and uses the OpenMP Application Programming Interface (API) to speedup the process of estimating an optimal set of parameters. The information obtained in this work can be used to dynamically reconfigure systems, with potential applications in robotics, medical imaging and tracking.
Abstract models for the synthesis of optimization algorithms.
NASA Technical Reports Server (NTRS)
Meyer, G. G. L.; Polak, E.
1971-01-01
Systematic approach to the problem of synthesis of optimization algorithms. Abstract models for algorithms are developed which guide the inventive process toward ?conceptual' algorithms which may consist of operations that are inadmissible in a practical method. Once the abstract models are established a set of methods for converting ?conceptual' algorithms falling into the class defined by the abstract models into ?implementable' iterative procedures is presented.
Bell-Curve Based Evolutionary Optimization Algorithm
NASA Technical Reports Server (NTRS)
Sobieszczanski-Sobieski, J.; Laba, K.; Kincaid, R.
1998-01-01
The paper presents an optimization algorithm that falls in the category of genetic, or evolutionary algorithms. While the bit exchange is the basis of most of the Genetic Algorithms (GA) in research and applications in America, some alternatives, also in the category of evolutionary algorithms, but use a direct, geometrical approach have gained popularity in Europe and Asia. The Bell-Curve Based Evolutionary Algorithm (BCB) is in this alternative category and is distinguished by the use of a combination of n-dimensional geometry and the normal distribution, the bell-curve, in the generation of the offspring. The tool for creating a child is a geometrical construct comprising a line connecting two parents and a weighted point on that line. The point that defines the child deviates from the weighted point in two directions: parallel and orthogonal to the connecting line, the deviation in each direction obeying a probabilistic distribution. Tests showed satisfactory performance of BCB. The principal advantage of BCB is its controllability via the normal distribution parameters and the geometrical construct variables.
Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.
2005-01-01
A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.
Parallel algorithms for unconstrained optimizations by multisplitting
He, Qing
1994-12-31
In this paper a new parallel iterative algorithm for unconstrained optimization using the idea of multisplitting is proposed. This algorithm uses the existing sequential algorithms without any parallelization. Some convergence and numerical results for this algorithm are presented. The experiments are performed on an Intel iPSC/860 Hyper Cube with 64 nodes. It is interesting that the sequential implementation on one node shows that if the problem is split properly, the algorithm converges much faster than one without splitting.
Multidisciplinary design optimization using genetic algorithms
NASA Technical Reports Server (NTRS)
Unal, Resit
1994-01-01
Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared
Multiobjective Optimization Using a Pareto Differential Evolution Approach
NASA Technical Reports Server (NTRS)
Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)
2002-01-01
Differential Evolution is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. In this paper, the Differential Evolution algorithm is extended to multiobjective optimization problems by using a Pareto-based approach. The algorithm performs well when applied to several test optimization problems from the literature.
Unification of algorithms for minimum mode optimization
NASA Astrophysics Data System (ADS)
Zeng, Yi; Xiao, Penghao; Henkelman, Graeme
2014-01-01
Minimum mode following algorithms are widely used for saddle point searching in chemical and material systems. Common to these algorithms is a component to find the minimum curvature mode of the second derivative, or Hessian matrix. Several methods, including Lanczos, dimer, Rayleigh-Ritz minimization, shifted power iteration, and locally optimal block preconditioned conjugate gradient, have been proposed for this purpose. Each of these methods finds the lowest curvature mode iteratively without calculating the Hessian matrix, since the full matrix calculation is prohibitively expensive in the high dimensional spaces of interest. Here we unify these iterative methods in the same theoretical framework using the concept of the Krylov subspace. The Lanczos method finds the lowest eigenvalue in a Krylov subspace of increasing size, while the other methods search in a smaller subspace spanned by the set of previous search directions. We show that these smaller subspaces are contained within the Krylov space for which the Lanczos method explicitly finds the lowest curvature mode, and hence the theoretical efficiency of the minimum mode finding methods are bounded by the Lanczos method. Numerical tests demonstrate that the dimer method combined with second-order optimizers approaches but does not exceed the efficiency of the Lanczos method for minimum mode optimization.
Unification of algorithms for minimum mode optimization.
Zeng, Yi; Xiao, Penghao; Henkelman, Graeme
2014-01-28
Minimum mode following algorithms are widely used for saddle point searching in chemical and material systems. Common to these algorithms is a component to find the minimum curvature mode of the second derivative, or Hessian matrix. Several methods, including Lanczos, dimer, Rayleigh-Ritz minimization, shifted power iteration, and locally optimal block preconditioned conjugate gradient, have been proposed for this purpose. Each of these methods finds the lowest curvature mode iteratively without calculating the Hessian matrix, since the full matrix calculation is prohibitively expensive in the high dimensional spaces of interest. Here we unify these iterative methods in the same theoretical framework using the concept of the Krylov subspace. The Lanczos method finds the lowest eigenvalue in a Krylov subspace of increasing size, while the other methods search in a smaller subspace spanned by the set of previous search directions. We show that these smaller subspaces are contained within the Krylov space for which the Lanczos method explicitly finds the lowest curvature mode, and hence the theoretical efficiency of the minimum mode finding methods are bounded by the Lanczos method. Numerical tests demonstrate that the dimer method combined with second-order optimizers approaches but does not exceed the efficiency of the Lanczos method for minimum mode optimization.
Intelligent perturbation algorithms to space scheduling optimization
NASA Technical Reports Server (NTRS)
Kurtzman, Clifford R.
1991-01-01
The limited availability and high cost of crew time and scarce resources make optimization of space operations critical. Advances in computer technology coupled with new iterative search techniques permit the near optimization of complex scheduling problems that were previously considered computationally intractable. Described here is a class of search techniques called Intelligent Perturbation Algorithms. Several scheduling systems which use these algorithms to optimize the scheduling of space crew, payload, and resource operations are also discussed.
Superscattering of light optimized by a genetic algorithm
Mirzaei, Ali Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S.
2014-07-07
We analyse scattering of light from multi-layer plasmonic nanowires and employ a genetic algorithm for optimizing the scattering cross section. We apply the mode-expansion method using experimental data for material parameters to demonstrate that our genetic algorithm allows designing realistic core-shell nanostructures with the superscattering effect achieved at any desired wavelength. This approach can be employed for optimizing both superscattering and cloaking at different wavelengths in the visible spectral range.
Smell Detection Agent Based Optimization Algorithm
NASA Astrophysics Data System (ADS)
Vinod Chandra, S. S.
2016-09-01
In this paper, a novel nature-inspired optimization algorithm has been employed and the trained behaviour of dogs in detecting smell trails is adapted into computational agents for problem solving. The algorithm involves creation of a surface with smell trails and subsequent iteration of the agents in resolving a path. This algorithm can be applied in different computational constraints that incorporate path-based problems. Implementation of the algorithm can be treated as a shortest path problem for a variety of datasets. The simulated agents have been used to evolve the shortest path between two nodes in a graph. This algorithm is useful to solve NP-hard problems that are related to path discovery. This algorithm is also useful to solve many practical optimization problems. The extensive derivation of the algorithm can be enabled to solve shortest path problems.
OPC recipe optimization using genetic algorithm
NASA Astrophysics Data System (ADS)
Asthana, Abhishek; Wilkinson, Bill; Power, Dave
2016-03-01
Optimization of OPC recipes is not trivial due to multiple parameters that need tuning and their correlation. Usually, no standard methodologies exist for choosing the initial recipe settings, and in the keyword development phase, parameters are chosen either based on previous learning, vendor recommendations, or to resolve specific problems on particular special constructs. Such approaches fail to holistically quantify the effects of parameters on other or possible new designs, and to an extent are based on the keyword developer's intuition. In addition, when a quick fix is needed for a new design, numerous customization statements are added to the recipe, which make it more complex. The present work demonstrates the application of Genetic Algorithm (GA) technique for optimizing OPC recipes. GA is a search technique that mimics Darwinian natural selection and has applications in various science and engineering disciplines. In this case, GA search heuristic is applied to two problems: (a) an overall OPC recipe optimization with respect to selected parameters and, (b) application of GA to improve printing and via coverage at line end geometries. As will be demonstrated, the optimized recipe significantly reduced the number of ORC violations for case (a). For case (b) line end for various features showed significant printing and filling improvement.
Genetic Algorithm Approaches for Actuator Placement
NASA Technical Reports Server (NTRS)
Crossley, William A.
2000-01-01
This research investigated genetic algorithm approaches for smart actuator placement to provide aircraft maneuverability without requiring hinged flaps or other control surfaces. The effort supported goals of the Multidisciplinary Design Optimization focus efforts in NASA's Aircraft au program. This work helped to properly identify various aspects of the genetic algorithm operators and parameters that allow for placement of discrete control actuators/effectors. An improved problem definition, including better definition of the objective function and constraints, resulted from this research effort. The work conducted for this research used a geometrically simple wing model; however, an increasing number of potential actuator placement locations were incorporated to illustrate the ability of the GA to determine promising actuator placement arrangements. This effort's major result is a useful genetic algorithm-based approach to assist in the discrete actuator/effector placement problem.
Instrument design and optimization using genetic algorithms
Hoelzel, Robert; Bentley, Phillip M.; Fouquet, Peter
2006-10-15
This article describes the design of highly complex physical instruments by using a canonical genetic algorithm (GA). The procedure can be applied to all instrument designs where performance goals can be quantified. It is particularly suited to the optimization of instrument design where local optima in the performance figure of merit are prevalent. Here, a GA is used to evolve the design of the neutron spin-echo spectrometer WASP which is presently being constructed at the Institut Laue-Langevin, Grenoble, France. A comparison is made between this artificial intelligence approach and the traditional manual design methods. We demonstrate that the search of parameter space is more efficient when applying the genetic algorithm, and the GA produces a significantly better instrument design. Furthermore, it is found that the GA increases flexibility, by facilitating the reoptimization of the design after changes in boundary conditions during the design phase. The GA also allows the exploration of 'nonstandard' magnet coil geometries. We conclude that this technique constitutes a powerful complementary tool for the design and optimization of complex scientific apparatus, without replacing the careful thought processes employed in traditional design methods.
Spaceborne SAR Imaging Algorithm for Coherence Optimized
Qiu, Zhiwei; Yue, Jianping; Wang, Xueqin; Yue, Shun
2016-01-01
This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR) by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR) research and application. PMID:26871446
Angelic Hierarchical Planning: Optimal and Online Algorithms
2008-12-06
describe an alternative “satisficing” algorithm, AHSS . 4.1 Abstract Lookahead Trees Our ALT data structures support our search algorithms by efficiently...Angelic Hierarchical Satisficing Search ( AHSS ), which at- tempts to find a plan that reaches the goal with at most some pre-specified cost α. AHSS can be...much more efficient than AHA*, since it can commit to a plan without first proving its optimality. At each step, AHSS (see Algorithm 3) begins by
Belief Propagation Algorithm for Portfolio Optimization Problems.
Shinzato, Takashi; Yasuda, Muneki
2015-01-01
The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm.
PDE Nozzle Optimization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Billings, Dana; Turner, James E. (Technical Monitor)
2000-01-01
Genetic algorithms, which simulate evolution in natural systems, have been used to find solutions to optimization problems that seem intractable to standard approaches. In this study, the feasibility of using a GA to find an optimum, fixed profile nozzle for a pulse detonation engine (PDE) is demonstrated. The objective was to maximize impulse during the detonation wave passage and blow-down phases of operation. Impulse of each profile variant was obtained by using the CFD code Mozart/2.0 to simulate the transient flow. After 7 generations, the method has identified a nozzle profile that certainly is a candidate for optimum solution. The constraints on the generality of this possible solution remain to be clarified.
Optimizing connected component labeling algorithms
NASA Astrophysics Data System (ADS)
Wu, Kesheng; Otoo, Ekow; Shoshani, Arie
2005-04-01
This paper presents two new strategies that can be used to greatly improve the speed of connected component labeling algorithms. To assign a label to a new object, most connected component labeling algorithms use a scanning step that examines some of its neighbors. The first strategy exploits the dependencies among them to reduce the number of neighbors examined. When considering 8-connected components in a 2D image, this can reduce the number of neighbors examined from four to one in many cases. The second strategy uses an array to store the equivalence information among the labels. This replaces the pointer based rooted trees used to store the same equivalence information. It reduces the memory required and also produces consecutive final labels. Using an array instead of the pointer based rooted trees speeds up the connected component labeling algorithms by a factor of 5 ~ 100 in our tests on random binary images.
Applying new optimization algorithms to more predictive control
Wright, S.J.
1996-03-01
The connections between optimization and control theory have been explored by many researchers and optimization algorithms have been applied with success to optimal control. The rapid pace of developments in model predictive control has given rise to a host of new problems to which optimization has yet to be applied. Concurrently, developments in optimization, and especially in interior-point methods, have produced a new set of algorithms that may be especially helpful in this context. In this paper, we reexamine the relatively simple problem of control of linear processes subject to quadratic objectives and general linear constraints. We show how new algorithms for quadratic programming can be applied efficiently to this problem. The approach extends to several more general problems in straightforward ways.
Algorithms for optimal dyadic decision trees
Hush, Don; Porter, Reid
2009-01-01
A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, revising the core tree-building algorithm so that its run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.
Air data system optimization using a genetic algorithm
NASA Technical Reports Server (NTRS)
Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III
1992-01-01
An optimization method for flush-orifice air data system design has been developed using the Genetic Algorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the Genetic Algorithm method are compared to the results obtained by conventional gradient search method.
Multidisciplinary Optimization of Airborne Radome Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Tang, Xinggang; Zhang, Weihong; Zhu, Jihong
A multidisciplinary optimization scheme of airborne radome is proposed. The optimization procedure takes into account the structural and the electromagnetic responses simultaneously. The structural analysis is performed with the finite element method using Patran/Nastran, while the electromagnetic analysis is carried out using the Plane Wave Spectrum and Surface Integration technique. The genetic algorithm is employed for the multidisciplinary optimization process. The thicknesses of multilayer radome wall are optimized to maximize the overall transmission coefficient of the antenna-radome system under the constraint of the structural failure criteria. The proposed scheme and the optimization approach are successfully assessed with an illustrative numerical example.
Optimal Hops-Based Adaptive Clustering Algorithm
NASA Astrophysics Data System (ADS)
Xuan, Xin; Chen, Jian; Zhen, Shanshan; Kuo, Yonghong
This paper proposes an optimal hops-based adaptive clustering algorithm (OHACA). The algorithm sets an energy selection threshold before the cluster forms so that the nodes with less energy are more likely to go to sleep immediately. In setup phase, OHACA introduces an adaptive mechanism to adjust cluster head and load balance. And the optimal distance theory is applied to discover the practical optimal routing path to minimize the total energy for transmission. Simulation results show that OHACA prolongs the life of network, improves utilizing rate and transmits more data because of energy balance.
An algorithm for LQ optimal actuator location
NASA Astrophysics Data System (ADS)
Darivandi, Neda; Morris, Kirsten; Khajepour, Amir
2013-03-01
The locations of the control hardware are typically a design variable in controller design for distributed parameter systems. In order to obtain the most efficient control system, the locations of control hardware as well as the feedback gain should be optimized. These optimization problems are generally non-convex. In addition, the models for these systems typically have a large number of degrees of freedom. Consequently, existing optimization schemes for optimal actuator placement may be inaccurate or computationally impractical. In this paper, the feedback control is chosen to be an optimal linear quadratic regulator. The optimal actuator location problem is reformulated as a convex optimization problem. A subgradient-based optimization scheme which leads to the global solution of the problem is used to optimize actuator locations. The optimization algorithm is applied to optimize the placement of piezoelectric actuators in vibration control of flexible structures. This method is compared with a genetic algorithm, and is observed to be faster and more accurate. Experiments are performed to verify the efficacy of optimal actuator placement.
Optimizing doped libraries by using genetic algorithms
NASA Astrophysics Data System (ADS)
Tomandl, Dirk; Schober, Andreas; Schwienhorst, Andreas
1997-01-01
The insertion of random sequences into protein-encoding genes in combination with biologicalselection techniques has become a valuable tool in the design of molecules that have usefuland possibly novel properties. By employing highly effective screening protocols, a functionaland unique structure that had not been anticipated can be distinguished among a hugecollection of inactive molecules that together represent all possible amino acid combinations.This technique is severely limited by its restriction to a library of manageable size. Oneapproach for limiting the size of a mutant library relies on `doping schemes', where subsetsof amino acids are generated that reveal only certain combinations of amino acids in a proteinsequence. Three mononucleotide mixtures for each codon concerned must be designed, suchthat the resulting codons that are assembled during chemical gene synthesis represent thedesired amino acid mixture on the level of the translated protein. In this paper we present adoping algorithm that `reverse translates' a desired mixture of certain amino acids into threemixtures of mononucleotides. The algorithm is designed to optimally bias these mixturestowards the codons of choice. This approach combines a genetic algorithm with localoptimization strategies based on the downhill simplex method. Disparate relativerepresentations of all amino acids (and stop codons) within a target set can be generated.Optional weighing factors are employed to emphasize the frequencies of certain amino acidsand their codon usage, and to compensate for reaction rates of different mononucleotidebuilding blocks (synthons) during chemical DNA synthesis. The effect of statistical errors thataccompany an experimental realization of calculated nucleotide mixtures on the generatedmixtures of amino acids is simulated. These simulations show that the robustness of differentoptima with respect to small deviations from calculated values depends on their concomitantfitness. Furthermore
Comparison of evolutionary algorithms for LPDA antenna optimization
NASA Astrophysics Data System (ADS)
Lazaridis, Pavlos I.; Tziris, Emmanouil N.; Zaharis, Zaharias D.; Xenos, Thomas D.; Cosmas, John P.; Gallion, Philippe B.; Holmes, Violeta; Glover, Ian A.
2016-08-01
A novel approach to broadband log-periodic antenna design is presented, where some of the most powerful evolutionary algorithms are applied and compared for the optimal design of wire log-periodic dipole arrays (LPDA) using Numerical Electromagnetics Code. The target is to achieve an optimal antenna design with respect to maximum gain, gain flatness, front-to-rear ratio (F/R) and standing wave ratio. The parameters of the LPDA optimized are the dipole lengths, the spacing between the dipoles, and the dipole wire diameters. The evolutionary algorithms compared are the Differential Evolution (DE), Particle Swarm (PSO), Taguchi, Invasive Weed (IWO), and Adaptive Invasive Weed Optimization (ADIWO). Superior performance is achieved by the IWO (best results) and PSO (fast convergence) algorithms.
Protein structure optimization with a "Lamarckian" ant colony algorithm.
Oakley, Mark T; Richardson, E Grace; Carr, Harriet; Johnston, Roy L
2013-01-01
We describe the LamarckiAnt algorithm: a search algorithm that combines the features of a "Lamarckian" genetic algorithm and ant colony optimization. We have implemented this algorithm for the optimization of BLN model proteins, which have frustrated energy landscapes and represent a challenge for global optimization algorithms. We demonstrate that LamarckiAnt performs competitively with other state-of-the-art optimization algorithms.
Algorithm Optimally Allocates Actuation of a Spacecraft
NASA Technical Reports Server (NTRS)
Motaghedi, Shi
2007-01-01
A report presents an algorithm that solves the following problem: Allocate the force and/or torque to be exerted by each thruster and reaction-wheel assembly on a spacecraft for best performance, defined as minimizing the error between (1) the total force and torque commanded by the spacecraft control system and (2) the total of forces and torques actually exerted by all the thrusters and reaction wheels. The algorithm incorporates the matrix vector relationship between (1) the total applied force and torque and (2) the individual actuator force and torque values. It takes account of such constraints as lower and upper limits on the force or torque that can be applied by a given actuator. The algorithm divides the aforementioned problem into two optimization problems that it solves sequentially. These problems are of a type, known in the art as semi-definite programming problems, that involve linear matrix inequalities. The algorithm incorporates, as sub-algorithms, prior algorithms that solve such optimization problems very efficiently. The algorithm affords the additional advantage that the solution requires the minimum rate of consumption of fuel for the given best performance.
An efficient cuckoo search algorithm for numerical function optimization
NASA Astrophysics Data System (ADS)
Ong, Pauline; Zainuddin, Zarita
2013-04-01
Cuckoo search algorithm which reproduces the breeding strategy of the best known brood parasitic bird, the cuckoos has demonstrated its superiority in obtaining the global solution for numerical optimization problems. However, the involvement of fixed step approach in its exploration and exploitation behavior might slow down the search process considerably. In this regards, an improved cuckoo search algorithm with adaptive step size adjustment is introduced and its feasibility on a variety of benchmarks is validated. The obtained results show that the proposed scheme outperforms the standard cuckoo search algorithm in terms of convergence characteristic while preserving the fascinating features of the original method.
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.
A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations
NASA Technical Reports Server (NTRS)
Venter, Gerhard; Sobieszczanski-Sobieski, Jaroslaw
2005-01-01
A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.
Optimized Algorithms for Prediction within Robotic Tele-Operative Interfaces
NASA Technical Reports Server (NTRS)
Martin, Rodney A.; Wheeler, Kevin R.; SunSpiral, Vytas; Allan, Mark B.
2006-01-01
Robonaut, the humanoid robot developed at the Dexterous Robotics Laboratory at NASA Johnson Space Center serves as a testbed for human-robot collaboration research and development efforts. One of the primary efforts investigates how adjustable autonomy can provide for a safe and more effective completion of manipulation-based tasks. A predictive algorithm developed in previous work was deployed as part of a software interface that can be used for long-distance tele-operation. In this paper we provide the details of this algorithm, how to improve upon the methods via optimization, and also present viable alternatives to the original algorithmic approach. We show that all of the algorithms presented can be optimized to meet the specifications of the metrics shown as being useful for measuring the performance of the predictive methods. Judicious feature selection also plays a significant role in the conclusions drawn.
Two hybrid compaction algorithms for the layout optimization problem.
Xiao, Ren-Bin; Xu, Yi-Chun; Amos, Martyn
2007-01-01
In this paper we present two new algorithms for the layout optimization problem: this concerns the placement of circular, weighted objects inside a circular container, the two objectives being to minimize imbalance of mass and to minimize the radius of the container. This problem carries real practical significance in industrial applications (such as the design of satellites), as well as being of significant theoretical interest. We present two nature-inspired algorithms for this problem, the first based on simulated annealing, and the second on particle swarm optimization. We compare our algorithms with the existing best-known algorithm, and show that our approaches out-perform it in terms of both solution quality and execution time.
Optimized TRIAD Algorithm for Attitude Determination
NASA Technical Reports Server (NTRS)
Bar-Itzhack, Itzhack Y.; Harman, Richard R.
1996-01-01
TRIAD is a well known simple algorithm that generates the attitude matrix between two coordinate systems when the components of two abstract vectors are given in the two systems. TRIAD however, is sensitive to the order in which the algorithm handles the vectors, such that the resulting attitude matrix is influenced more by the vector processed first. In this work we present a new algorithm, which we call Optimized TRIAD, that blends in a specified manner the two matrices generated by TRIAD when processing one vector first, and then when processing the other vector first. On the average, Optimized TRIAD yields a matrix which is better than either one of the two matrices in that is ti the closest to the correct matrix. This result is demonstrated through simulation.
Algorithm for fixed-range optimal trajectories
NASA Technical Reports Server (NTRS)
Lee, H. Q.; Erzberger, H.
1980-01-01
An algorithm for synthesizing optimal aircraft trajectories for specified range was developed and implemented in a computer program written in FORTRAN IV. The algorithm, its computer implementation, and a set of example optimum trajectories for the Boeing 727-100 aircraft are described. The algorithm optimizes trajectories with respect to a cost function that is the weighted sum of fuel cost and time cost. The optimum trajectory consists at most of a three segments: climb, cruise, and descent. The climb and descent profiles are generated by integrating a simplified set of kinematic and dynamic equations wherein the total energy of the aircraft is the independent or time like variable. At each energy level the optimum airspeeds and thrust settings are obtained as the values that minimize the variational Hamiltonian. Although the emphasis is on an off-line, open-loop computation, eventually the most important application will be in an on-board flight management system.
An efficient algorithm for numerical airfoil optimization
NASA Technical Reports Server (NTRS)
Vanderplaats, G. N.
1979-01-01
A new optimization algorithm is presented. The method is based on sequential application of a second-order Taylor's series approximation to the airfoil characteristics. Compared to previous methods, design efficiency improvements of more than a factor of 2 are demonstrated. If multiple optimizations are performed, the efficiency improvements are more dramatic due to the ability of the technique to utilize existing data. The method is demonstrated by application to subsonic and transonic airfoil design but is a general optimization technique and is not limited to a particular application or aerodynamic analysis.
Swarm algorithms with chaotic jumps for optimization of multimodal functions
NASA Astrophysics Data System (ADS)
Krohling, Renato A.; Mendel, Eduardo; Campos, Mauro
2011-11-01
In this article, the use of some well-known versions of particle swarm optimization (PSO) namely the canonical PSO, the bare bones PSO (BBPSO) and the fully informed particle swarm (FIPS) is investigated on multimodal optimization problems. A hybrid approach which consists of swarm algorithms combined with a jump strategy in order to escape from local optima is developed and tested. The jump strategy is based on the chaotic logistic map. The hybrid algorithm was tested for all three versions of PSO and simulation results show that the addition of the jump strategy improves the performance of swarm algorithms for most of the investigated optimization problems. Comparison with the off-the-shelf PSO with local topology (l best model) has also been performed and indicates the superior performance of the standard PSO with chaotic jump over the standard both using local topology (l best model).
Optimization Algorithms in Optimal Predictions of Atomistic Properties by Kriging.
Di Pasquale, Nicodemo; Davie, Stuart J; Popelier, Paul L A
2016-04-12
The machine learning method kriging is an attractive tool to construct next-generation force fields. Kriging can accurately predict atomistic properties, which involves optimization of the so-called concentrated log-likelihood function (i.e., fitness function). The difficulty of this optimization problem quickly escalates in response to an increase in either the number of dimensions of the system considered or the size of the training set. In this article, we demonstrate and compare the use of two search algorithms, namely, particle swarm optimization (PSO) and differential evolution (DE), to rapidly obtain the maximum of this fitness function. The ability of these two algorithms to find a stationary point is assessed by using the first derivative of the fitness function. Finally, the converged position obtained by PSO and DE is refined through the limited-memory Broyden-Fletcher-Goldfarb-Shanno bounded (L-BFGS-B) algorithm, which belongs to the class of quasi-Newton algorithms. We show that both PSO and DE are able to come close to the stationary point, even in high-dimensional problems. They do so in a reasonable amount of time, compared to that with the Newton and quasi-Newton algorithms, regardless of the starting position in the search space of kriging hyperparameters. The refinement through L-BFGS-B is able to give the position of the maximum with whichever precision is desired.
Optimal control of switched linear systems based on Migrant Particle Swarm Optimization algorithm
NASA Astrophysics Data System (ADS)
Xie, Fuqiang; Wang, Yongji; Zheng, Zongzhun; Li, Chuanfeng
2009-10-01
The optimal control problem for switched linear systems with internally forced switching has more constraints than with externally forced switching. Heavy computations and slow convergence in solving this problem is a major obstacle. In this paper we describe a new approach for solving this problem, which is called Migrant Particle Swarm Optimization (Migrant PSO). Imitating the behavior of a flock of migrant birds, the Migrant PSO applies naturally to both continuous and discrete spaces, in which definitive optimization algorithm and stochastic search method are combined. The efficacy of the proposed algorithm is illustrated via a numerical example.
NASA Astrophysics Data System (ADS)
Chen, Fang; Chang, Honglong; Yuan, Weizheng; Wilcock, Reuben; Kraft, Michael
2012-10-01
This paper describes a novel multiobjective parameter optimization method based on a genetic algorithm (GA) for the design of a sixth-order continuous-time, force feedback band-pass sigma-delta modulator (BP-ΣΔM) interface for the sense mode of a MEMS gyroscope. The design procedure starts by deriving a parameterized Simulink model of the BP-ΣΔM gyroscope interface. The system parameters are then optimized by the GA. Consequently, the optimized design is tested for robustness by a Monte Carlo analysis to find a solution that is both optimal and robust. System level simulations result in a signal-to-noise ratio (SNR) larger than 90 dB in a bandwidth of 64 Hz with a 200° s-1 angular rate input signal; the noise floor is about -100 dBV Hz-1/2. The simulations are compared to measured data from a hardware implementation. For zero input rotation with the gyroscope operating at atmospheric pressure, the spectrum of the output bitstream shows an obvious band-pass noise shaping and a deep notch at the gyroscope resonant frequency. The noise floor of measured power spectral density (PSD) of the output bitstream agrees well with simulation of the optimized system level model. The bias stability, rate sensitivity and nonlinearity of the gyroscope controlled by an optimized BP-ΣΔM closed-loop interface are 34.15° h-1, 22.3 mV °-1 s-1, 98 ppm, respectively. This compares to a simple open-loop interface for which the corresponding values are 89° h-1, 14.3 mV °-1 s-1, 7600 ppm, and a nonoptimized BP-ΣΔM closed-loop interface with corresponding values of 60° h-1, 17 mV °-1 s-1, 200 ppm.
Optimized dynamical decoupling via genetic algorithms
NASA Astrophysics Data System (ADS)
Quiroz, Gregory; Lidar, Daniel A.
2013-11-01
We utilize genetic algorithms aided by simulated annealing to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse intervals and perform the optimization with respect to pulse type and order. In this manner, we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite-pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure that underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.
Polynomial Local Improvement Algorithms in Combinatorial Optimization.
1981-11-01
NUMBER SOL 81- 21 IIS -J O 15 14. TITLE (am#Su&Utl & YEO RPR ERO OEE Polynomial Local Improvement Algorithms in TcnclRpr Combinatorial Optimization 6...Stanford, CA 94305 II . CONTROLLING OFFICE NAME AND ADDRESS It. REPORT DATE Office of Naval Research - Dept. of the Navy November 1981 800 N. Qu~incy Street...corresponds to a node of the tree. ii ) The father of a vertex is its optimal adjacent vertex; if a vertex is a local optimum, it has no father. The tree is
A novel mating approach for genetic algorithms.
Galán, Severino F; Mengshoel, Ole J; Pinter, Rafael
2013-01-01
Genetic algorithms typically use crossover, which relies on mating a set of selected parents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in combination with a traditional similarity-based criterion to measure distance between individuals or with a fitness-based criterion. We introduce a parameter called the mating index that allows different mating strategies to be developed within a uniform framework: an exploitative strategy called best-first, an explorative strategy called best-last, and an adaptive strategy called self-adaptive. Self-adaptive mating is defined in the context of the novel algorithm, and aims to achieve a balance between exploitation and exploration in a domain-independent manner. The present work formally defines the novel mating approach, analyzes its behavior, and conducts an extensive experimental study to quantitatively determine its benefits. In the domain of real function optimization, the experiments show that, as the degree of multimodality of the function at hand grows, increasing the mating index improves performance. In the case of the self-adaptive mating strategy, the experiments give strong results for several case studies.
FOGSAA: Fast Optimal Global Sequence Alignment Algorithm
NASA Astrophysics Data System (ADS)
Chakraborty, Angana; Bandyopadhyay, Sanghamitra
2013-04-01
In this article we propose a Fast Optimal Global Sequence Alignment Algorithm, FOGSAA, which aligns a pair of nucleotide/protein sequences faster than any optimal global alignment method including the widely used Needleman-Wunsch (NW) algorithm. FOGSAA is applicable for all types of sequences, with any scoring scheme, and with or without affine gap penalty. Compared to NW, FOGSAA achieves a time gain of (70-90)% for highly similar nucleotide sequences (> 80% similarity), and (54-70)% for sequences having (30-80)% similarity. For other sequences, it terminates with an approximate score. For protein sequences, the average time gain is between (25-40)%. Compared to three heuristic global alignment methods, the quality of alignment is improved by about 23%-53%. FOGSAA is, in general, suitable for aligning any two sequences defined over a finite alphabet set, where the quality of the global alignment is of supreme importance.
Intelligent perturbation algorithms for space scheduling optimization
NASA Technical Reports Server (NTRS)
Kurtzman, Clifford R.
1990-01-01
The optimization of space operations is examined in the light of optimization heuristics for computer algorithms and iterative search techniques. Specific attention is given to the search concepts known collectively as intelligent perturbation algorithms (IPAs) and their application to crew/resource allocation problems. IPAs iteratively examine successive schedules which become progressively more efficient, and the characteristics of good perturbation operators are listed. IPAs can be applied to aerospace systems to efficiently utilize crews, payloads, and resources in the context of systems such as Space-Station scheduling. A program is presented called the MFIVE Space Station Scheduling Worksheet which generates task assignments and resource usage structures. The IPAs can be used to develop flexible manifesting and scheduling for the Industrial Space Facility.
RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Hogenboom, Alexander; Milea, Viorel; Frasincar, Flavius; Kaymak, Uzay
The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm called RCQ-GA that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark further improves.
Algorithms for optimizing CT fluence control
NASA Astrophysics Data System (ADS)
Hsieh, Scott S.; Pelc, Norbert J.
2014-03-01
The ability to customize the incident x-ray fluence in CT via beam-shaping filters or mA modulation is known to improve image quality and/or reduce radiation dose. Previous work has shown that complete control of x-ray fluence (ray-by-ray fluence modulation) would further improve dose efficiency. While complete control of fluence is not currently possible, emerging concepts such as dynamic attenuators and inverse-geometry CT allow nearly complete control to be realized. Optimally using ray-by-ray fluence modulation requires solving a very high-dimensional optimization problem. Most optimization techniques fail or only provide approximate solutions. We present efficient algorithms for minimizing mean or peak variance given a fixed dose limit. The reductions in variance can easily be translated to reduction in dose, if the original variance met image quality requirements. For mean variance, a closed form solution is derived. The peak variance problem is recast as iterated, weighted mean variance minimization, and at each iteration it is possible to bound the distance to the optimal solution. We apply our algorithms in simulations of scans of the thorax and abdomen. Peak variance reductions of 45% and 65% are demonstrated in the abdomen and thorax, respectively, compared to a bowtie filter alone. Mean variance shows smaller gains (about 15%).
Scope of Gradient and Genetic Algorithms in Multivariable Function Optimization
NASA Technical Reports Server (NTRS)
Shaykhian, Gholam Ali; Sen, S. K.
2007-01-01
Global optimization of a multivariable function - constrained by bounds specified on each variable and also unconstrained - is an important problem with several real world applications. Deterministic methods such as the gradient algorithms as well as the randomized methods such as the genetic algorithms may be employed to solve these problems. In fact, there are optimization problems where a genetic algorithm/an evolutionary approach is preferable at least from the quality (accuracy) of the results point of view. From cost (complexity) point of view, both gradient and genetic approaches are usually polynomial-time; there are no serious differences in this regard, i.e., the computational complexity point of view. However, for certain types of problems, such as those with unacceptably erroneous numerical partial derivatives and those with physically amplified analytical partial derivatives whose numerical evaluation involves undesirable errors and/or is messy, a genetic (stochastic) approach should be a better choice. We have presented here the pros and cons of both the approaches so that the concerned reader/user can decide which approach is most suited for the problem at hand. Also for the function which is known in a tabular form, instead of an analytical form, as is often the case in an experimental environment, we attempt to provide an insight into the approaches focusing our attention toward accuracy. Such an insight will help one to decide which method, out of several available methods, should be employed to obtain the best (least error) output. *
Multiobjective Optimization of Rocket Engine Pumps Using Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
Oyama, Akira; Liou, Meng-Sing
2001-01-01
A design optimization method for turbopumps of cryogenic rocket engines has been developed. Multiobjective Evolutionary Algorithm (MOEA) is used for multiobjective pump design optimizations. Performances of design candidates are evaluated by using the meanline pump flow modeling method based on the Euler turbine equation coupled with empirical correlations for rotor efficiency. To demonstrate the feasibility of the present approach, a single stage centrifugal pump design and multistage pump design optimizations are presented. In both cases, the present method obtains very reasonable Pareto-optimal solutions that include some designs outperforming the original design in total head while reducing input power by one percent. Detailed observation of the design results also reveals some important design criteria for turbopumps in cryogenic rocket engines. These results demonstrate the feasibility of the EA-based design optimization method in this field.
A Matrix-Free Algorithm for Multidisciplinary Design Optimization
NASA Astrophysics Data System (ADS)
Lambe, Andrew Borean
Multidisciplinary design optimization (MDO) is an approach to engineering design that exploits the coupling between components or knowledge disciplines in a complex system to improve the final product. In aircraft design, MDO methods can be used to simultaneously design the outer shape of the aircraft and the internal structure, taking into account the complex interaction between the aerodynamic forces and the structural flexibility. Efficient strategies are needed to solve such design optimization problems and guarantee convergence to an optimal design. This work begins with a comprehensive review of MDO problem formulations and solution algorithms. First, a fundamental MDO problem formulation is defined from which other formulations may be obtained through simple transformations. Using these fundamental problem formulations, decomposition methods from the literature are reviewed and classified. All MDO methods are presented in a unified mathematical notation to facilitate greater understanding. In addition, a novel set of diagrams, called extended design structure matrices, are used to simultaneously visualize both data communication and process flow between the many software components of each method. For aerostructural design optimization, modern decomposition-based MDO methods cannot efficiently handle the tight coupling between the aerodynamic and structural states. This fact motivates the exploration of methods that can reduce the computational cost. A particular structure in the direct and adjoint methods for gradient computation. motivates the idea of a matrix-free optimization method. A simple matrix-free optimizer is developed based on the augmented Lagrangian algorithm. This new matrix-free optimizer is tested on two structural optimization problems and one aerostructural optimization problem. The results indicate that the matrix-free optimizer is able to efficiently solve structural and multidisciplinary design problems with thousands of variables and
A Matrix-Free Algorithm for Multidisciplinary Design Optimization
NASA Astrophysics Data System (ADS)
Lambe, Andrew Borean
Multidisciplinary design optimization (MDO) is an approach to engineering design that exploits the coupling between components or knowledge disciplines in a complex system to improve the final product. In aircraft design, MDO methods can be used to simultaneously design the outer shape of the aircraft and the internal structure, taking into account the complex interaction between the aerodynamic forces and the structural flexibility. Efficient strategies are needed to solve such design optimization problems and guarantee convergence to an optimal design. This work begins with a comprehensive review of MDO problem formulations and solution algorithms. First, a fundamental MDO problem formulation is defined from which other formulations may be obtained through simple transformations. Using these fundamental problem formulations, decomposition methods from the literature are reviewed and classified. All MDO methods are presented in a unified mathematical notation to facilitate greater understanding. In addition, a novel set of diagrams, called extended design structure matrices, are used to simultaneously visualize both data communication and process flow between the many software components of each method. For aerostructural design optimization, modern decomposition-based MDO methods cannot efficiently handle the tight coupling between the aerodynamic and structural states. This fact motivates the exploration of methods that can reduce the computational cost. A particular structure in the direct and adjoint methods for gradient computation motivates the idea of a matrix-free optimization method. A simple matrix-free optimizer is developed based on the augmented Lagrangian algorithm. This new matrix-free optimizer is tested on two structural optimization problems and one aerostructural optimization problem. The results indicate that the matrix-free optimizer is able to efficiently solve structural and multidisciplinary design problems with thousands of variables and
Optimization of meander line antennas for RFID applications by using genetic algorithm
NASA Astrophysics Data System (ADS)
Bucuci, Stefania C.; Anchidin, Liliana; Dumitrascu, Ana; Danisor, Alin; Berescu, Serban; Tamas, Razvan D.
2015-02-01
In this paper, we propose an approach of optimization of meander line antennas by using genetic algorithm. Such antennas are used in RFID applications. As opposed to other approaches for meander antennas, we propose the use of only two optimization objectives, i.e. gain and size. As an example, we have optimized a single meander dipole antenna, resonating at 869 MHz.
New approaches to the design optimization of hydrofoils
NASA Astrophysics Data System (ADS)
Beyhaghi, Pooriya; Meneghello, Gianluca; Bewley, Thomas
2015-11-01
Two simulation-based approaches are developed to optimize the design of hydrofoils for foiling catamarans, with the objective of maximizing efficiency (lift/drag). In the first, a simple hydrofoil model based on the vortex-lattice method is coupled with a hybrid global and local optimization algorithm that combines our Delaunay-based optimization algorithm with a Generalized Pattern Search. This optimization procedure is compared with the classical Newton-based optimization method. The accuracy of the vortex-lattice simulation of the optimized design is compared with a more accurate and computationally expensive LES-based simulation. In the second approach, the (expensive) LES model of the flow is used directly during the optimization. A modified Delaunay-based optimization algorithm is used to maximize the efficiency of the optimization, which measures a finite-time averaged approximation of the infinite-time averaged value of an ergodic and stationary process. Since the optimization algorithm takes into account the uncertainty of the finite-time averaged approximation of the infinite-time averaged statistic of interest, the total computational time of the optimization algorithm is significantly reduced. Results from the two different approaches are compared.
Multi-objective nested algorithms for optimal reservoir operation
NASA Astrophysics Data System (ADS)
Delipetrev, Blagoj; Solomatine, Dimitri
2016-04-01
The optimal reservoir operation is in general a multi-objective problem, meaning that multiple objectives are to be considered at the same time. For solving multi-objective optimization problems there exist a large number of optimization algorithms - which result in a generation of a Pareto set of optimal solutions (typically containing a large number of them), or more precisely, its approximation. At the same time, due to the complexity and computational costs of solving full-fledge multi-objective optimization problems some authors use a simplified approach which is generically called "scalarization". Scalarization transforms the multi-objective optimization problem to a single-objective optimization problem (or several of them), for example by (a) single objective aggregated weighted functions, or (b) formulating some objectives as constraints. We are using the approach (a). A user can decide how many multi-objective single search solutions will generate, depending on the practical problem at hand and by choosing a particular number of the weight vectors that are used to weigh the objectives. It is not guaranteed that these solutions are Pareto optimal, but they can be treated as a reasonably good and practically useful approximation of a Pareto set, albeit small. It has to be mentioned that the weighted-sum approach has its known shortcomings because the linear scalar weights will fail to find Pareto-optimal policies that lie in the concave region of the Pareto front. In this context the considered approach is implemented as follows: there are m sets of weights {w1i, …wni} (i starts from 1 to m), and n objectives applied to single objective aggregated weighted sum functions of nested dynamic programming (nDP), nested stochastic dynamic programming (nSDP) and nested reinforcement learning (nRL). By employing the multi-objective optimization by a sequence of single-objective optimization searches approach, these algorithms acquire the multi-objective properties
An adaptive penalty method for DIRECT algorithm in engineering optimization
NASA Astrophysics Data System (ADS)
Vilaça, Rita; Rocha, Ana Maria A. C.
2012-09-01
The most common approach for solving constrained optimization problems is based on penalty functions, where the constrained problem is transformed into a sequence of unconstrained problem by penalizing the objective function when constraints are violated. In this paper, we analyze the implementation of an adaptive penalty method, within the DIRECT algorithm, in which the constraints that are more difficult to be satisfied will have relatively higher penalty values. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.
Optimization of image processing algorithms on mobile platforms
NASA Astrophysics Data System (ADS)
Poudel, Pramod; Shirvaikar, Mukul
2011-03-01
This work presents a technique to optimize popular image processing algorithms on mobile platforms such as cell phones, net-books and personal digital assistants (PDAs). The increasing demand for video applications like context-aware computing on mobile embedded systems requires the use of computationally intensive image processing algorithms. The system engineer has a mandate to optimize them so as to meet real-time deadlines. A methodology to take advantage of the asymmetric dual-core processor, which includes an ARM and a DSP core supported by shared memory, is presented with implementation details. The target platform chosen is the popular OMAP 3530 processor for embedded media systems. It has an asymmetric dual-core architecture with an ARM Cortex-A8 and a TMS320C64x Digital Signal Processor (DSP). The development platform was the BeagleBoard with 256 MB of NAND RAM and 256 MB SDRAM memory. The basic image correlation algorithm is chosen for benchmarking as it finds widespread application for various template matching tasks such as face-recognition. The basic algorithm prototypes conform to OpenCV, a popular computer vision library. OpenCV algorithms can be easily ported to the ARM core which runs a popular operating system such as Linux or Windows CE. However, the DSP is architecturally more efficient at handling DFT algorithms. The algorithms are tested on a variety of images and performance results are presented measuring the speedup obtained due to dual-core implementation. A major advantage of this approach is that it allows the ARM processor to perform important real-time tasks, while the DSP addresses performance-hungry algorithms.
Lunar Habitat Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
SanScoucie, M. P.; Hull, P. V.; Tinker, M. L.; Dozier, G. V.
2007-01-01
Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters.
Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
2005-01-01
This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.
Harmony search algorithm: application to the redundancy optimization problem
NASA Astrophysics Data System (ADS)
Nahas, Nabil; Thien-My, Dao
2010-09-01
The redundancy optimization problem is a well known NP-hard problem which involves the selection of elements and redundancy levels to maximize system performance, given different system-level constraints. This article presents an efficient algorithm based on the harmony search algorithm (HSA) to solve this optimization problem. The HSA is a new nature-inspired algorithm which mimics the improvization process of music players. Two kinds of problems are considered in testing the proposed algorithm, with the first limited to the binary series-parallel system, where the problem consists of a selection of elements and redundancy levels used to maximize the system reliability given various system-level constraints; the second problem for its part concerns the multi-state series-parallel systems with performance levels ranging from perfect operation to complete failure, and in which identical redundant elements are included in order to achieve a desirable level of availability. Numerical results for test problems from previous research are reported and compared. The results of HSA showed that this algorithm could provide very good solutions when compared to those obtained through other approaches.
Optimal reservoir operation policies using novel nested algorithms
NASA Astrophysics Data System (ADS)
Delipetrev, Blagoj; Jonoski, Andreja; Solomatine, Dimitri
2015-04-01
Historically, the two most widely practiced methods for optimal reservoir operation have been dynamic programming (DP) and stochastic dynamic programming (SDP). These two methods suffer from the so called "dual curse" which prevents them to be used in reasonably complex water systems. The first one is the "curse of dimensionality" that denotes an exponential growth of the computational complexity with the state - decision space dimension. The second one is the "curse of modelling" that requires an explicit model of each component of the water system to anticipate the effect of each system's transition. We address the problem of optimal reservoir operation concerning multiple objectives that are related to 1) reservoir releases to satisfy several downstream users competing for water with dynamically varying demands, 2) deviations from the target minimum and maximum reservoir water levels and 3) hydropower production that is a combination of the reservoir water level and the reservoir releases. Addressing such a problem with classical methods (DP and SDP) requires a reasonably high level of discretization of the reservoir storage volume, which in combination with the required releases discretization for meeting the demands of downstream users leads to computationally expensive formulations and causes the curse of dimensionality. We present a novel approach, named "nested" that is implemented in DP, SDP and reinforcement learning (RL) and correspondingly three new algorithms are developed named nested DP (nDP), nested SDP (nSDP) and nested RL (nRL). The nested algorithms are composed from two algorithms: 1) DP, SDP or RL and 2) nested optimization algorithm. Depending on the way we formulate the objective function related to deficits in the allocation problem in the nested optimization, two methods are implemented: 1) Simplex for linear allocation problems, and 2) quadratic Knapsack method in the case of nonlinear problems. The novel idea is to include the nested
A "Tuned" Mask Learnt Approach Based on Gravitational Search Algorithm.
Wan, Youchuan; Wang, Mingwei; Ye, Zhiwei; Lai, Xudong
2016-01-01
Texture image classification is an important topic in many applications in machine vision and image analysis. Texture feature extracted from the original texture image by using "Tuned" mask is one of the simplest and most effective methods. However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) easily fall into the local optimum. A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper. In the proposed approach, "Tuned" mask is viewed as a constrained optimization problem and the optimal "Tuned" mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA). The optimal "Tuned" mask is achieved through the convergence of GSA. The proposed approach has been, respectively, tested on some public texture and remote sensing images. The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO), and artificial immune algorithm (AIA). Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison. Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy.
Parallel Algorithms for Graph Optimization using Tree Decompositions
Sullivan, Blair D; Weerapurage, Dinesh P; Groer, Christopher S
2012-06-01
Although many $\\cal{NP}$-hard graph optimization problems can be solved in polynomial time on graphs of bounded tree-width, the adoption of these techniques into mainstream scientific computation has been limited due to the high memory requirements of the necessary dynamic programming tables and excessive runtimes of sequential implementations. This work addresses both challenges by proposing a set of new parallel algorithms for all steps of a tree decomposition-based approach to solve the maximum weighted independent set problem. A hybrid OpenMP/MPI implementation includes a highly scalable parallel dynamic programming algorithm leveraging the MADNESS task-based runtime, and computational results demonstrate scaling. This work enables a significant expansion of the scale of graphs on which exact solutions to maximum weighted independent set can be obtained, and forms a framework for solving additional graph optimization problems with similar techniques.
Quantum algorithm for molecular properties and geometry optimization.
Kassal, Ivan; Aspuru-Guzik, Alán
2009-12-14
Quantum computers, if available, could substantially accelerate quantum simulations. We extend this result to show that the computation of molecular properties (energy derivatives) could also be sped up using quantum computers. We provide a quantum algorithm for the numerical evaluation of molecular properties, whose time cost is a constant multiple of the time needed to compute the molecular energy, regardless of the size of the system. Molecular properties computed with the proposed approach could also be used for the optimization of molecular geometries or other properties. For that purpose, we discuss the benefits of quantum techniques for Newton's method and Householder methods. Finally, global minima for the proposed optimizations can be found using the quantum basin hopper algorithm, which offers an additional quadratic reduction in cost over classical multi-start techniques.
Economic Dispatch Using Genetic Algorithm Based Hybrid Approach
Tahir Nadeem Malik; Aftab Ahmad; Shahab Khushnood
2006-07-01
Power Economic Dispatch (ED) is vital and essential daily optimization procedure in the system operation. Present day large power generating units with multi-valves steam turbines exhibit a large variation in the input-output characteristic functions, thus non-convexity appears in the characteristic curves. Various mathematical and optimization techniques have been developed, applied to solve economic dispatch (ED) problem. Most of these are calculus-based optimization algorithms that are based on successive linearization and use the first and second order differentiations of objective function and its constraint equations as the search direction. They usually require heat input, power output characteristics of generators to be of monotonically increasing nature or of piecewise linearity. These simplifying assumptions result in an inaccurate dispatch. Genetic algorithms have used to solve the economic dispatch problem independently and in conjunction with other AI tools and mathematical programming approaches. Genetic algorithms have inherent ability to reach the global minimum region of search space in a short time, but then take longer time to converge the solution. GA based hybrid approaches get around this problem and produce encouraging results. This paper presents brief survey on hybrid approaches for economic dispatch, an architecture of extensible computational framework as common environment for conventional, genetic algorithm and hybrid approaches based solution for power economic dispatch, the implementation of three algorithms in the developed framework. The framework tested on standard test systems for its performance evaluation. (authors)
Optimal robust motion controller design using multiobjective genetic algorithm.
Sarjaš, Andrej; Svečko, Rajko; Chowdhury, Amor
2014-01-01
This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm-differential evolution.
Expedite Particle Swarm Optimization Algorithm (EPSO) for Optimization of MSA
NASA Astrophysics Data System (ADS)
Rathi, Amit; Vijay, Ritu
This paper presents a new designing method of Rectangular patch Microstrip Antenna using an Artificial searches Algorithm with some constraints. It requires two stages for designing. In first stage, bandwidth of MSA is modeled using bench Mark function. In second stage, output of first stage give to modified Artificial search Algorithm which is Particle Swarm Algorithm (PSO) as input and get output in the form of five parameter- dimensions width, frequency range, dielectric loss tangent, length over a ground plane with a substrate thickness and electrical thickness. In PSO Cognition, factor and Social learning Factor give very important effect on balancing the local search and global search in PSO. Basing the modification of cognition factor and social learning factor, this paper presents the strategy that at the starting process cognition-learning factor has more effect then social learning factor. Gradually social learning factor has more impact after learning cognition factor for find out global best. The aim is to find out under above circumstances these modifications in PSO can give better result for optimization of microstrip Antenna (MSA).
Assessment of Optimal Interrogation Approaches
2007-05-01
4 ( 01-03-2006 Final March 2006 - May 2007 Assessment of Optimal Interrogation Approaches H9C101-6-0051... interrogator . Specifically, DACA wanted the researchers to gather information from "expert" interrogators (referred to as "superior" interrogators ...common approaches/techniques that are employed by the majority of interrogators . U U U U 129 David E. Smith (314) 209-9495 ext 701 Prepared for the
Managing and learning with multiple models: Objectives and optimization algorithms
Probert, William J. M.; Hauser, C.E.; McDonald-Madden, E.; Runge, M.C.; Baxter, P.W.J.; Possingham, H.P.
2011-01-01
The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. ?? 2010 Elsevier Ltd.
Optimizing remediation of an unconfined aquifer using a hybrid algorithm.
Hsiao, Chin-Tsai; Chang, Liang-Cheng
2005-01-01
We present a novel hybrid algorithm, integrating a genetic algorithm (GA) and constrained differential dynamic programming (CDDP), to achieve remediation planning for an unconfined aquifer. The objective function includes both fixed and dynamic operation costs. GA determines the primary structure of the proposed algorithm, and a chromosome therein implemented by a series of binary digits represents a potential network design. The time-varying optimal operation cost associated with the network design is computed by the CDDP, in which is embedded a numerical transport model. Several computational approaches, including a chromosome bookkeeping procedure, are implemented to alleviate computational loading. Additionally, case studies that involve fixed and time-varying operating costs for confined and unconfined aquifers, respectively, are discussed to elucidate the effectiveness of the proposed algorithm. Simulation results indicate that the fixed costs markedly affect the optimal design, including the number and locations of the wells. Furthermore, the solution obtained using the confined approximation for an unconfined aquifer may be infeasible, as determined by an unconfined simulation.
A sequential linear optimization approach for controller design
NASA Technical Reports Server (NTRS)
Horta, L. G.; Juang, J.-N.; Junkins, J. L.
1985-01-01
A linear optimization approach with a simple real arithmetic algorithm is presented for reliable controller design and vibration suppression of flexible structures. Using first order sensitivity of the system eigenvalues with respect to the design parameters in conjunction with a continuation procedure, the method converts a nonlinear optimization problem into a maximization problem with linear inequality constraints. The method of linear programming is then applied to solve the converted linear optimization problem. The general efficiency of the linear programming approach allows the method to handle structural optimization problems with a large number of inequality constraints on the design vector. The method is demonstrated using a truss beam finite element model for the optimal sizing and placement of active/passive-structural members for damping augmentation. Results using both the sequential linear optimization approach and nonlinear optimization are presented and compared. The insensitivity to initial conditions of the linear optimization approach is also demonstrated.
Algorithm Optimally Orders Forward-Chaining Inference Rules
NASA Technical Reports Server (NTRS)
James, Mark
2008-01-01
People typically develop knowledge bases in a somewhat ad hoc manner by incrementally adding rules with no specific organization. This often results in a very inefficient execution of those rules since they are so often order sensitive. This is relevant to tasks like Deep Space Network in that it allows the knowledge base to be incrementally developed and have it automatically ordered for efficiency. Although data flow analysis was first developed for use in compilers for producing optimal code sequences, its usefulness is now recognized in many software systems including knowledge-based systems. However, this approach for exhaustively computing data-flow information cannot directly be applied to inference systems because of the ubiquitous execution of the rules. An algorithm is presented that efficiently performs a complete producer/consumer analysis for each antecedent and consequence clause in a knowledge base to optimally order the rules to minimize inference cycles. An algorithm was developed that optimally orders a knowledge base composed of forwarding chaining inference rules such that independent inference cycle executions are minimized, thus, resulting in significantly faster execution. This algorithm was integrated into the JPL tool Spacecraft Health Inference Engine (SHINE) for verification and it resulted in a significant reduction in inference cycles for what was previously considered an ordered knowledge base. For a knowledge base that is completely unordered, then the improvement is much greater.
Optimizing SRF Gun Cavity Profiles in a Genetic Algorithm Framework
Alicia Hofler, Pavel Evtushenko, Frank Marhauser
2009-09-01
Automation of DC photoinjector designs using a genetic algorithm (GA) based optimization is an accepted practice in accelerator physics. Allowing the gun cavity field profile shape to be varied can extend the utility of this optimization methodology to superconducting and normal conducting radio frequency (SRF/RF) gun based injectors. Finding optimal field and cavity geometry configurations can provide guidance for cavity design choices and verify existing designs. We have considered two approaches for varying the electric field profile. The first is to determine the optimal field profile shape that should be used independent of the cavity geometry, and the other is to vary the geometry of the gun cavity structure to produce an optimal field profile. The first method can provide a theoretical optimal and can illuminate where possible gains can be made in field shaping. The second method can produce more realistically achievable designs that can be compared to existing designs. In this paper, we discuss the design and implementation for these two methods for generating field profiles for SRF/RF guns in a GA based injector optimization scheme and provide preliminary results.
Effective multi-objective optimization with the coral reefs optimization algorithm
NASA Astrophysics Data System (ADS)
Salcedo-Sanz, S.; Pastor-Sánchez, A.; Portilla-Figueras, J. A.; Prieto, L.
2016-06-01
In this article a new algorithm for multi-objective optimization is presented, the Multi-Objective Coral Reefs Optimization (MO-CRO) algorithm. The algorithm is based on the simulation of processes in coral reefs, such as corals' reproduction and fight for space in the reef. The adaptation to multi-objective problems is a process based on domination or non-domination during the process of fight for space in the reef. The final MO-CRO is an easily-implemented and fast algorithm, simple and robust, since it is able to keep diversity in the population of corals (solutions) in a natural way. The experimental evaluation of this new approach for multi-objective optimization problems is carried out on different multi-objective benchmark problems, where the MO-CRO has shown excellent performance in cases with limited computational resources, and in a real-world problem of wind speed prediction, where the MO-CRO algorithm is used to find the best set of features to predict the wind speed, taking into account two objective functions related to the performance of the prediction and the computation time of the regressor.
Efficiency Improvements to the Displacement Based Multilevel Structural Optimization Algorithm
NASA Technical Reports Server (NTRS)
Plunkett, C. L.; Striz, A. G.; Sobieszczanski-Sobieski, J.
2001-01-01
Multilevel Structural Optimization (MSO) continues to be an area of research interest in engineering optimization. In the present project, the weight optimization of beams and trusses using Displacement based Multilevel Structural Optimization (DMSO), a member of the MSO set of methodologies, is investigated. In the DMSO approach, the optimization task is subdivided into a single system and multiple subsystems level optimizations. The system level optimization minimizes the load unbalance resulting from the use of displacement functions to approximate the structural displacements. The function coefficients are then the design variables. Alternately, the system level optimization can be solved using the displacements themselves as design variables, as was shown in previous research. Both approaches ensure that the calculated loads match the applied loads. In the subsystems level, the weight of the structure is minimized using the element dimensions as design variables. The approach is expected to be very efficient for large structures, since parallel computing can be utilized in the different levels of the problem. In this paper, the method is applied to a one-dimensional beam and a large three-dimensional truss. The beam was tested to study possible simplifications to the system level optimization. In previous research, polynomials were used to approximate the global nodal displacements. The number of coefficients of the polynomials equally matched the number of degrees of freedom of the problem. Here it was desired to see if it is possible to only match a subset of the degrees of freedom in the system level. This would lead to a simplification of the system level, with a resulting increase in overall efficiency. However, the methods tested for this type of system level simplification did not yield positive results. The large truss was utilized to test further improvements in the efficiency of DMSO. In previous work, parallel processing was applied to the
Improved hybrid optimization algorithm for 3D protein structure prediction.
Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang
2014-07-01
A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.
Optimization approaches for planning external beam radiotherapy
NASA Astrophysics Data System (ADS)
Gozbasi, Halil Ozan
Cancer begins when cells grow out of control as a result of damage to their DNA. These abnormal cells can invade healthy tissue and form tumors in various parts of the body. Chemotherapy, immunotherapy, surgery and radiotherapy are the most common treatment methods for cancer. According to American Cancer Society about half of the cancer patients receive a form of radiation therapy at some stage. External beam radiotherapy is delivered from outside the body and aimed at cancer cells to damage their DNA making them unable to divide and reproduce. The beams travel through the body and may damage nearby healthy tissue unless carefully planned. Therefore, the goal of treatment plan optimization is to find the best system parameters to deliver sufficient dose to target structures while avoiding damage to healthy tissue. This thesis investigates optimization approaches for two external beam radiation therapy techniques: Intensity-Modulated Radiation Therapy (IMRT) and Volumetric-Modulated Arc Therapy (VMAT). We develop automated treatment planning technology for IMRT that produces several high-quality treatment plans satisfying provided clinical requirements in a single invocation and without human guidance. A novel bi-criteria scoring based beam selection algorithm is part of the planning system and produces better plans compared to those produced using a well-known scoring-based algorithm. Our algorithm is very efficient and finds the beam configuration at least ten times faster than an exact integer programming approach. Solution times range from 2 minutes to 15 minutes which is clinically acceptable. With certain cancers, especially lung cancer, a patient's anatomy changes during treatment. These anatomical changes need to be considered in treatment planning. Fortunately, recent advances in imaging technology can provide multiple images of the treatment region taken at different points of the breathing cycle, and deformable image registration algorithms can
Algorithm for correcting optimization convergence errors in Eclipse.
Zacarias, Albert S; Mills, Michael D
2009-10-14
IMRT plans generated in Eclipse use a fast algorithm to evaluate dose for optimization and a more accurate algorithm for a final dose calculation, the Analytical Anisotropic Algorithm. The use of a fast optimization algorithm introduces optimization convergence errors into an IMRT plan. Eclipse has a feature where optimization may be performed on top of an existing base plan. This feature allows for the possibility of arriving at a recursive solution to optimization that relies on the accuracy of the final dose calculation algorithm and not the optimizer algorithm. When an IMRT plan is used as a base plan for a second optimization, the second optimization can compensate for heterogeneity and modulator errors in the original base plan. Plans with the same field arrangement as the initial base plan may be added together by adding the initial plan optimal fluence to the dose correcting plan optimal fluence.A simple procedure to correct for optimization errors is presented that may be implemented in the Eclipse treatment planning system, along with an Excel spreadsheet to add optimized fluence maps together.
An algorithmic approach to crustal deformation analysis
NASA Technical Reports Server (NTRS)
Iz, Huseyin Baki
1987-01-01
In recent years the analysis of crustal deformation measurements has become important as a result of current improvements in geodetic methods and an increasing amount of theoretical and observational data provided by several earth sciences. A first-generation data analysis algorithm which combines a priori information with current geodetic measurements was proposed. Relevant methods which can be used in the algorithm were discussed. Prior information is the unifying feature of this algorithm. Some of the problems which may arise through the use of a priori information in the analysis were indicated and preventive measures were demonstrated. The first step in the algorithm is the optimal design of deformation networks. The second step in the algorithm identifies the descriptive model of the deformation field. The final step in the algorithm is the improved estimation of deformation parameters. Although deformation parameters are estimated in the process of model discrimination, they can further be improved by the use of a priori information about them. According to the proposed algorithm this information must first be tested against the estimates calculated using the sample data only. Null-hypothesis testing procedures were developed for this purpose. Six different estimators which employ a priori information were examined. Emphasis was put on the case when the prior information is wrong and analytical expressions for possible improvements under incompatible prior information were derived.
A linear programming approach for optimal contrast-tone mapping.
Wu, Xiaolin
2011-05-01
This paper proposes a novel algorithmic approach of image enhancement via optimal contrast-tone mapping. In a fundamental departure from the current practice of histogram equalization for contrast enhancement, the proposed approach maximizes expected contrast gain subject to an upper limit on tone distortion and optionally to other constraints that suppress artifacts. The underlying contrast-tone optimization problem can be solved efficiently by linear programming. This new constrained optimization approach for image enhancement is general, and the user can add and fine tune the constraints to achieve desired visual effects. Experimental results demonstrate clearly superior performance of the new approach over histogram equalization and its variants.
Linear antenna array optimization using flower pollination algorithm.
Saxena, Prerna; Kothari, Ashwin
2016-01-01
Flower pollination algorithm (FPA) is a new nature-inspired evolutionary algorithm used to solve multi-objective optimization problems. The aim of this paper is to introduce FPA to the electromagnetics and antenna community for the optimization of linear antenna arrays. FPA is applied for the first time to linear array so as to obtain optimized antenna positions in order to achieve an array pattern with minimum side lobe level along with placement of deep nulls in desired directions. Various design examples are presented that illustrate the use of FPA for linear antenna array optimization, and subsequently the results are validated by benchmarking along with results obtained using other state-of-the-art, nature-inspired evolutionary algorithms such as particle swarm optimization, ant colony optimization and cat swarm optimization. The results suggest that in most cases, FPA outperforms the other evolutionary algorithms and at times it yields a similar performance.
Rezaee, Kh.; Azizi, E.; Haddadnia, J.
2016-01-01
Background Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer’s and stroke, it is the third widespread nervous disorder. Objective In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG) has been proposed. 844 hours of EEG were recorded form 23 pediatric patients consecutively with 163 occurrences of seizures. Signals had been collected from Children’s Hospital Boston with a sampling frequency of 256 Hz through 18 channels in order to assess epilepsy surgery. By selecting effective features from seizure and non-seizure signals of each individual and putting them into two categories, the proposed algorithm detects the onset of seizures quickly and with high sensitivity. Method In this algorithm, L-sec epochs of signals are displayed in form of a third-order tensor in spatial, spectral and temporal spaces by applying wavelet transform. Then, after applying general tensor discriminant analysis (GTDA) on tensors and calculating mapping matrix, feature vectors are extracted. GTDA increases the sensitivity of the algorithm by storing data without deleting them. Finally, K-Nearest neighbors (KNN) is used to classify the selected features. Results The results of simulating algorithm on algorithm standard dataset shows that the algorithm is capable of detecting 98 percent of seizures with an average delay of 4.7 seconds and the average error rate detection of three errors in 24 hours. Conclusion Today, the lack of an automated system to detect or predict the seizure onset is strongly felt. PMID:27672628
Remediation Optimization: Definition, Scope and Approach
This document provides a general definition, scope and approach for conducting optimization reviews within the Superfund Program and includes the fundamental principles and themes common to optimization.
LP based approach to optimal stable matchings
Teo, Chung-Piaw; Sethuraman, J.
1997-06-01
We study the classical stable marriage and stable roommates problems using a polyhedral approach. We propose a new LP formulation for the stable roommates problem. This formulation is non-empty if and only if the underlying roommates problem has a stable matching. Furthermore, for certain special weight functions on the edges, we construct a 2-approximation algorithm for the optimal stable roommates problem. Our technique uses a crucial geometry of the fractional solutions in this formulation. For the stable marriage problem, we show that a related geometry allows us to express any fractional solution in the stable marriage polytope as convex combination of stable marriage solutions. This leads to a genuinely simple proof of the integrality of the stable marriage polytope. Based on these ideas, we devise a heuristic to solve the optimal stable roommates problem. The heuristic combines the power of rounding and cutting-plane methods. We present some computational results based on preliminary implementations of this heuristic.
Optimized Algorithms for Prediction Within Robotic Tele-Operative Interfaces
NASA Technical Reports Server (NTRS)
Martin, Rodney A.; Wheeler, Kevin R.; Allan, Mark B.; SunSpiral, Vytas
2010-01-01
Robonaut, the humanoid robot developed at the Dexterous Robotics Labo ratory at NASA Johnson Space Center serves as a testbed for human-rob ot collaboration research and development efforts. One of the recent efforts investigates how adjustable autonomy can provide for a safe a nd more effective completion of manipulation-based tasks. A predictiv e algorithm developed in previous work was deployed as part of a soft ware interface that can be used for long-distance tele-operation. In this work, Hidden Markov Models (HMM?s) were trained on data recorded during tele-operation of basic tasks. In this paper we provide the d etails of this algorithm, how to improve upon the methods via optimization, and also present viable alternatives to the original algorithmi c approach. We show that all of the algorithms presented can be optim ized to meet the specifications of the metrics shown as being useful for measuring the performance of the predictive methods. 1
Duan, Hai-Bin; Xu, Chun-Fang; Xing, Zhi-Hui
2010-02-01
In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.
HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN
While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...
Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm
Svečko, Rajko
2014-01-01
This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749
Cystic Lung Diseases: Algorithmic Approach.
Raoof, Suhail; Bondalapati, Praveen; Vydyula, Ravikanth; Ryu, Jay H; Gupta, Nishant; Raoof, Sabiha; Galvin, Jeff; Rosen, Mark J; Lynch, David; Travis, William; Mehta, Sanjeev; Lazzaro, Richard; Naidich, David
2016-10-01
Cysts are commonly seen on CT scans of the lungs, and diagnosis can be challenging. Clinical and radiographic features combined with a multidisciplinary approach may help differentiate among various disease entities, allowing correct diagnosis. It is important to distinguish cysts from cavities because they each have distinct etiologies and associated clinical disorders. Conditions such as emphysema, and cystic bronchiectasis may also mimic cystic disease. A simplified classification of cysts is proposed. Cysts can occur in greater profusion in the subpleural areas, when they typically represent paraseptal emphysema, bullae, or honeycombing. Cysts that are present in the lung parenchyma but away from subpleural areas may be present without any other abnormalities on high-resolution CT scans. These are further categorized into solitary or multifocal/diffuse cysts. Solitary cysts may be incidentally discovered and may be an age related phenomenon or may be a remnant of prior trauma or infection. Multifocal/diffuse cysts can occur with lymphoid interstitial pneumonia, Birt-Hogg-Dubé syndrome, tracheobronchial papillomatosis, or primary and metastatic cancers. Multifocal/diffuse cysts may be associated with nodules (lymphoid interstitial pneumonia, light-chain deposition disease, amyloidosis, and Langerhans cell histiocytosis) or with ground-glass opacities (Pneumocystis jirovecii pneumonia and desquamative interstitial pneumonia). Using the results of the high-resolution CT scans as a starting point, and incorporating the patient's clinical history, physical examination, and laboratory findings, is likely to narrow the differential diagnosis of cystic lesions considerably.
Applying fuzzy clustering optimization algorithm to extracting traffic spatial pattern
NASA Astrophysics Data System (ADS)
Hu, Chunchun; Shi, Wenzhong; Meng, Lingkui; Liu, Min
2009-10-01
Traditional analytical methods for traffic information can't meet to need of intelligent traffic system. Mining value-add information can deal with more traffic problems. The paper exploits a new clustering optimization algorithm to extract useful spatial clustered pattern for predicting long-term traffic flow from macroscopic view. Considering the sensitivity of initial parameters and easy falling into local extreme in FCM algorithm, the new algorithm applies Particle Swarm Optimization method, which can discovery the globe optimal result, to the FCM algorithm. And the algorithm exploits the union of the clustering validity index and objective function of the FCM algorithm as the fitness function of the PSO algorithm. The experimental result indicates that it is effective and efficient. For fuzzy clustering of road traffic data, it can produce useful spatial clustered pattern. And the clustered centers represent the locations which have heavy traffic flow. Moreover, the parameters of the patterns can provide intelligent traffic system with assistant decision support.
Beyond Keyframing: An Algorithmic Approach to Animation
1991-05-01
AD-A241 337 1 IIllIlt1111111 l llrl lt iltlliii Beyond Keyframing: An Algorithmic Approach to Animation A. James Stewart James F. Cremer TR 91-1207...o’ed ! L. ,: , : ...,.,c~ (, J ale; its 91-08125 oj,7 Beyond Keyframing: An Algorithmic Approach to Animation IN:h - ,A. James Stewart ’ " xJ James F...Acknowledgements This work was supported in part by NSF grant DMC 86-17355, ONR grant N0014-86K-0281 and DARPA grant N0014-88K-0591. Support for James Stewart is
Quadruped Robot Locomotion using a Global Optimization Stochastic Algorithm
NASA Astrophysics Data System (ADS)
Oliveira, Miguel; Santos, Cristina; Costa, Lino; Ferreira, Manuel
2011-09-01
The problem of tuning nonlinear dynamical systems parameters, such that the attained results are considered good ones, is a relevant one. This article describes the development of a gait optimization system that allows a fast but stable robot quadruped crawl gait. We combine bio-inspired Central Patterns Generators (CPGs) and Genetic Algorithms (GA). CPGs are modelled as autonomous differential equations, that generate the necessar y limb movement to perform the required walking gait. The GA finds parameterizations of the CPGs parameters which attain good gaits in terms of speed, vibration and stability. Moreover, two constraint handling techniques based on tournament selection and repairing mechanism are embedded in the GA to solve the proposed constrained optimization problem and make the search more efficient. The experimental results, performed on a simulated Aibo robot, demonstrate that our approach allows low vibration with a high velocity and wide stability margin for a quadruped slow crawl gait.
NASA Astrophysics Data System (ADS)
Iwan Solihin, Mahmud; Fauzi Zanil, Mohd
2016-11-01
Cuckoo Search (CS) and Differential Evolution (DE) algorithms are considerably robust meta-heuristic algorithms to solve constrained optimization problems. In this study, the performance of CS and DE are compared in solving the constrained optimization problem from selected benchmark functions. Selection of the benchmark functions are based on active or inactive constraints and dimensionality of variables (i.e. number of solution variable). In addition, a specific constraint handling and stopping criterion technique are adopted in the optimization algorithm. The results show, CS approach outperforms DE in term of repeatability and the quality of the optimum solutions.
Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem
Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi
2013-01-01
Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem. PMID:23935429
Genetic-Algorithm Tool For Search And Optimization
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven
1995-01-01
SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.
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. PMID:23935416
NASA Astrophysics Data System (ADS)
Milic, Vladimir; Kasac, Josip; Novakovic, Branko
2015-10-01
This paper is concerned with ?-gain optimisation of input-affine nonlinear systems controlled by analytic fuzzy logic system. Unlike the conventional fuzzy-based strategies, the non-conventional analytic fuzzy control method does not require an explicit fuzzy rule base. As the first contribution of this paper, we prove, by using the Stone-Weierstrass theorem, that the proposed fuzzy system without rule base is universal approximator. The second contribution of this paper is an algorithm for solving a finite-horizon minimax problem for ?-gain optimisation. The proposed algorithm consists of recursive chain rule for first- and second-order derivatives, Newton's method, multi-step Adams method and automatic differentiation. Finally, the results of this paper are evaluated on a second-order nonlinear system.
Elyasigomari, V; Lee, D A; Screen, H R C; Shaheed, M H
2017-03-01
For each cancer type, only a few genes are informative. Due to the so-called 'curse of dimensionality' problem, the gene selection task remains a challenge. To overcome this problem, we propose a two-stage gene selection method called MRMR-COA-HS. In the first stage, the minimum redundancy and maximum relevance (MRMR) feature selection is used to select a subset of relevant genes. The selected genes are then fed into a wrapper setup that combines a new algorithm, COA-HS, using the support vector machine as a classifier. The method was applied to four microarray datasets, and the performance was assessed by the leave one out cross-validation method. Comparative performance assessment of the proposed method with other evolutionary algorithms suggested that the proposed algorithm significantly outperforms other methods in selecting a fewer number of genes while maintaining the highest classification accuracy. The functions of the selected genes were further investigated, and it was confirmed that the selected genes are biologically relevant to each cancer type.
Iterative phase retrieval algorithms. I: optimization.
Guo, Changliang; Liu, Shi; Sheridan, John T
2015-05-20
Two modified Gerchberg-Saxton (GS) iterative phase retrieval algorithms are proposed. The first we refer to as the spatial phase perturbation GS algorithm (SPP GSA). The second is a combined GS hybrid input-output algorithm (GS/HIOA). In this paper (Part I), it is demonstrated that the SPP GS and GS/HIO algorithms are both much better at avoiding stagnation during phase retrieval, allowing them to successfully locate superior solutions compared with either the GS or the HIO algorithms. The performances of the SPP GS and GS/HIO algorithms are also compared. Then, the error reduction (ER) algorithm is combined with the HIO algorithm (ER/HIOA) to retrieve the input object image and the phase, given only some knowledge of its extent and the amplitude in the Fourier domain. In Part II, the algorithms developed here are applied to carry out known plaintext and ciphertext attacks on amplitude encoding and phase encoding double random phase encryption systems. Significantly, ER/HIOA is then used to carry out a ciphertext-only attack on AE DRPE systems.
A Danger-Theory-Based Immune Network Optimization Algorithm
Li, Tao; Xiao, Xin; Shi, Yuanquan
2013-01-01
Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet. The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas around danger signals are called danger zones. By defining the danger zone to calculate danger signals for each antibody, the algorithm adjusts antibodies' concentrations through its own danger signals and then triggers immune responses of self-regulation. So the population diversity can be maintained. Experimental results show that the algorithm has more advantages in the solution quality and diversity of the population. Compared with influential optimization algorithms, CLONALG, opt-aiNet, and dopt-aiNet, the algorithm has smaller error values and higher success rates and can find solutions to meet the accuracies within the specified function evaluation times. PMID:23483853
Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models.
Yuan, Gonglin; Duan, Xiabin; Liu, Wenjie; Wang, Xiaoliang; Cui, Zengru; Sheng, Zhou
2015-01-01
Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1) βk ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations.
An Adaptive Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-11-03
In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.
Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models
Yuan, Gonglin; Duan, Xiabin; Liu, Wenjie; Wang, Xiaoliang; Cui, Zengru; Sheng, Zhou
2015-01-01
Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1)βk ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations. PMID:26502409
Evaluation of a particle swarm algorithm for biomechanical optimization.
Schutte, Jaco F; Koh, Byung-Il; Reinbolt, Jeffrey A; Haftka, Raphael T; George, Alan D; Fregly, Benjamin J
2005-06-01
Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recently-developed version of the particle swarm optimization (PSO) algorithm to address these problems. The algorithm's global search capabilities were investigated using a suite of difficult analytical test problems, while its scale-independent nature was proven mathematically and verified using a biomechanical test problem. For comparison, all test problems were also solved with three off-the-shelf optimization algorithms--a global genetic algorithm (GA) and multistart gradient-based sequential quadratic programming (SQP) and quasi-Newton (BFGS) algorithms. For the analytical test problems, only the PSO algorithm was successful on the majority of the problems. When compared to previously published results for the same problems, PSO was more robust than a global simulated annealing algorithm but less robust than a different, more complex genetic algorithm. For the biomechanical test problem, only the PSO algorithm was insensitive to design variable scaling, with the GA algorithm being mildly sensitive and the SQP and BFGS algorithms being highly sensitive. The proposed PSO algorithm provides a new off-the-shelf global optimization option for difficult biomechanical problems, especially those utilizing design variables with different length scales or units.
A hybrid artificial bee colony algorithm for numerical function optimization
NASA Astrophysics Data System (ADS)
Alqattan, Zakaria N.; Abdullah, Rosni
2015-02-01
Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).
A parallel Jacobson-Oksman optimization algorithm. [parallel processing (computers)
NASA Technical Reports Server (NTRS)
Straeter, T. A.; Markos, A. T.
1975-01-01
A gradient-dependent optimization technique which exploits the vector-streaming or parallel-computing capabilities of some modern computers is presented. The algorithm, derived by assuming that the function to be minimized is homogeneous, is a modification of the Jacobson-Oksman serial minimization method. In addition to describing the algorithm, conditions insuring the convergence of the iterates of the algorithm and the results of numerical experiments on a group of sample test functions are presented. The results of these experiments indicate that this algorithm will solve optimization problems in less computing time than conventional serial methods on machines having vector-streaming or parallel-computing capabilities.
DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach
NASA Astrophysics Data System (ADS)
Tchagang, Alain B.; Tewfik, Ahmed H.
2006-12-01
Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneous row-column clustering. Biclustering problems arise in DNA microarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so forth. When dealing with DNA microarray experimental data for example, the goal of biclustering algorithms is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this study, we develop novel biclustering algorithms using basic linear algebra and arithmetic tools. The proposed biclustering algorithms can be used to search for all biclusters with constant values, biclusters with constant values on rows, biclusters with constant values on columns, and biclusters with coherent values from a set of data in a timely manner and without solving any optimization problem. We also show how one of the proposed biclustering algorithms can be adapted to identify biclusters with coherent evolution. The algorithms developed in this study discover all valid biclusters of each type, while almost all previous biclustering approaches will miss some.
Genetic algorithms - What fitness scaling is optimal?
NASA Technical Reports Server (NTRS)
Kreinovich, Vladik; Quintana, Chris; Fuentes, Olac
1993-01-01
A problem of choosing the best scaling function as a mathematical optimization problem is formulated and solved under different optimality criteria. A list of functions which are optimal under different criteria is presented which includes both the best functions empirically proved and new functions that may be worth trying.
Computational Approaches to Simulation and Optimization of Global Aircraft Trajectories
NASA Technical Reports Server (NTRS)
Ng, Hok Kwan; Sridhar, Banavar
2016-01-01
This study examines three possible approaches to improving the speed in generating wind-optimal routes for air traffic at the national or global level. They are: (a) using the resources of a supercomputer, (b) running the computations on multiple commercially available computers and (c) implementing those same algorithms into NASAs Future ATM Concepts Evaluation Tool (FACET) and compares those to a standard implementation run on a single CPU. Wind-optimal aircraft trajectories are computed using global air traffic schedules. The run time and wait time on the supercomputer for trajectory optimization using various numbers of CPUs ranging from 80 to 10,240 units are compared with the total computational time for running the same computation on a single desktop computer and on multiple commercially available computers for potential computational enhancement through parallel processing on the computer clusters. This study also re-implements the trajectory optimization algorithm for further reduction of computational time through algorithm modifications and integrates that with FACET to facilitate the use of the new features which calculate time-optimal routes between worldwide airport pairs in a wind field for use with existing FACET applications. The implementations of trajectory optimization algorithms use MATLAB, Python, and Java programming languages. The performance evaluations are done by comparing their computational efficiencies and based on the potential application of optimized trajectories. The paper shows that in the absence of special privileges on a supercomputer, a cluster of commercially available computers provides a feasible approach for national and global air traffic system studies.
Efficiency Improvements in Meta-Heuristic Algorithms to Solve the Optimal Power Flow Problem
NASA Astrophysics Data System (ADS)
Reddy, S. Surender; Bijwe, P. R.
2016-12-01
This paper proposes the efficient approaches for solving the Optimal Power Flow (OPF) problem using the meta-heuristic algorithms. Mathematically, OPF is formulated as non-linear equality and inequality constrained optimization problem. The main drawback of meta-heuristic algorithm based OPF is the excessive execution time required due to the large number of power flows needed in the solution process. The proposed efficient approaches uses the lower and upper bounds of objective function values. By using this approach, the number of power flows to be performed are reduced substantially, resulting in the solution speed up. The efficiently generated objective function bounds can result in the faster solutions of meta-heuristic algorithms. The original advantages of meta-heuristic algorithms, such as ability to handle complex non-linearities, discontinuities in the objective function, discrete variables handling, and multi-objective optimization, etc., are still available in the proposed efficient approaches. The proposed OPF formulation includes the active and reactive power generation limits, Valve Point Loading (VPL) and Prohibited Operating Zones (POZs) effects of generating units. The effectiveness of proposed approach is examined on IEEE 30, 118 and 300 bus test systems, and the simulation results confirm the efficiency and superiority of the proposed approaches over the other meta-heuristic algorithms. The proposed efficient approach is generic enough to use with any type of meta-heuristic algorithm based OPF.
Yang, Jie; Zhang, Pengcheng; Zhang, Liyuan; Shu, Huazhong; Li, Baosheng; Gui, Zhiguo
2017-01-01
In inverse treatment planning of intensity-modulated radiation therapy (IMRT), the objective function is typically the sum of the weighted sub-scores, where the weights indicate the importance of the sub-scores. To obtain a high-quality treatment plan, the planner manually adjusts the objective weights using a trial-and-error procedure until an acceptable plan is reached. In this work, a new particle swarm optimization (PSO) method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment, thereby reducing the workload of the human planner and contributing to the development of a fully automated planning process. The proposed optimization method consists of three steps. (i) First, a swarm of weighting factors (i.e., particles) is initialized randomly in the search space, where each particle corresponds to a global objective function. (ii) Then, a plan optimization solver is employed to obtain the optimal solution for each particle, and the values of the evaluation functions used to determine the particle's location and the population global location for the PSO are calculated based on these results. (iii) Next, the weighting factors are updated based on the particle's location and the population global location. Step (ii) is performed alternately with step (iii) until the termination condition is reached. In this method, the evaluation function is a combination of several key points on the dose volume histograms. Furthermore, a perturbation strategy - the crossover and mutation operator hybrid approach - is employed to enhance the population diversity, and two arguments are applied to the evaluation function to improve the flexibility of the algorithm. In this study, the proposed method was used to develop IMRT treatment plans involving five unequally spaced 6MV photon beams for 10 prostate cancer cases. The proposed optimization algorithm yielded high-quality plans for all of the cases, without human
NASA Astrophysics Data System (ADS)
Morshed, Mohammad Sarwar; Kamal, Mostafa Mashnoon; Khan, Somaiya Islam
2016-07-01
Inventory has been a major concern in supply chain and numerous researches have been done lately on inventory control which brought forth a number of methods that efficiently manage inventory and related overheads by reducing cost of replenishment. This research is aimed towards providing a better replenishment policy in case of multi-product, single supplier situations for chemical raw materials of textile industries in Bangladesh. It is assumed that industries currently pursue individual replenishment system. The purpose is to find out the optimum ideal cycle time and individual replenishment cycle time of each product for replenishment that will cause lowest annual holding and ordering cost, and also find the optimum ordering quantity. In this paper indirect grouping strategy has been used. It is suggested that indirect grouping Strategy outperforms direct grouping strategy when major cost is high. An algorithm by Kaspi and Rosenblatt (1991) called RAND is exercised for its simplicity and ease of application. RAND provides an ideal cycle time (T) for replenishment and integer multiplier (ki) for individual items. Thus the replenishment cycle time for each product is found as T×ki. Firstly, based on data, a comparison between currently prevailing (individual) process and RAND is provided that uses the actual demands which presents 49% improvement in total cost of replenishment. Secondly, discrepancies in demand is corrected by using Holt's method. However, demands can only be forecasted one or two months into the future because of the demand pattern of the industry under consideration. Evidently, application of RAND with corrected demand display even greater improvement. The results of this study demonstrates that cost of replenishment can be significantly reduced by applying RAND algorithm and exponential smoothing models.
GRAVITATIONAL LENS MODELING WITH GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZERS
Rogers, Adam; Fiege, Jason D.
2011-02-01
Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point-spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our 'matrix-free' approach avoids construction of the lens and blurring operators while retaining the least-squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automatically, which represents the trade-off between the image {chi}{sup 2} and regularization effects, and allows an estimate of the optimally regularized solution for each lens parameter set. In the final step of the optimization procedure, the lens model with the lowest {chi}{sup 2} is used while the global optimizer solves for the source intensity distribution directly. This allows us to accurately determine the number of degrees of freedom in the problem to facilitate comparison between lens models and enforce positivity on the source profile. In practice, we find that the GA conducts a more thorough search of the parameter space than the PSO.
NASA Astrophysics Data System (ADS)
Panda, S.; Mishra, D.; Biswal, B. B.; Tripathy, M.
2014-02-01
Robotic manipulators with three-revolute (3R) motions to attain desired positional configurations are very common in industrial robots. The capability of these robots depends largely on the workspace of the manipulator in addition to other parameters. In this study, an evolutionary optimization algorithm based on the foraging behaviour of the Escherichia coli bacteria present in the human intestine is utilized to optimize the workspace volume of a 3R manipulator. The new optimization method is modified from the original algorithm for faster convergence. This method is also useful for optimization problems in a highly constrained environment, such as robot workspace optimization. The new approach for workspace optimization of 3R manipulators is tested using three cases. The test results are compared with standard results available using other optimization algorithms, i.e. the differential evolution algorithm, the genetic algorithm and the particle swarm optimization algorithm. The present method is found to be superior to the other methods in terms of computational efficiency.
A Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-06-24
Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.
Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.
Rani, R Ranjani; Ramyachitra, D
2016-12-01
Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods.
A parallel variable metric optimization algorithm
NASA Technical Reports Server (NTRS)
Straeter, T. A.
1973-01-01
An algorithm, designed to exploit the parallel computing or vector streaming (pipeline) capabilities of computers is presented. When p is the degree of parallelism, then one cycle of the parallel variable metric algorithm is defined as follows: first, the function and its gradient are computed in parallel at p different values of the independent variable; then the metric is modified by p rank-one corrections; and finally, a single univariant minimization is carried out in the Newton-like direction. Several properties of this algorithm are established. The convergence of the iterates to the solution is proved for a quadratic functional on a real separable Hilbert space. For a finite-dimensional space the convergence is in one cycle when p equals the dimension of the space. Results of numerical experiments indicate that the new algorithm will exploit parallel or pipeline computing capabilities to effect faster convergence than serial techniques.
Kidney-inspired algorithm for optimization problems
NASA Astrophysics Data System (ADS)
Jaddi, Najmeh Sadat; Alvankarian, Jafar; Abdullah, Salwani
2017-01-01
In this paper, a population-based algorithm inspired by the kidney process in the human body is proposed. In this algorithm the solutions are filtered in a rate that is calculated based on the mean of objective functions of all solutions in the current population of each iteration. The filtered solutions as the better solutions are moved to filtered blood and the rest are transferred to waste representing the worse solutions. This is a simulation of the glomerular filtration process in the kidney. The waste solutions are reconsidered in the iterations if after applying a defined movement operator they satisfy the filtration rate, otherwise it is expelled from the waste solutions, simulating the reabsorption and excretion functions of the kidney. In addition, a solution assigned as better solution is secreted if it is not better than the worst solutions simulating the secreting process of blood in the kidney. After placement of all the solutions in the population, the best of them is ranked, the waste and filtered blood are merged to become a new population and the filtration rate is updated. Filtration provides the required exploitation while generating a new solution and reabsorption gives the necessary exploration for the algorithm. The algorithm is assessed by applying it on eight well-known benchmark test functions and compares the results with other algorithms in the literature. The performance of the proposed algorithm is better on seven out of eight test functions when it is compared with the most recent researches in literature. The proposed kidney-inspired algorithm is able to find the global optimum with less function evaluations on six out of eight test functions. A statistical analysis further confirms the ability of this algorithm to produce good-quality results.
Fast-convergence superpixel algorithm via an approximate optimization
NASA Astrophysics Data System (ADS)
Nakamura, Kensuke; Hong, Byung-Woo
2016-09-01
We propose an optimization scheme that achieves fast yet accurate computation of superpixels from an image. Our optimization is designed to improve the efficiency and robustness for the minimization of a composite energy functional in the expectation-minimization (EM) framework where we restrict the update of an estimate to avoid redundant computations. We consider a superpixel energy formulation that consists of L2-norm for the spatial regularity and L1-norm for the data fidelity in the demonstration of the robustness of the proposed algorithm. The quantitative and qualitative evaluations indicate that our superpixel algorithm outperforms SLIC and SEEDS algorithms. It is also demonstrated that our algorithm guarantees the convergence with less computational cost by up to 89% on average compared to the SLIC algorithm while preserving the accuracy. Our optimization scheme can be easily extended to other applications in which the alternating minimization is applicable in the EM framework.
Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem
NASA Astrophysics Data System (ADS)
Chen, Wei
2015-07-01
In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.
Path Optimization for Single and Multiple Searchers: Models and Algorithms
2008-09-01
the k-th it- eration of Algorithm 11, the master problem MP4 (k) defined below is solved. The optimal value and optimal solution of MP4 (k) are denoted z...k) and y(k), respectively. In each iteration of Algorithm 11, U cuts are generated at once. Formulation of Master problem : MP4 (k) min z = ∑U u=1...master problem MP4 (k), and obtain its optimal value z(k) and optimal solution y(k). If z(k) > q, then q = z(k). Step 3. Calculate fu(y (k)) and fu(y (k
An algorithm for the systematic disturbance of optimal rotational solutions
NASA Technical Reports Server (NTRS)
Grunwald, Arthur J.; Kaiser, Mary K.
1989-01-01
An algorithm for introducing a systematic rotational disturbance into an optimal (i.e., single axis) rotational trajectory is described. This disturbance introduces a motion vector orthogonal to the quaternion-defined optimal rotation axis. By altering the magnitude of this vector, the degree of non-optimality can be controlled. The metric properties of the distortion parameter are described, with analogies to two-dimensional translational motion. This algorithm was implemented in a motion-control program on a three-dimensional graphic workstation. It supports a series of human performance studies on the detectability of rotational trajectory optimality by naive observers.
NASA Astrophysics Data System (ADS)
Salcedo-Sanz, S.; Camacho-Gómez, C.; Magdaleno, A.; Pereira, E.; Lorenzana, A.
2017-04-01
In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures within a single population of solutions. The proposed approach is able to solve the TMD design and location problem, by exploiting the combination of different types of searching mechanisms. This promotes a powerful evolutionary-like algorithm for optimization problems, which is shown to be very effective in this particular problem of TMDs tuning. The proposed algorithm's performance has been evaluated and compared with several reference algorithms in two building models with two and four floors, respectively.
A homotopy algorithm for digital optimal projection control GASD-HADOC
NASA Technical Reports Server (NTRS)
Collins, Emmanuel G., Jr.; Richter, Stephen; Davis, Lawrence D.
1993-01-01
The linear-quadratic-gaussian (LQG) compensator was developed to facilitate the design of control laws for multi-input, multi-output (MIMO) systems. The compensator is computed by solving two algebraic equations for which standard closed-loop solutions exist. Unfortunately, the minimal dimension of an LQG compensator is almost always equal to the dimension of the plant and can thus often violate practical implementation constraints on controller order. This deficiency is especially highlighted when considering control-design for high-order systems such as flexible space structures. This deficiency motivated the development of techniques that enable the design of optimal controllers whose dimension is less than that of the design plant. A homotopy approach based on the optimal projection equations that characterize the necessary conditions for optimal reduced-order control. Homotopy algorithms have global convergence properties and hence do not require that the initializing reduced-order controller be close to the optimal reduced-order controller to guarantee convergence. However, the homotopy algorithm previously developed for solving the optimal projection equations has sublinear convergence properties and the convergence slows at higher authority levels and may fail. A new homotopy algorithm for synthesizing optimal reduced-order controllers for discrete-time systems is described. Unlike the previous homotopy approach, the new algorithm is a gradient-based, parameter optimization formulation and was implemented in MATLAB. The results reported may offer the foundation for a reliable approach to optimal, reduced-order controller design.
Salcedo-Sanz, S; Del Ser, J; Landa-Torres, I; Gil-López, S; Portilla-Figueras, J A
2014-01-01
This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems.
Salcedo-Sanz, S.; Del Ser, J.; Landa-Torres, I.; Gil-López, S.; Portilla-Figueras, J. A.
2014-01-01
This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. PMID:25147860
Research on particle swarm optimization algorithm based on optimal movement probability
NASA Astrophysics Data System (ADS)
Ma, Jianhong; Zhang, Han; He, Baofeng
2017-01-01
The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.
PCB Drill Path Optimization by Combinatorial Cuckoo Search Algorithm
Lim, Wei Chen Esmonde; Kanagaraj, G.; Ponnambalam, S. G.
2014-01-01
Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process. PMID:24707198
PCB drill path optimization by combinatorial cuckoo search algorithm.
Lim, Wei Chen Esmonde; Kanagaraj, G; Ponnambalam, S G
2014-01-01
Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process.
An evolutionary algorithm for global optimization based on self-organizing maps
NASA Astrophysics Data System (ADS)
Barmada, Sami; Raugi, Marco; Tucci, Mauro
2016-10-01
In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.
Numerical optimization algorithm for rotationally invariant multi-orbital slave-boson method
NASA Astrophysics Data System (ADS)
Quan, Ya-Min; Wang, Qing-wei; Liu, Da-Yong; Yu, Xiang-Long; Zou, Liang-Jian
2015-06-01
We develop a generalized numerical optimization algorithm for the rotationally invariant multi-orbital slave boson approach, which is applicable for arbitrary boundary constraints of high-dimensional objective function by combining several classical optimization techniques. After constructing the calculation architecture of rotationally invariant multi-orbital slave boson model, we apply this optimization algorithm to find the stable ground state and magnetic configuration of two-orbital Hubbard models. The numerical results are consistent with available solutions, confirming the correctness and accuracy of our present algorithm. Furthermore, we utilize it to explore the effects of the transverse Hund's coupling terms on metal-insulator transition, orbital selective Mott phase and magnetism. These results show the quick convergency and robust stable character of our algorithm in searching the optimized solution of strongly correlated electron systems.
Shape Optimization of Cochlear Implant Electrode Array Using Genetic Algorithms
2007-11-02
Shape Optimization of Cochlear Implant Electrode Array using Genetic Algorithms Charles T.M. Choi, Ph.D., senior member, IEEE Department of...c.t.choi@ieee.org Abstract−Finite element analysis is used to compute the current distribution of the human cochlea during cochlear implant electrical...stimulation. Genetic algorithms are then applied in conjunction with the finite element analysis to optimize the shape of cochlear implant electrode array
Matott, L Shawn; Bartelt-Hunt, Shannon L; Rabideau, Alan J; Fowler, K R
2006-10-15
Although heuristic optimization techniques are increasingly applied in environmental engineering applications, algorithm selection and configuration are often approached in an ad hoc fashion. In this study, the design of a multilayer sorptive barrier system served as a benchmark problem for evaluating several algorithm-tuning procedures, as applied to three global optimization techniques (genetic algorithms, simulated annealing, and particle swarm optimization). Each design problem was configured as a combinatorial optimization in which sorptive materials were selected for inclusion in a landfill liner to minimize the transport of three common organic contaminants. Relative to multilayer sorptive barrier design, study results indicate (i) the binary-coded genetic algorithm is highly efficient and requires minimal tuning, (ii) constraint violations must be carefully integrated to avoid poor algorithm convergence, and (iii) search algorithm performance is strongly influenced by the physical-chemical properties of the organic contaminants of concern. More generally, the results suggest that formal algorithm tuning, which has not been widely applied to environmental engineering optimization, can significantly improve algorithm performance and provide insight into the physical processes that control environmental systems.
Motion Cueing Algorithm Development: Human-Centered Linear and Nonlinear Approaches
NASA Technical Reports Server (NTRS)
Houck, Jacob A. (Technical Monitor); Telban, Robert J.; Cardullo, Frank M.
2005-01-01
While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. Prior research identified viable features from two algorithms: the nonlinear "adaptive algorithm", and the "optimal algorithm" that incorporates human vestibular models. A novel approach to motion cueing, the "nonlinear algorithm" is introduced that combines features from both approaches. This algorithm is formulated by optimal control, and incorporates a new integrated perception model that includes both visual and vestibular sensation and the interaction between the stimuli. Using a time-varying control law, the matrix Riccati equation is updated in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. The neurocomputing approach was crucial in that the number of presentations of an input vector could be reduced to meet the real time requirement without degrading the quality of the motion cues.
Advanced optimization of permanent magnet wigglers using a genetic algorithm
Hajima, Ryoichi
1995-12-31
In permanent magnet wigglers, magnetic imperfection of each magnet piece causes field error. This field error can be reduced or compensated by sorting magnet pieces in proper order. We showed a genetic algorithm has good property for this sorting scheme. In this paper, this optimization scheme is applied to the case of permanent magnets which have errors in the direction of field. The result shows the genetic algorithm is superior to other algorithms.
Differential evolution algorithm for global optimizations in nuclear physics
NASA Astrophysics Data System (ADS)
Qi, Chong
2017-04-01
We explore the applicability of the differential evolution algorithm in finding the global minima of three typical nuclear structure physics problems: the global deformation minimum in the nuclear potential energy surface, the optimization of mass model parameters and the lowest eigenvalue of a nuclear Hamiltonian. The algorithm works very effectively and efficiently in identifying the minima in all problems we have tested. We also show that the algorithm can be parallelized in a straightforward way.
Hybrid particle swarm global optimization algorithm for phase diversity phase retrieval.
Zhang, P G; Yang, C L; Xu, Z H; Cao, Z L; Mu, Q Q; Xuan, L
2016-10-31
The core problem of phase diversity phase retrieval (PDPR) is to find suitable optimization algorithms for wave-front sensing of different scales, especially for large-scale wavefront sensing. When dealing with large-scale wave-front sensing, existing gradient-based local optimization algorithms used in PDPR are easily trapped in local minimums near initial positions, and available global optimization algorithms possess low convergence efficiency. We construct a practicable optimization algorithm used in PDPR for large-scale wave-front sensing. This algorithm, named EPSO-BFGS, is a two-step hybrid global optimization algorithm based on the combination of evolutionary particle swarm optimization (EPSO) and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Firstly, EPSO provides global search and obtains a rough global minimum position in limited search steps. Then, BFGS initialized by the rough global minimum position approaches the global minimum with high accuracy and fast convergence speed. Numerical examples testify to the feasibility and reliability of EPSO-BFGS for wave-front sensing of different scales. Two numerical cases also validate the ability of EPSO-BFGS for large-scale wave-front sensing. The effectiveness of EPSO-BFGS is further affirmed by performing a verification experiment.
Parallel optimization algorithms and their implementation in VLSI design
NASA Technical Reports Server (NTRS)
Lee, G.; Feeley, J. J.
1991-01-01
Two new parallel optimization algorithms based on the simplex method are described. They may be executed by a SIMD parallel processor architecture and be implemented in VLSI design. Several VLSI design implementations are introduced. An application example is reported to demonstrate that the algorithms are effective.
Relaxed controls and the convergence of optimal control algorithms
NASA Technical Reports Server (NTRS)
Williamson, L. J.; Polak, E.
1976-01-01
This paper presents a framework for the study of the convergence properties of optimal control algorithms and illustrates its use by means of two examples. The framework consists of an algorithm prototype with a convergence theorem, together with some results in relaxed controls theory.
Optimization of heterogeneous Bin packing using adaptive genetic algorithm
NASA Astrophysics Data System (ADS)
Sridhar, R.; Chandrasekaran, M.; Sriramya, C.; Page, Tom
2017-03-01
This research is concentrates on a very interesting work, the bin packing using hybrid genetic approach. The optimal and feasible packing of goods for transportation and distribution to various locations by satisfying the practical constraints are the key points in this project work. As the number of boxes for packing can not be predicted in advance and the boxes may not be of same category always. It also involves many practical constraints that are why the optimal packing makes much importance to the industries. This work presents a combinational of heuristic Genetic Algorithm (HGA) for solving Three Dimensional (3D) Single container arbitrary sized rectangular prismatic bin packing optimization problem by considering most of the practical constraints facing in logistic industries. This goal was achieved in this research by optimizing the empty volume inside the container using genetic approach. Feasible packing pattern was achieved by satisfying various practical constraints like box orientation, stack priority, container stability, weight constraint, overlapping constraint, shipment placement constraint. 3D bin packing problem consists of ‘n’ number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and in-turn profit. Furthermore, Boxes to be packed may be of arbitrary sizes. The user input data are the number of bins, its size, shape, weight, and constraints if any along with standard container dimension. This user input were stored in the database and encoded to string (chromosomes) format which were normally acceptable by GA. GA operators were allowed to act over these encoded strings for finding the best solution.
Genetic algorithm for neural networks optimization
NASA Astrophysics Data System (ADS)
Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta
2004-11-01
This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.
Optimization of composite structures by estimation of distribution algorithms
NASA Astrophysics Data System (ADS)
Grosset, Laurent
The design of high performance composite laminates, such as those used in aerospace structures, leads to complex combinatorial optimization problems that cannot be addressed by conventional methods. These problems are typically solved by stochastic algorithms, such as evolutionary algorithms. This dissertation proposes a new evolutionary algorithm for composite laminate optimization, named Double-Distribution Optimization Algorithm (DDOA). DDOA belongs to the family of estimation of distributions algorithms (EDA) that build a statistical model of promising regions of the design space based on sets of good points, and use it to guide the search. A generic framework for introducing statistical variable dependencies by making use of the physics of the problem is proposed. The algorithm uses two distributions simultaneously: the marginal distributions of the design variables, complemented by the distribution of auxiliary variables. The combination of the two generates complex distributions at a low computational cost. The dissertation demonstrates the efficiency of DDOA for several laminate optimization problems where the design variables are the fiber angles and the auxiliary variables are the lamination parameters. The results show that its reliability in finding the optima is greater than that of a simple EDA and of a standard genetic algorithm, and that its advantage increases with the problem dimension. A continuous version of the algorithm is presented and applied to a constrained quadratic problem. Finally, a modification of the algorithm incorporating probabilistic and directional search mechanisms is proposed. The algorithm exhibits a faster convergence to the optimum and opens the way for a unified framework for stochastic and directional optimization.
Global search algorithm for optimal control
NASA Technical Reports Server (NTRS)
Brocker, D. H.; Kavanaugh, W. P.; Stewart, E. C.
1970-01-01
Random-search algorithm employs local and global properties to solve two-point boundary value problem in Pontryagin maximum principle for either fixed or variable end-time problems. Mixed boundary value problem is transformed to an initial value problem. Mapping between initial and terminal values utilizes hybrid computer.
Optimization of deep learning algorithms for object classification
NASA Astrophysics Data System (ADS)
Horváth, András.
2017-02-01
Deep learning is currently the state of the art algorithm for image classification. The complexity of these feedforward neural networks have overcome a critical point, resulting algorithmic breakthroughs in various fields. On the other hand their complexity makes them executable in tasks, where High-throughput computing powers are available. The optimization of these networks -considering computational complexity and applicability on embedded systems- has not yet been studied and investigated in details. In this paper I show some examples how this algorithms can be optimized and accelerated on embedded systems.
Adaptive Wing Camber Optimization: A Periodic Perturbation Approach
NASA Technical Reports Server (NTRS)
Espana, Martin; Gilyard, Glenn
1994-01-01
Available redundancy among aircraft control surfaces allows for effective wing camber modifications. As shown in the past, this fact can be used to improve aircraft performance. To date, however, algorithm developments for in-flight camber optimization have been limited. This paper presents a perturbational approach for cruise optimization through in-flight camber adaptation. The method uses, as a performance index, an indirect measurement of the instantaneous net thrust. As such, the actual performance improvement comes from the integrated effects of airframe and engine. The algorithm, whose design and robustness properties are discussed, is demonstrated on the NASA Dryden B-720 flight simulator.
Optimal fractional order PID design via Tabu Search based algorithm.
Ateş, Abdullah; Yeroglu, Celaleddin
2016-01-01
This paper presents an optimization method based on the Tabu Search Algorithm (TSA) to design a Fractional-Order Proportional-Integral-Derivative (FOPID) controller. All parameter computations of the FOPID employ random initial conditions, using the proposed optimization method. Illustrative examples demonstrate the performance of the proposed FOPID controller design method.
Model Specification Searches Using Ant Colony Optimization Algorithms
ERIC Educational Resources Information Center
Marcoulides, George A.; Drezner, Zvi
2003-01-01
Ant colony optimization is a recently proposed heuristic procedure inspired by the behavior of real ants. This article applies the procedure to model specification searches in structural equation modeling and reports the results. The results demonstrate the capabilities of ant colony optimization algorithms for conducting automated searches.
An Enhanced Memetic Algorithm for Single-Objective Bilevel Optimization Problems.
Islam, Md Monjurul; Singh, Hemant Kumar; Ray, Tapabrata; Sinha, Ankur
2016-11-07
Bilevel optimization, as the name reflects, deals with optimization at two interconnected hierarchical levels. The aim is to identify the optimum of an upper-level leader problem, subject to the optimality of a lower-level follower problem. Several problems from the domain of engineering, logistics, economics, and transportation have an inherent nested structure which requires them to be modeled as bilevel optimization problems. Increasing size and complexity of such problems has prompted active theoretical and practical interest in the design of efficient algorithms for bilevel optimization. Given the nested nature of bilevel problems, the computational effort (number of function evaluations) required to solve them is often quite high. In this article, we explore the use of a Memetic Algorithm (MA) to solve bilevel optimization problems. While MAs have been quite successful in solving single-level optimization problems, there have been relatively few studies exploring their potential for solving bilevel optimization problems. MAs essentially attempt to combine advantages of global and local search strategies to identify optimum solutions with low computational cost (function evaluations). The approach introduced in this article is a nested Bilevel Memetic Algorithm (BLMA). At both upper and lower levels, either a global or a local search method is used during different phases of the search. The performance of BLMA is presented on twenty-five standard test problems and two real-life applications. The results are compared with other established algorithms to demonstrate the efficacy of the proposed approach.
Moore, J H
1995-06-01
A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.
Optimal placement of tuning masses on truss structures by genetic algorithms
NASA Technical Reports Server (NTRS)
Ponslet, Eric; Haftka, Raphael T.; Cudney, Harley H.
1993-01-01
Optimal placement of tuning masses, actuators and other peripherals on large space structures is a combinatorial optimization problem. This paper surveys several techniques for solving this problem. The genetic algorithm approach to the solution of the placement problem is described in detail. An example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms. The relative efficiencies of different codings are compared using the results of a large number of optimization runs.
PCNN document segmentation method based on bacterial foraging optimization algorithm
NASA Astrophysics Data System (ADS)
Liao, Yanping; Zhang, Peng; Guo, Qiang; Wan, Jian
2014-04-01
Pulse Coupled Neural Network(PCNN) is widely used in the field of image processing, but it is a difficult task to define the relative parameters properly in the research of the applications of PCNN. So far the determination of parameters of its model needs a lot of experiments. To deal with the above problem, a document segmentation based on the improved PCNN is proposed. It uses the maximum entropy function as the fitness function of bacterial foraging optimization algorithm, adopts bacterial foraging optimization algorithm to search the optimal parameters, and eliminates the trouble of manually set the experiment parameters. Experimental results show that the proposed algorithm can effectively complete document segmentation. And result of the segmentation is better than the contrast algorithms.
A Novel Hybrid Firefly Algorithm for Global Optimization
Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao
2016-01-01
Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate. PMID:27685869
Optimization approaches to volumetric modulated arc therapy planning
Unkelbach, Jan Bortfeld, Thomas; Craft, David; Alber, Markus; Bangert, Mark; Bokrantz, Rasmus; Chen, Danny; Li, Ruijiang; Xing, Lei; Men, Chunhua; Nill, Simeon; Papp, Dávid; Romeijn, Edwin; Salari, Ehsan
2015-03-15
Volumetric modulated arc therapy (VMAT) has found widespread clinical application in recent years. A large number of treatment planning studies have evaluated the potential for VMAT for different disease sites based on the currently available commercial implementations of VMAT planning. In contrast, literature on the underlying mathematical optimization methods used in treatment planning is scarce. VMAT planning represents a challenging large scale optimization problem. In contrast to fluence map optimization in intensity-modulated radiotherapy planning for static beams, VMAT planning represents a nonconvex optimization problem. In this paper, the authors review the state-of-the-art in VMAT planning from an algorithmic perspective. Different approaches to VMAT optimization, including arc sequencing methods, extensions of direct aperture optimization, and direct optimization of leaf trajectories are reviewed. Their advantages and limitations are outlined and recommendations for improvements are discussed.
Optimization approaches to volumetric modulated arc therapy planning.
Unkelbach, Jan; Bortfeld, Thomas; Craft, David; Alber, Markus; Bangert, Mark; Bokrantz, Rasmus; Chen, Danny; Li, Ruijiang; Xing, Lei; Men, Chunhua; Nill, Simeon; Papp, Dávid; Romeijn, Edwin; Salari, Ehsan
2015-03-01
Volumetric modulated arc therapy (VMAT) has found widespread clinical application in recent years. A large number of treatment planning studies have evaluated the potential for VMAT for different disease sites based on the currently available commercial implementations of VMAT planning. In contrast, literature on the underlying mathematical optimization methods used in treatment planning is scarce. VMAT planning represents a challenging large scale optimization problem. In contrast to fluence map optimization in intensity-modulated radiotherapy planning for static beams, VMAT planning represents a nonconvex optimization problem. In this paper, the authors review the state-of-the-art in VMAT planning from an algorithmic perspective. Different approaches to VMAT optimization, including arc sequencing methods, extensions of direct aperture optimization, and direct optimization of leaf trajectories are reviewed. Their advantages and limitations are outlined and recommendations for improvements are discussed.
A Discrete Lagrangian Algorithm for Optimal Routing Problems
Kosmas, O. T.; Vlachos, D. S.; Simos, T. E.
2008-11-06
The ideas of discrete Lagrangian methods for conservative systems are exploited for the construction of algorithms applicable in optimal ship routing problems. The algorithm presented here is based on the discretisation of Hamilton's principle of stationary action Lagrangian and specifically on the direct discretization of the Lagrange-Hamilton principle for a conservative system. Since, in contrast to the differential equations, the discrete Euler-Lagrange equations serve as constrains for the optimization of a given cost functional, in the present work we utilize this feature in order to minimize the cost function for optimal ship routing.
Optimal Configuration of a Square Array Group Testing Algorithm
Hudgens, Michael G.; Kim, Hae-Young
2009-01-01
We consider the optimal configuration of a square array group testing algorithm (denoted A2) to minimize the expected number of tests per specimen. For prevalence greater than 0.2498, individual testing is shown to be more efficient than A2. For prevalence less than 0.2498, closed form lower and upper bounds on the optimal group sizes for A2 are given. Arrays of dimension 2 × 2, 3 × 3, and 4 × 4 are shown to never be optimal. The results are illustrated by considering the design of a specimen pooling algorithm for detection of recent HIV infections in Malawi. PMID:21218195
Continuous Firefly Algorithm for Optimal Tuning of Pid Controller in Avr System
NASA Astrophysics Data System (ADS)
Bendjeghaba, Omar
2014-01-01
This paper presents a tuning approach based on Continuous firefly algorithm (CFA) to obtain the proportional-integral- derivative (PID) controller parameters in Automatic Voltage Regulator system (AVR). In the tuning processes the CFA is iterated to reach the optimal or the near optimal of PID controller parameters when the main goal is to improve the AVR step response characteristics. Conducted simulations show the effectiveness and the efficiency of the proposed approach. Furthermore the proposed approach can improve the dynamic of the AVR system. Compared with particle swarm optimization (PSO), the new CFA tuning method has better control system performance in terms of time domain specifications and set-point tracking.
OPTIMIZATION OF LONG RURAL FEEDERS USING A GENETIC ALGORITHM
Wishart, Michael; Ledwich, Gerard; Ghosh, Arindam; Ivanovich, Grujica
2010-06-15
This paper describes the optimization of conductor size and the voltage regulator location and magnitude of long rural distribution lines. The optimization minimizes the lifetime cost of the lines, including capital costs and losses while observing voltage drop and operational constraints using a Genetic Algorithm (GA). The GA optimization is applied to a real Single Wire Earth Return (SWER) network in regional Queensland and results are presented.
Sequential Quadratic Programming Algorithms for Optimization
1989-08-01
brief history of the evolution of SQP algorithms. Surveys for this area can be found in [GMWSl]. (Po831 or fGNISW ,] for example. The origins Ihe...0) S (TnI(P(O) K __jnfl’flj)j 2 < 0. lhe adjust uncut of thleslack variables. s in step (Ii) oft he algorith (-ii a ii only lvad to a fu rt her red
A large scale application of an optimal deterministic hydrothermal scheduling algorithm
Carneiro, A.A.F.M.; Soares, S. ); Bond, P.S. )
1990-02-01
This paper presents an application of a deterministic optimization algorithm in the hydrothermal scheduling of the large scale Brazilian south-southeast interconnected system, composed of 51 hydro and 12 thermal plants, corresponding to 45 GW of installed capacity. The application considers the system operational conditions according to the 1986 operational plan coordinated by the Brazilian electric holding company. The employed algorithm is based on a network flow approach especially developed for hydrothermal scheduling. For the south-southeast interconnected system the problem formulation suggests a primal decomposition optimization approach.
Optimal recombination in genetic algorithms for flowshop scheduling problems
NASA Astrophysics Data System (ADS)
Kovalenko, Julia
2016-10-01
The optimal recombination problem consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. We prove NP-hardness of the optimal recombination for various variants of the flowshop scheduling problem with makespan criterion and criterion of maximum lateness. An algorithm for solving the optimal recombination problem for permutation flowshop problems is built, using enumeration of prefect matchings in a special bipartite graph. The algorithm is adopted for the classical flowshop scheduling problem and for the no-wait flowshop problem. It is shown that the optimal recombination problem for the permutation flowshop scheduling problem is solvable in polynomial time for almost all pairs of parent solutions as the number of jobs tends to infinity.
A Hybrid Ant Colony Algorithm for Loading Pattern Optimization
NASA Astrophysics Data System (ADS)
Hoareau, F.
2014-06-01
Electricité de France (EDF) operates 58 nuclear power plant (NPP), of the Pressurized Water Reactor (PWR) type. The loading pattern (LP) optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R&D has developed automatic optimization tools that assist the experts. The latter can resort, for instance, to a loading pattern optimization software based on ant colony algorithm. This paper presents an analysis of the search space of a few realistic loading pattern optimization problems. This analysis leads us to introduce a hybrid algorithm based on ant colony and a local search method. We then show that this new algorithm is able to generate loading patterns of good quality.
Beyond Keyframing: An Algorithmic Approach to Animation
1989-01-09
SBeyond Keyframing: An Algorithmic Approach to Animation0 A. James Stewart DTIC James F. Cremer E.ECTE Computer Science Department JUL 141989 Cornell...grant N0014-86K-0281 and DARPA grant N0014-88K-0591. Support for James Stewart is provided in part by U.S. Army Mathematical Sciences Institute grant...and Control. Addison Wesley, 1986. [Cre89] James F. Cremer. PhD thesis, Cornell University, in preparation, 1989. [CS881 James F. Cremer and A. . James
Analytical optimal pulse shapes obtained with the aid of genetic algorithms
Guerrero, Rubén D.; Arango, Carlos A.; Reyes, Andrés
2015-09-28
We propose a methodology to design optimal pulses for achieving quantum optimal control on molecular systems. Our approach constrains pulse shapes to linear combinations of a fixed number of experimentally relevant pulse functions. Quantum optimal control is obtained by maximizing a multi-target fitness function using genetic algorithms. As a first application of the methodology, we generated an optimal pulse that successfully maximized the yield on a selected dissociation channel of a diatomic molecule. Our pulse is obtained as a linear combination of linearly chirped pulse functions. Data recorded along the evolution of the genetic algorithm contained important information regarding the interplay between radiative and diabatic processes. We performed a principal component analysis on these data to retrieve the most relevant processes along the optimal path. Our proposed methodology could be useful for performing quantum optimal control on more complex systems by employing a wider variety of pulse shape functions.
Analytical optimal pulse shapes obtained with the aid of genetic algorithms
NASA Astrophysics Data System (ADS)
Guerrero, Rubén D.; Arango, Carlos A.; Reyes, Andrés
2015-09-01
We propose a methodology to design optimal pulses for achieving quantum optimal control on molecular systems. Our approach constrains pulse shapes to linear combinations of a fixed number of experimentally relevant pulse functions. Quantum optimal control is obtained by maximizing a multi-target fitness function using genetic algorithms. As a first application of the methodology, we generated an optimal pulse that successfully maximized the yield on a selected dissociation channel of a diatomic molecule. Our pulse is obtained as a linear combination of linearly chirped pulse functions. Data recorded along the evolution of the genetic algorithm contained important information regarding the interplay between radiative and diabatic processes. We performed a principal component analysis on these data to retrieve the most relevant processes along the optimal path. Our proposed methodology could be useful for performing quantum optimal control on more complex systems by employing a wider variety of pulse shape functions.
A Solution Quality Assessment Method for Swarm Intelligence Optimization Algorithms
Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua
2014-01-01
Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of “value performance,” the “ordinal performance” is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and “good enough” set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method. PMID:25013845
A solution quality assessment method for swarm intelligence optimization algorithms.
Zhang, Zhaojun; Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua
2014-01-01
Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of "value performance," the "ordinal performance" is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and "good enough" set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method.
An Integrated Control and Minimum Mass Structural Optimization Algorithm for Large Space Structures
NASA Technical Reports Server (NTRS)
Messac, A.; Turner, J.; Soosaar, K.
1985-01-01
A new approach is discussed for solving dual structural control optimization problems for high-order flexible space structures, where reduced-order structural models are employed and minimum mass designs are sought. For a given initial structural design, a quadratic control cost is minimized subject to a constant-mass constraint. The sensitivity of the optimal control cost with respect to the structural design variables is then determined and used to obtain successive structural redesigns, using a constrained gradient optimization algorithm. This process is repeated until the constrained control cost sensitivity becomes negligible. The minimum mass design is obtained by solving a sequence of neighboring optimal constant mass designs, where the sequence of optimal performance indices has a minimum at the optimal minimum mass design. A numerical example is presented which demonstrates that this new approach effectively addresses the problem of dual optimization for potentially very high-order structures.
Sequential unconstrained minimization algorithms for constrained optimization
NASA Astrophysics Data System (ADS)
Byrne, Charles
2008-02-01
The problem of minimizing a function f(x):RJ → R, subject to constraints on the vector variable x, occurs frequently in inverse problems. Even without constraints, finding a minimizer of f(x) may require iterative methods. We consider here a general class of iterative algorithms that find a solution to the constrained minimization problem as the limit of a sequence of vectors, each solving an unconstrained minimization problem. Our sequential unconstrained minimization algorithm (SUMMA) is an iterative procedure for constrained minimization. At the kth step we minimize the function G_k(x)=f(x)+g_k(x), to obtain xk. The auxiliary functions gk(x):D ⊆ RJ → R+ are nonnegative on the set D, each xk is assumed to lie within D, and the objective is to minimize the continuous function f:RJ → R over x in the set C=\\overline D , the closure of D. We assume that such minimizers exist, and denote one such by \\hat x . We assume that the functions gk(x) satisfy the inequalities 0\\leq g_k(x)\\leq G_{k-1}(x)-G_{k-1}(x^{k-1}), for k = 2, 3, .... Using this assumption, we show that the sequence {f(xk)} is decreasing and converges to f({\\hat x}) . If the restriction of f(x) to D has bounded level sets, which happens if \\hat x is unique and f(x) is closed, proper and convex, then the sequence {xk} is bounded, and f(x^*)=f({\\hat x}) , for any cluster point x*. Therefore, if \\hat x is unique, x^*={\\hat x} and \\{x^k\\}\\rightarrow {\\hat x} . When \\hat x is not unique, convergence can still be obtained, in particular cases. The SUMMA includes, as particular cases, the well-known barrier- and penalty-function methods, the simultaneous multiplicative algebraic reconstruction technique (SMART), the proximal minimization algorithm of Censor and Zenios, the entropic proximal methods of Teboulle, as well as certain cases of gradient descent and the Newton-Raphson method. The proof techniques used for SUMMA can be extended to obtain related results for the induced proximal
Jin, Junchen
2016-01-01
The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality. PMID:27436998
Wang, Jiaxi; Lin, Boliang; Jin, Junchen
2016-01-01
The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality.
Performance Trend of Different Algorithms for Structural Design Optimization
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.
1996-01-01
Nonlinear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Center, a project was initiated to assess performance of different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with the sequential unconstrained minimizations technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.
Comparative Evaluation of Different Optimization Algorithms for Structural Design Applications
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.
1996-01-01
Non-linear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Centre, a project was initiated to assess the performance of eight different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using the eight different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems, however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with Sequential Unconstrained Minimizations Technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.
Zarepisheh, Masoud; Li, Nan; Long, Troy; Romeijn, H. Edwin; Tian, Zhen; Jia, Xun; Jiang, Steve B.
2014-06-15
Purpose: To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning. Methods: The algorithm automatically creates a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician-approved dose-volume trade-offs among different targets/organs and among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans. Results: The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel-weighting factors in order to generate a Pareto plan with similar DVH trade-offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency. Conclusions: A novel prior-knowledge-based optimization algorithm has been developed that automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART replanning and automatic treatment
New approaches to optimization in aerospace conceptual design
NASA Technical Reports Server (NTRS)
Gage, Peter J.
1995-01-01
Aerospace design can be viewed as an optimization process, but conceptual studies are rarely performed using formal search algorithms. Three issues that restrict the success of automatic search are identified in this work. New approaches are introduced to address the integration of analyses and optimizers, to avoid the need for accurate gradient information and a smooth search space (required for calculus-based optimization), and to remove the restrictions imposed by fixed complexity problem formulations. (1) Optimization should be performed in a flexible environment. A quasi-procedural architecture is used to conveniently link analysis modules and automatically coordinate their execution. It efficiently controls a large-scale design tasks. (2) Genetic algorithms provide a search method for discontinuous or noisy domains. The utility of genetic optimization is demonstrated here, but parameter encodings and constraint-handling schemes must be carefully chosen to avoid premature convergence to suboptimal designs. The relationship between genetic and calculus-based methods is explored. (3) A variable-complexity genetic algorithm is created to permit flexible parameterization, so that the level of description can change during optimization. This new optimizer automatically discovers novel designs in structural and aerodynamic tasks.
NASA Astrophysics Data System (ADS)
Slawig, Thomas; Rückelt, Johannes; Sauerland, Volkmar; Srivastav, Anand; Ward, Ben
2010-05-01
Methods and results for parameter optimization and uncertainty analysis for a one dimensional marine biogeochemical model of NPZD type developed by Schartau and Oschlies are presented. The model simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column and is driven by ocean data. For the optimization, we use two strategies: At first, a genetic algorithm combined with a local search method. Secondly, a gradient-based quasi-newton SQP method to identify parameters and fit them to given observational data. For the SQP method, we use gradients generated by a source transformation tool for Automatic/Algorithmic Differentiation (AD). The algorithm is designed in a flexible way: The local method is a freely available code that can be replaced by other methods offering the same features, e.g. treatment of box constarints. Both optimization methods are parallized and can be viewed as instances of a hybrid, mixed evolutionary and deterministic optimization algorithm. We compare the performance of both approaches. Moreover, we present an uncertainty analysis of the optimized parameters with respect to Gaussian perturbed observations. Here, an ensemble of perturbed observations is taken as target or desired state for the optimization. After the optimization is applied, the distribution of the optimal parameters shows the dependenc of the parameters with respect to uncertainty in the observations.
Optimization of computer-generated binary holograms using genetic algorithms
NASA Astrophysics Data System (ADS)
Cojoc, Dan; Alexandrescu, Adrian
1999-11-01
The aim of this paper is to compare genetic algorithms against direct point oriented coding in the design of binary phase Fourier holograms, computer generated. These are used as fan-out elements for free space optical interconnection. Genetic algorithms are optimization methods which model the natural process of genetic evolution. The configuration of the hologram is encoded to form a chromosome. To start the optimization, a population of different chromosomes randomly generated is considered. The chromosomes compete, mate and mutate until the best chromosome is obtained according to a cost function. After explaining the operators that are used by genetic algorithms, this paper presents two examples with 32 X 32 genes in a chromosome. The crossover type and the number of mutations are shown to be important factors which influence the convergence of the algorithm. GA is demonstrated to be a useful tool to design namely binary phase holograms of complicate structures.
Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.
2003-01-01
A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.
Multi-objective Optimization on Helium Liquefier Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Wang, H. R.; Xiong, L. Y.; Peng, N.; Meng, Y. R.; Liu, L. Q.
2017-02-01
Research on optimization of helium liquefier is limited at home and abroad, and most of the optimization is single-objective based on Collins cycle. In this paper, a multi-objective optimization is conducted using genetic algorithm (GA) on the 40 L/h helium liquefier developed by Technical Institute of Physics and Chemistry of the Chinese Academy of Science (TIPC, CAS), steady solutions are obtained in the end. In addition, the exergy loss of the optimized system is studied in the case of with and without liquid nitrogen pre-cooling. The results have guiding significance for the future design of large helium liquefier.
Study of genetic direct search algorithms for function optimization
NASA Technical Reports Server (NTRS)
Zeigler, B. P.
1974-01-01
The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.
Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization
NASA Technical Reports Server (NTRS)
Lohn, Jason D.; Kraus, William F.; Haith, Gary L.; Clancy, Daniel (Technical Monitor)
2002-01-01
We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front.
An algorithm for optimal structural design with frequency constraints
NASA Technical Reports Server (NTRS)
Kiusalaas, J.; Shaw, R. C. J.
1978-01-01
The paper presents a finite element method for minimum weight design of structures with lower-bound constraints on the natural frequencies, and upper and lower bounds on the design variables. The design algorithm is essentially an iterative solution of the Kuhn-Tucker optimality criterion. The three most important features of the algorithm are: (1) a small number of design iterations are needed to reach optimal or near-optimal design, (2) structural elements with a wide variety of size-stiffness may be used, the only significant restriction being the exclusion of curved beam and shell elements, and (3) the algorithm will work for multiple as well as single frequency constraints. The design procedure is illustrated with three simple problems.
Benchmarking derivative-free optimization algorithms.
More', J. J.; Wild, S. M.; Mathematics and Computer Science; Cornell Univ.
2009-01-01
We propose data profiles as a tool for analyzing the performance of derivative-free optimization solvers when there are constraints on the computational budget. We use performance and data profiles, together with a convergence test that measures the decrease in function value, to analyze the performance of three solvers on sets of smooth, noisy, and piecewise-smooth problems. Our results provide estimates for the performance difference between these solvers, and show that on these problems, the model-based solver tested performs better than the two direct search solvers tested.
Use of Algorithm of Changes for Optimal Design of Heat Exchanger
NASA Astrophysics Data System (ADS)
Tam, S. C.; Tam, H. K.; Chio, C. H.; Tam, L. M.
2010-05-01
For economic reasons, the optimal design of heat exchanger is required. Design of heat exchanger is usually based on the iterative process. The design conditions, equipment geometries, the heat transfer and friction factor correlations are totally involved in the process. Using the traditional iterative method, many trials are needed for satisfying the compromise between the heat exchange performance and the cost consideration. The process is cumbersome and the optimal design is often depending on the design engineer's experience. Therefore, in the recent studies, many researchers, reviewed in [1], applied the genetic algorithm (GA) [2] for designing the heat exchanger. The results outperformed the traditional method. In this study, the alternative approach, algorithm of changes, is proposed for optimal design of shell-tube heat exchanger [3]. This new method, algorithm of changes based on I Ching (???), is developed originality by the author. In the algorithms, the hexagram operations in I Ching has been generalized to binary string case and the iterative procedure which imitates the I Ching inference is also defined. On the basis of [3], the shell inside diameter, tube outside diameter, and baffles spacing were treated as the design (or optimized) variables. The cost of the heat exchanger was arranged as the objective function. Through the case study, the results show that the algorithm of changes is comparable to the GA method. Both of method can find the optimal solution in a short time. However, without interchanging information between binary strings, the algorithm of changes has advantage on parallel computation over GA.
Bayesian Optimization Algorithm, Population Sizing, and Time to Convergence
Pelikan, M.; Goldberg, D.E.; Cantu-Paz, E.
2000-01-19
This paper analyzes convergence properties of the Bayesian optimization algorithm (BOA). It settles the BOA into the framework of problem decomposition used frequently in order to model and understand the behavior of simple genetic algorithms. The growth of the population size and the number of generations until convergence with respect to the size of a problem is theoretically analyzed. The theoretical results are supported by a number of experiments.
A limited-memory algorithm for bound-constrained optimization
Byrd, R.H.; Peihuang, L.; Nocedal, J. |
1996-03-01
An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based on the gradient projection method and uses a limited-memory BFGS matrix to approximate the Hessian of the objective function. We show how to take advantage of the form of the limited-memory approximation to implement the algorithm efficiently. The results of numerical tests on a set of large problems are reported.
Optimization on robot arm machining by using genetic algorithms
NASA Astrophysics Data System (ADS)
Liu, Tung-Kuan; Chen, Chiu-Hung; Tsai, Shang-En
2007-12-01
In this study, an optimization problem on the robot arm machining is formulated and solved by using genetic algorithms (GAs). The proposed approach adopts direct kinematics model and utilizes GA's global search ability to find the optimum solution. The direct kinematics equations of the robot arm are formulated and can be used to compute the end-effector coordinates. Based on these, the objective of optimum machining along a set of points can be evolutionarily evaluated with the distance between machining points and end-effector positions. Besides, a 3D CAD application, CATIA, is used to build up the 3D models of the robot arm, work-pieces and their components. A simulated experiment in CATIA is used to verify the computation results first and a practical control on the robot arm through the RS232 port is also performed. From the results, this approach is proved to be robust and can be suitable for most machining needs when robot arms are adopted as the machining tools.
Genetic Algorithm Optimizes Q-LAW Control Parameters
NASA Technical Reports Server (NTRS)
Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard
2008-01-01
A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.
On the optimality of the neighbor-joining algorithm
Eickmeyer, Kord; Huggins, Peter; Pachter, Lior; Yoshida, Ruriko
2008-01-01
The popular neighbor-joining (NJ) algorithm used in phylogenetics is a greedy algorithm for finding the balanced minimum evolution (BME) tree associated to a dissimilarity map. From this point of view, NJ is "optimal" when the algorithm outputs the tree which minimizes the balanced minimum evolution criterion. We use the fact that the NJ tree topology and the BME tree topology are determined by polyhedral subdivisions of the spaces of dissimilarity maps R+(n2) to study the optimality of the neighbor-joining algorithm. In particular, we investigate and compare the polyhedral subdivisions for n ≤ 8. This requires the measurement of volumes of spherical polytopes in high dimension, which we obtain using a combination of Monte Carlo methods and polyhedral algorithms. Our results include a demonstration that highly unrelated trees can be co-optimal in BME reconstruction, and that NJ regions are not convex. We obtain the l2 radius for neighbor-joining for n = 5 and we conjecture that the ability of the neighbor-joining algorithm to recover the BME tree depends on the diameter of the BME tree. PMID:18447942
NASA Astrophysics Data System (ADS)
Önol, Can; Alkış, Sena; Gökçe, Özer; Ergül, Özgür
2016-07-01
We consider fast and efficient optimizations of arrays involving three-dimensional antennas with arbitrary shapes and geometries. Heuristic algorithms, particularly genetic algorithms, are used for optimizations, while the required solutions are carried out accurately and efficiently via the multilevel fast multipole algorithm (MLFMA). The superposition principle is employed to reduce the number of MLFMA solutions to the number of array elements per frequency. The developed mechanism is used to optimize arrays for multifrequency and/or multidirection operations, i.e., to find the most suitable set of antenna excitations for desired radiation characteristics simultaneously at different frequencies and/or directions. The capabilities of the optimization environment are demonstrated on arrays of bowtie and Vivaldi antennas.
New near-optimal feedback guidance algorithms for space missions
NASA Astrophysics Data System (ADS)
Hawkins, Matthew Jay
This dissertation describes several different spacecraft guidance algorithms, with applications including asteroid intercept and rendezvous, planetary landing, and orbital transfer. A comprehensive review of spacecraft guidance algorithms for asteroid intercept and rendezvous. Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) guidance is introduced and applied to asteroid intercept and rendezvous, and to a wealth of different example problems, including missile intercept, planetary landing, and orbital transfer. It is seen that the ZEM/ZEV guidance law can be used in many different scenarios, and that it provides near-optimal performance where an analytical optimal guidance law does not exist, such as in a non-linear gravity field.
Vaitheeswaran, Ranganathan; Sathiya, Narayanan V K; Bhangle, Janhavi R; Nirhali, Amit; Kumar, Namita; Basu, Sumit; Maiya, Vikram
2011-04-01
The study aims to introduce a hybrid optimization algorithm for anatomy-based intensity modulated radiotherapy (AB-IMRT). Our proposal is that by integrating an exact optimization algorithm with a heuristic optimization algorithm, the advantages of both the algorithms can be combined, which will lead to an efficient global optimizer solving the problem at a very fast rate. Our hybrid approach combines Gaussian elimination algorithm (exact optimizer) with fast simulated annealing algorithm (a heuristic global optimizer) for the optimization of beam weights in AB-IMRT. The algorithm has been implemented using MATLAB software. The optimization efficiency of the hybrid algorithm is clarified by (i) analysis of the numerical characteristics of the algorithm and (ii) analysis of the clinical capabilities of the algorithm. The numerical and clinical characteristics of the hybrid algorithm are compared with Gaussian elimination method (GEM) and fast simulated annealing (FSA). The numerical characteristics include convergence, consistency, number of iterations and overall optimization speed, which were analyzed for the respective cases of 8 patients. The clinical capabilities of the hybrid algorithm are demonstrated in cases of (a) prostate and (b) brain. The analyses reveal that (i) the convergence speed of the hybrid algorithm is approximately three times higher than that of FSA algorithm; (ii) the convergence (percentage reduction in the cost function) in hybrid algorithm is about 20% improved as compared to that in GEM algorithm; (iii) the hybrid algorithm is capable of producing relatively better treatment plans in terms of Conformity Index (CI) [~ 2% - 5% improvement] and Homogeneity Index (HI) [~ 4% - 10% improvement] as compared to GEM and FSA algorithms; (iv) the sparing of organs at risk in hybrid algorithm-based plans is better than that in GEM-based plans and comparable to that in FSA-based plans; and (v) the beam weights resulting from the hybrid algorithm are
Effective and efficient algorithm for multiobjective optimization of hydrologic models
NASA Astrophysics Data System (ADS)
Vrugt, Jasper A.; Gupta, Hoshin V.; Bastidas, Luis A.; Bouten, Willem; Sorooshian, Soroosh
2003-08-01
Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity.
An Efficient Globally Optimal Algorithm for Asymmetric Point Matching.
Lian, Wei; Zhang, Lei; Yang, Ming-Hsuan
2016-08-29
Although the robust point matching algorithm has been demonstrated to be effective for non-rigid registration, there are several issues with the adopted deterministic annealing optimization technique. First, it is not globally optimal and regularization on the spatial transformation is needed for good matching results. Second, it tends to align the mass centers of two point sets. To address these issues, we propose a globally optimal algorithm for the robust point matching problem where each model point has a counterpart in scene set. By eliminating the transformation variables, we show that the original matching problem is reduced to a concave quadratic assignment problem where the objective function has a low rank Hessian matrix. This facilitates the use of large scale global optimization techniques. We propose a branch-and-bound algorithm based on rectangular subdivision where in each iteration, multiple rectangles are used to increase the chances of subdividing the one containing the global optimal solution. In addition, we present an efficient lower bounding scheme which has a linear assignment formulation and can be efficiently solved. Extensive experiments on synthetic and real datasets demonstrate the proposed algorithm performs favorably against the state-of-the-art methods in terms of robustness to outliers, matching accuracy, and run-time.
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
2015-01-01
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.
Optimization Algorithm for the Generation of ONCV Pseudopotentials
NASA Astrophysics Data System (ADS)
Schlipf, Martin; Gygi, Francois
2015-03-01
We present an optimization algorithm to construct pseudopotentials and use it to generate a set of Optimized Norm-Conserving Vanderbilt (ONCV) pseudopotentials for elements up to Z=83 (Bi) (excluding Lanthanides). We introduce a quality function that assesses the agreement of a pseudopotential calculation with all-electron FLAPW results, and the necessary plane-wave energy cutoff. This quality function allows us to use a Nelder-Mead optimization algorithm on a training set of materials to optimize the input parameters of the pseudopotential construction for most of the periodic table. We control the accuracy of the resulting pseudopotentials on a test set of materials independent of the training set. We find that the automatically constructed pseudopotentials provide a good agreement with the all-electron results obtained using the FLEUR code with a plane-wave energy cutoff of approximately 60 Ry. Supported by DOE/BES Grant DE-SC0008938.
Optimization algorithm for the generation of ONCV pseudopotentials
NASA Astrophysics Data System (ADS)
Schlipf, Martin; Gygi, François
2015-11-01
We present an optimization algorithm to construct pseudopotentials and use it to generate a set of Optimized Norm-Conserving Vanderbilt (ONCV) pseudopotentials for elements up to Z = 83 (Bi) (excluding Lanthanides). We introduce a quality function that assesses the agreement of a pseudopotential calculation with all-electron FLAPW results, and the necessary plane-wave energy cutoff. This quality function allows us to use a Nelder-Mead optimization algorithm on a training set of materials to optimize the input parameters of the pseudopotential construction for most of the periodic table. We control the accuracy of the resulting pseudopotentials on a test set of materials independent of the training set. We find that the automatically constructed pseudopotentials
NASA Astrophysics Data System (ADS)
Helou, E. S.; Zibetti, M. V. W.; Miqueles, E. X.
2017-04-01
We propose the superiorization of incremental algorithms for tomographic image reconstruction. The resulting methods follow a better path in its way to finding the optimal solution for the maximum likelihood problem in the sense that they are closer to the Pareto optimal curve than the non-superiorized techniques. A new scaled gradient iteration is proposed and three superiorization schemes are evaluated. Theoretical analysis of the methods as well as computational experiments with both synthetic and real data are provided.
Control optimization, stabilization and computer algorithms for aircraft applications
NASA Technical Reports Server (NTRS)
1975-01-01
Research related to reliable aircraft design is summarized. Topics discussed include systems reliability optimization, failure detection algorithms, analysis of nonlinear filters, design of compensators incorporating time delays, digital compensator design, estimation for systems with echoes, low-order compensator design, descent-phase controller for 4-D navigation, infinite dimensional mathematical programming problems and optimal control problems with constraints, robust compensator design, numerical methods for the Lyapunov equations, and perturbation methods in linear filtering and control.
A Global Optimization Algorithm Using Stochastic Differential Equations.
1985-02-01
Bari (Italy).2Istituto di Fisica , 2 UniversitA di Roma "Tor Vergata", Via Orazio Raimondo, 00173 (La Romanina) Roma (Italy). 3Istituto di Matematica ...accompanying Algorithm. lDipartininto di Matematica , Universita di Bari, 70125 Bar (Italy). Istituto di Fisica , 2a UniversitA di Roim ’"Tor Vergata", Via...Optimization, Stochastic Differential Equations Work Unit Number 5 (Optimization and Large Scale Systems) 6Dipartimento di Matematica , Universita di Bari, 70125
A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm
NASA Astrophysics Data System (ADS)
Mohanty, Prases K.; Parhi, Dayal R.
2014-12-01
Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.
A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)
NASA Astrophysics Data System (ADS)
Cantó, J.; Curiel, S.; Martínez-Gómez, E.
2009-07-01
Context: Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims: We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (asexual genetic algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two examples: the orbits of exoplanets by taking a set of radial velocity data, and the spectral energy distribution (SED) observed towards a YSO (Young Stellar Object). Methods: The algorithm AGA may also be called genetic, although it differs from standard genetic algorithms in two main aspects: a) the initial population is not encoded; and b) the new generations are constructed by asexual reproduction. Results: Applying our algorithm in optimizing some complicated functions, we find the global maxima within a few iterations. For model fitting to the orbits of exoplanets and the SED of a YSO, we estimate the parameters and their associated errors.
Optimization of Power Coefficient of Wind Turbine Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Rajakumar, Sappani; Ravindran, Durairaj; Sivakumar, Mahalingam; Venkatachalam, Gopalan; Muthukumar, Shunmugavelu
2016-06-01
In the design of a wind turbine, the goal is to attain the highest possible power output under specified atmospheric conditions. The optimization of power coefficient of horizontal axis wind turbine has been carried out by integration of blade element momentum method and genetic algorithm (GA). The design variables considered are wind velocity, angle of attack and tip speed ratio. The objective function is power coefficient of wind turbine. The different combination of design variables are optimized using GA and then the Power coefficient is optimized. The optimized design variables are validated with the experimental results available in the literature. By this optimization work the optimum design variables of wind turbine can be found economically than experimental work. NACA44XX series airfoils are considered for this optimization work.
Making Big Data, Safe Data: A Test Optimization Approach
2016-06-15
Business & Public Policy Naval Postgraduate School Executive Summary The following report outlines a procedure and algorithm to optimize the...the Network Data ............................................................................. 11 Step 6: Run Algorithm ... Algorithm ....................................................................................................... 16 Step 7: Perform Suggested Tests
NASA Astrophysics Data System (ADS)
Galvan-Sosa, M.; Portilla, J.; Hernandez-Rueda, J.; Siegel, J.; Moreno, L.; Ruiz de la Cruz, A.; Solis, J.
2014-02-01
Femtosecond laser pulse temporal shaping techniques have led to important advances in different research fields like photochemistry, laser physics, non-linear optics, biology, or materials processing. This success is partly related to the use of optimal control algorithms. Due to the high dimensionality of the solution and control spaces, evolutionary algorithms are extensively applied and, among them, genetic ones have reached the status of a standard adaptive strategy. Still, their use is normally accompanied by a reduction of the problem complexity by different modalities of parameterization of the spectral phase. Exploiting Rabitz and co-authors' ideas about the topology of quantum landscapes, in this work we analyze the optimization of two different problems under a deterministic approach, using a multiple one-dimensional search (MODS) algorithm. In the first case we explore the determination of the optimal phase mask required for generating arbitrary temporal pulse shapes and compare the performance of the MODS algorithm to the standard iterative Gerchberg-Saxton algorithm. Based on the good performance achieved, the same method has been applied for optimizing two-photon absorption starting from temporally broadened laser pulses, or from laser pulses temporally and spectrally distorted by non-linear absorption in air, obtaining similarly good results which confirm the validity of the deterministic search approach.
NASA Astrophysics Data System (ADS)
Galvan-Sosa, M.; Portilla, J.; Hernandez-Rueda, J.; Siegel, J.; Moreno, L.; Ruiz de la Cruz, A.; Solis, J.
2013-04-01
Femtosecond laser pulse temporal shaping techniques have led to important advances in different research fields like photochemistry, laser physics, non-linear optics, biology, or materials processing. This success is partly related to the use of optimal control algorithms. Due to the high dimensionality of the solution and control spaces, evolutionary algorithms are extensively applied and, among them, genetic ones have reached the status of a standard adaptive strategy. Still, their use is normally accompanied by a reduction of the problem complexity by different modalities of parameterization of the spectral phase. Exploiting Rabitz and co-authors' ideas about the topology of quantum landscapes, in this work we analyze the optimization of two different problems under a deterministic approach, using a multiple one-dimensional search (MODS) algorithm. In the first case we explore the determination of the optimal phase mask required for generating arbitrary temporal pulse shapes and compare the performance of the MODS algorithm to the standard iterative Gerchberg-Saxton algorithm. Based on the good performance achieved, the same method has been applied for optimizing two-photon absorption starting from temporally broadened laser pulses, or from laser pulses temporally and spectrally distorted by non-linear absorption in air, obtaining similarly good results which confirm the validity of the deterministic search approach.
Du, Yanqin; Huang, Hua
2011-10-01
Fetal electrocardiogram (FECG) is an objective index of the activities of fetal cardiac electrophysiology. The acquired FECG is interfered by maternal electrocardiogram (MECG). How to extract the fetus ECG quickly and effectively has become an important research topic. During the non-invasive FECG extraction algorithms, independent component analysis(ICA) algorithm is considered as the best method, but the existing algorithms of obtaining the decomposition of the convergence properties of the matrix do not work effectively. Quantum particle swarm optimization (QPSO) is an intelligent optimization algorithm converging in the global. In order to extract the FECG signal effectively and quickly, we propose a method combining ICA and QPSO. The results show that this approach can extract the useful signal more clearly and accurately than other non-invasive methods.
Environmental Optimization Using the WAste Reduction Algorithm (WAR)
Traditionally chemical process designs were optimized using purely economic measures such as rate of return. EPA scientists developed the WAste Reduction algorithm (WAR) so that environmental impacts of designs could easily be evaluated. The goal of WAR is to reduce environme...
Attitude determination using vector observations - A fast optimal matrix algorithm
NASA Technical Reports Server (NTRS)
Markley, F. L.
1993-01-01
The attitude matrix minimizing Wahba's loss function is computed directly by a method that is competitive with the fastest known algorithm for finding this optimal estimate. The method also provides an estimate of the attitude error covariance matrix. Analysis of the special case of two vector observations identifies those cases for which the TRIAD or algebraic method minimizes Wahba's loss function.
Attitude determination using vector observations: A fast optimal matrix algorithm
NASA Technical Reports Server (NTRS)
Markley, F. Landis
1993-01-01
The attitude matrix minimizing Wahba's loss function is computed directly by a method that is competitive with the fastest known algorithm for finding this optimal estimate. The method also provides an estimate of the attitude error covariance matrix. Analysis of the special case of two vector observations identifies those cases for which the TRIAD or algebraic method minimizes Wahba's loss function.
Optimal pulse shaping for coherent control by the penalty algorithm
NASA Astrophysics Data System (ADS)
Shen, Hai; Dussault, Jean-Pièrre; Bandrauk, André D.
1994-04-01
We use penalty methods coupled with unitary exponential operator methods to solve the optimal control problem for molecular time-dependent Schrödinger equations involving laser pulse excitations. A stable numerical algorithm is presented which propagates directly from initial states to given final states. Results are reported for an analytically solvable model for the complete inversion of a three-state system.
Numerical Optimization Algorithms and Software for Systems Biology
Saunders, Michael
2013-02-02
The basic aims of this work are: to develop reliable algorithms for solving optimization problems involving large stoi- chiometric matrices; to investigate cyclic dependency between metabolic and macromolecular biosynthetic networks; and to quantify the significance of thermodynamic constraints on prokaryotic metabolism.
Experimental implementation of an adiabatic quantum optimization algorithm
NASA Astrophysics Data System (ADS)
Steffen, Matthias; van Dam, Wim; Hogg, Tad; Breyta, Greg; Chuang, Isaac
2003-03-01
A novel quantum algorithm using adiabatic evolution was recently presented by Ed Farhi [1] and Tad Hogg [2]. This algorithm represents a remarkable discovery because it offers new insights into the usefulness of quantum resources. An experimental demonstration of an adiabatic algorithm has remained beyond reach because it requires an experimentally accessible Hamiltonian which encodes the problem and which must also be smoothly varied over time. We present tools to overcome these difficulties by discretizing the algorithm and extending average Hamiltonian techniques [3]. We used these techniques in the first experimental demonstration of an adiabatic optimization algorithm: solving an instance of the MAXCUT problem using three qubits and nuclear magnetic resonance techniques. We show that there exists an optimal run-time of the algorithm which can be predicted using a previously developed decoherence model. [1] E. Farhi et al., quant-ph/0001106 (2000) [2] T. Hogg, PRA, 61, 052311 (2000) [3] W. Rhim, A. Pines, J. Waugh, PRL, 24,218 (1970)
Optimization algorithms for large-scale multireservoir hydropower systems
Hiew, K.L.
1987-01-01
Five optimization algorithms were vigorously evaluated based on applications on a hypothetical five-reservoir hydropower system. These algorithms are incremental dynamic programming (IDP), successive linear programing (SLP), feasible direction method (FDM), optimal control theory (OCT) and objective-space dynamic programming (OSDP). The performance of these algorithms were comparatively evaluated using unbiased, objective criteria which include accuracy of results, rate of convergence, smoothness of resulting storage and release trajectories, computer time and memory requirements, robustness and other pertinent secondary considerations. Results have shown that all the algorithms, with the exception of OSDP converge to optimum objective values within 1.0% difference from one another. The highest objective value is obtained by IDP, followed closely by OCT. Computer time required by these algorithms, however, differ by more than two orders of magnitude, ranging from 10 seconds in the case of OCT to a maximum of about 2000 seconds for IDP. With a well-designed penalty scheme to deal with state-space constraints, OCT proves to be the most-efficient algorithm based on its overall performance. SLP, FDM, and OCT were applied to the case study of Mahaweli project, a ten-powerplant system in Sri Lanka.
Model updating based on an affine scaling interior optimization algorithm
NASA Astrophysics Data System (ADS)
Zhang, Y. X.; Jia, C. X.; Li, Jian; Spencer, B. F.
2013-11-01
Finite element model updating is usually considered as an optimization process. Affine scaling interior algorithms are powerful optimization algorithms that have been developed over the past few years. A new finite element model updating method based on an affine scaling interior algorithm and a minimization of modal residuals is proposed in this article, and a general finite element model updating program is developed based on the proposed method. The performance of the proposed method is studied through numerical simulation and experimental investigation using the developed program. The results of the numerical simulation verified the validity of the method. Subsequently, the natural frequencies obtained experimentally from a three-dimensional truss model were used to update a finite element model using the developed program. After updating, the natural frequencies of the truss and finite element model matched well.
An improved particle swarm optimization algorithm for reliability problems.
Wu, Peifeng; Gao, Liqun; Zou, Dexuan; Li, Steven
2011-01-01
An improved particle swarm optimization (IPSO) algorithm is proposed to solve reliability problems in this paper. The IPSO designs two position updating strategies: In the early iterations, each particle flies and searches according to its own best experience with a large probability; in the late iterations, each particle flies and searches according to the fling experience of the most successful particle with a large probability. In addition, the IPSO introduces a mutation operator after position updating, which can not only prevent the IPSO from trapping into the local optimum, but also enhances its space developing ability. Experimental results show that the proposed algorithm has stronger convergence and stability than the other four particle swarm optimization algorithms on solving reliability problems, and that the solutions obtained by the IPSO are better than the previously reported best-known solutions in the recent literature.
Endgame implementations for the Efficient Global Optimization (EGO) algorithm
NASA Astrophysics Data System (ADS)
Southall, Hugh L.; O'Donnell, Teresa H.; Kaanta, Bryan
2009-05-01
Efficient Global Optimization (EGO) is a competent evolutionary algorithm which can be useful for problems with expensive cost functions [1,2,3,4,5]. The goal is to find the global minimum using as few function evaluations as possible. Our research indicates that EGO requires far fewer evaluations than genetic algorithms (GAs). However, both algorithms do not always drill down to the absolute minimum, therefore the addition of a final local search technique is indicated. In this paper, we introduce three "endgame" techniques. The techniques can improve optimization efficiency (fewer cost function evaluations) and, if required, they can provide very accurate estimates of the global minimum. We also report results using a different cost function than the one previously used [2,3].
Optimal brushless DC motor design using genetic algorithms
NASA Astrophysics Data System (ADS)
Rahideh, A.; Korakianitis, T.; Ruiz, P.; Keeble, T.; Rothman, M. T.
2010-11-01
This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.
Two neural network algorithms for designing optimal terminal controllers with open final time
NASA Technical Reports Server (NTRS)
Plumer, Edward S.
1992-01-01
Multilayer neural networks, trained by the backpropagation through time algorithm (BPTT), have been used successfully as state-feedback controllers for nonlinear terminal control problems. Current BPTT techniques, however, are not able to deal systematically with open final-time situations such as minimum-time problems. Two approaches which extend BPTT to open final-time problems are presented. In the first, a neural network learns a mapping from initial-state to time-to-go. In the second, the optimal number of steps for each trial run is found using a line-search. Both methods are derived using Lagrange multiplier techniques. This theoretical framework is used to demonstrate that the derived algorithms are direct extensions of forward/backward sweep methods used in N-stage optimal control. The two algorithms are tested on a Zermelo problem and the resulting trajectories compare favorably to optimal control results.
NASA Astrophysics Data System (ADS)
von Rudorff, Guido Falk; Wehmeyer, Christoph; Sebastiani, Daniel
2014-06-01
We adapt a swarm-intelligence-based optimization method (the artificial bee colony algorithm, ABC) to enhance its parallel scaling properties and to improve the escaping behavior from deep local minima. Specifically, we apply the approach to the geometry optimization of Lennard-Jones clusters. We illustrate the performance and the scaling properties of the parallelization scheme for several system sizes (5-20 particles). Our main findings are specific recommendations for ranges of the parameters of the ABC algorithm which yield maximal performance for Lennard-Jones clusters and Morse clusters. The suggested parameter ranges for these different interaction potentials turn out to be very similar; thus, we believe that our reported values are fairly general for the ABC algorithm applied to chemical optimization problems.
CUDA optimization strategies for compute- and memory-bound neuroimaging algorithms.
Lee, Daren; Dinov, Ivo; Dong, Bin; Gutman, Boris; Yanovsky, Igor; Toga, Arthur W
2012-06-01
As neuroimaging algorithms and technology continue to grow faster than CPU performance in complexity and image resolution, data-parallel computing methods will be increasingly important. The high performance, data-parallel architecture of modern graphical processing units (GPUs) can reduce computational times by orders of magnitude. However, its massively threaded architecture introduces challenges when GPU resources are exceeded. This paper presents optimization strategies for compute- and memory-bound algorithms for the CUDA architecture. For compute-bound algorithms, the registers are reduced through variable reuse via shared memory and the data throughput is increased through heavier thread workloads and maximizing the thread configuration for a single thread block per multiprocessor. For memory-bound algorithms, fitting the data into the fast but limited GPU resources is achieved through reorganizing the data into self-contained structures and employing a multi-pass approach. Memory latencies are reduced by selecting memory resources whose cache performance are optimized for the algorithm's access patterns. We demonstrate the strategies on two computationally expensive algorithms and achieve optimized GPU implementations that perform up to 6× faster than unoptimized ones. Compared to CPU implementations, we achieve peak GPU speedups of 129× for the 3D unbiased nonlinear image registration technique and 93× for the non-local means surface denoising algorithm.
A Unified Approach to Optimization
2014-10-02
and dynamic programming, logic-based Benders decomposition, and unification of exact and heuristic methods. The publications associated with this...Logic-Based Benders Decomposition Logic-based Benders decomposition (LBBD) has been used for some years to combine CP and MIP, usually by solving the...classical Benders decomposition, but can be any optimization problem. Benders cuts are generated by solving the inference dual of the subproblem
Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding
Sun, Lijuan; Guo, Jian; Xu, Bin; Li, Shujing
2017-01-01
The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur's entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability. PMID:28127305
Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding.
Li, Linguo; Sun, Lijuan; Guo, Jian; Qi, Jin; Xu, Bin; Li, Shujing
2017-01-01
The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur's entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability.
Optimal online learning: a Bayesian approach
NASA Astrophysics Data System (ADS)
Solla, Sara A.; Winther, Ole
1999-09-01
A recently proposed Bayesian approach to online learning is applied to learning a rule defined as a noisy single layer perceptron. In the Bayesian online approach, the exact posterior distribution is approximated by a simple parametric posterior that is updated online as new examples are incorporated to the dataset. In the case of binary weights, the approximate posterior is chosen to be a biased binary distribution. The resulting online algorithm is shown to outperform several other online approaches to this problem.
An active set algorithm for treatment planning optimization.
Hristov, D H; Fallone, B G
1997-09-01
An active set algorithm for optimization of radiation therapy dose planning by intensity modulated beams has been developed. The algorithm employs a conjugate-gradient routine for subspace minimization in order to achieve a higher rate of convergence than the widely used constrained steepest-descent method at the expense of a negligible amount of overhead calculations. The performance of the new algorithm has been compared to that of the constrained steepest-descent method for various treatment geometries and two different objectives. The active set algorithm is found to be superior to the constrained steepest descent, both in terms of its convergence properties and the residual value of the cost functions at termination. Its use can significantly accelerate the design of conformal plans with intensity modulated beams by decreasing the number of time-consuming dose calculations.
Optimization of circuits using a constructive learning algorithm
Beiu, V.
1997-05-01
The paper presents an application of a constructive learning algorithm to optimization of circuits. For a given Boolean function f. a fresh constructive learning algorithm builds circuits belonging to the smallest F{sub n,m} class of functions (n inputs and having m groups of ones in their truth table). The constructive proofs, which show how arbitrary Boolean functions can be implemented by this algorithm, are shortly enumerated An interesting aspect is that the algorithm can be used for generating both classical Boolean circuits and threshold gate circuits (i.e. analogue inputs and digital outputs), or a mixture of them, thus taking advantage of mixed analogue/digital technologies. One illustrative example is detailed The size and the area of the different circuits are compared (special cost functions can be used to closer estimate the area and the delay of VLSI implementations). Conclusions and further directions of research are ending the paper.
SOPRA: Scaffolding algorithm for paired reads via statistical optimization
2010-01-01
Background High throughput sequencing (HTS) platforms produce gigabases of short read (<100 bp) data per run. While these short reads are adequate for resequencing applications, de novo assembly of moderate size genomes from such reads remains a significant challenge. These limitations could be partially overcome by utilizing mate pair technology, which provides pairs of short reads separated by a known distance along the genome. Results We have developed SOPRA, a tool designed to exploit the mate pair/paired-end information for assembly of short reads. The main focus of the algorithm is selecting a sufficiently large subset of simultaneously satisfiable mate pair constraints to achieve a balance between the size and the quality of the output scaffolds. Scaffold assembly is presented as an optimization problem for variables associated with vertices and with edges of the contig connectivity graph. Vertices of this graph are individual contigs with edges drawn between contigs connected by mate pairs. Similar graph problems have been invoked in the context of shotgun sequencing and scaffold building for previous generation of sequencing projects. However, given the error-prone nature of HTS data and the fundamental limitations from the shortness of the reads, the ad hoc greedy algorithms used in the earlier studies are likely to lead to poor quality results in the current context. SOPRA circumvents this problem by treating all the constraints on equal footing for solving the optimization problem, the solution itself indicating the problematic constraints (chimeric/repetitive contigs, etc.) to be removed. The process of solving and removing of constraints is iterated till one reaches a core set of consistent constraints. For SOLiD sequencer data, SOPRA uses a dynamic programming approach to robustly translate the color-space assembly to base-space. For assessing the quality of an assembly, we report the no-match/mismatch error rate as well as the rates of various
Machnes, S.; Sander, U.; Glaser, S. J.; Schulte-Herbrueggen, T.; Fouquieres, P. de; Gruslys, A.; Schirmer, S.
2011-08-15
For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. Moreover, we introduce a unifying algorithmic framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.
Evaluation of Genetic Algorithm Concepts using Model Problems. Part 1; Single-Objective Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.
2003-01-01
A genetic-algorithm-based optimization approach is described and evaluated using a simple hill-climbing model problem. The model problem utilized herein allows for the broad specification of a large number of search spaces including spaces with an arbitrary number of genes or decision variables and an arbitrary number hills or modes. In the present study, only single objective problems are considered. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all problems attempted. The most difficult problems - those with large hyper-volumes and multi-mode search spaces containing a large number of genes - require a large number of function evaluations for GA convergence, but they always converge.
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.
Garro, Beatriz A; Vázquez, Roberto A
2015-01-01
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.
Iris recognition using Gabor filters optimized by the particle swarm algorithm
NASA Astrophysics Data System (ADS)
Tsai, Chung-Chih; Taur, Jin-Shiuh; Tao, Chin-Wang
2009-04-01
An efficient feature extraction algorithm based on optimized Gabor filters and a relative variation analysis approach is proposed for iris recognition. The Gabor filters are optimized by using the particle swarm algorithm to adjust the parameters. Moreover, a sequential scheme is developed to determine the number of filters in the optimal Gabor filter bank. In the preprocessing step, the lower part of the iris image is unwrapped and normalized to a rectangular block that is then decomposed by the optimal Gabor filters. After that, a simple encoding method is adopted to generate a compact iris code. Experimental results show that with a smaller iris code size, the proposed method can produce comparable performance to that of the existing iris recognition systems.
Fine-Tuning ADAS Algorithm Parameters for Optimizing Traffic ...
With the development of the Connected Vehicle technology that facilitates wirelessly communication among vehicles and road-side infrastructure, the Advanced Driver Assistance Systems (ADAS) can be adopted as an effective tool for accelerating traffic safety and mobility optimization at various highway facilities. To this end, the traffic management centers identify the optimal ADAS algorithm parameter set that enables the maximum improvement of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. After adopting the optimal parameter set, the ADAS-equipped drivers become active agents in the traffic stream that work collectively and consistently to prevent traffic conflicts, lower the intensity of traffic disturbances, and suppress the development of traffic oscillations into heavy traffic jams. Successful implementation of this objective requires the analysis capability of capturing the impact of the ADAS on driving behaviors, and measuring traffic safety and mobility performance under the influence of the ADAS. To address this challenge, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through an optimization programming framework to enable th
Optimization of warfarin dose by population-specific pharmacogenomic algorithm.
Pavani, A; Naushad, S M; Rupasree, Y; Kumar, T R; Malempati, A R; Pinjala, R K; Mishra, R C; Kutala, V K
2012-08-01
To optimize the warfarin dose, a population-specific pharmacogenomic algorithm was developed using multiple linear regression model with vitamin K intake and cytochrome P450 IIC polypeptide9 (CYP2C9(*)2 and (*)3), vitamin K epoxide reductase complex 1 (VKORC1(*)3, (*)4, D36Y and -1639 G>A) polymorphism profile of subjects who attained therapeutic international normalized ratio as predictors. New algorithm was validated by correlating with Wadelius, International Warfarin Pharmacogenetics Consortium and Gage algorithms; and with the therapeutic dose (r=0.64, P<0.0001). New algorithm was more accurate (Overall: 0.89 vs 0.51, warfarin resistant: 0.96 vs 0.77 and warfarin sensitive: 0.80 vs 0.24), more sensitive (0.87 vs 0.52) and specific (0.93 vs 0.50) compared with clinical data. It has significantly reduced the rate of overestimation (0.06 vs 0.50) and underestimation (0.13 vs 0.48). To conclude, this population-specific algorithm has greater clinical utility in optimizing the warfarin dose, thereby decreasing the adverse effects of suboptimal dose.
Study of sequential optimal control algorithm smart isolation structure based on Simulink-S function
NASA Astrophysics Data System (ADS)
Liu, Xiaohuan; Liu, Yanhui
2017-01-01
The study of this paper focuses on smart isolation structure, a method for realizing structural vibration control by using Simulink simulation is proposed according to the proposed sequential optimal control algorithm. In the Simulink simulation environment, A smart isolation structure is used to compare the control effect of three algorithms, i.e., classical optimal control algorithm, linear quadratic gaussian control algorithm and sequential optimal control algorithm under the condition of sensor contaminated with noise. Simulation results show that this method can be applied to the simulation of sequential optimal control algorithm and the proposed sequential optimal control algorithm has a good ability of resisting the noise and better control efficiency.
Optimization of Circular Ring Microstrip Antenna Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Sathi, V.; Ghobadi, Ch.; Nourinia, J.
2008-10-01
Circular ring microstrip antennas have several interesting properties that make it attractive in wireless applications. Although several analysis techniques such as cavity model, generalized transmission line model, Fourier-Hankel transform domain and the method of matched asymptotic expansion have been studied by researchers, there is no efficient design tool that has been incorporated with a suitable optimization algorithm. In this paper, the cavity model analysis along with the genetic optimization algorithm is presented for the design of circular ring microstrip antennas. The method studied here is based on the well-known cavity model and the optimization of the dimensions and feed point location of the circular ring antenna is performed via the genetic optimization algorithm, to achieve an acceptable antenna operation around a desired resonance frequency. The antennas designed by this efficient design procedure were realized experimentally, and the results are compared. In addition, these results are also compared to the results obtained by the commercial electromagnetic simulation tool, the FEM based software, HFSS by ANSOFT.
Facial Skin Segmentation Using Bacterial Foraging Optimization Algorithm
Bakhshali, Mohamad Amin; Shamsi, Mousa
2012-01-01
Nowadays, analyzing human facial image has gained an ever-increasing importance due to its various applications. Image segmentation is required as a very important and fundamental operation for significant analysis and interpretation of images. Among the segmentation methods, image thresholding technique is one of the most well-known methods due to its simplicity, robustness, and high precision. Thresholding based on optimization of the objective function is among the best methods. Numerous methods exist for the optimization process and bacterial foraging optimization (BFO) is among the most efficient and novel ones. Using this method, optimal threshold is extracted and then segmentation of facial skin is performed. In the proposed method, first, the color facial image is converted from RGB color space to Improved Hue-Luminance-Saturation (IHLS) color space, because IHLS has a great mapping of the skin color. To perform thresholding, the entropy-based method is applied. In order to find the optimum threshold, BFO is used. In order to analyze the proposed algorithm, color images of the database of Sahand University of Technology of Tabriz, Iran were used. Then, using Otsu and Kapur methods, thresholding was performed. In order to have a better understanding from the proposed algorithm; genetic algorithm (GA) is also used for finding the optimum threshold. The proposed method shows the better results than other thresholding methods. These results include misclassification error accuracy (88%), non-uniformity accuracy (89%), and the accuracy of region's area error (89%). PMID:23724370
Facial skin segmentation using bacterial foraging optimization algorithm.
Bakhshali, Mohamad Amin; Shamsi, Mousa
2012-10-01
Nowadays, analyzing human facial image has gained an ever-increasing importance due to its various applications. Image segmentation is required as a very important and fundamental operation for significant analysis and interpretation of images. Among the segmentation methods, image thresholding technique is one of the most well-known methods due to its simplicity, robustness, and high precision. Thresholding based on optimization of the objective function is among the best methods. Numerous methods exist for the optimization process and bacterial foraging optimization (BFO) is among the most efficient and novel ones. Using this method, optimal threshold is extracted and then segmentation of facial skin is performed. In the proposed method, first, the color facial image is converted from RGB color space to Improved Hue-Luminance-Saturation (IHLS) color space, because IHLS has a great mapping of the skin color. To perform thresholding, the entropy-based method is applied. In order to find the optimum threshold, BFO is used. In order to analyze the proposed algorithm, color images of the database of Sahand University of Technology of Tabriz, Iran were used. Then, using Otsu and Kapur methods, thresholding was performed. In order to have a better understanding from the proposed algorithm; genetic algorithm (GA) is also used for finding the optimum threshold. The proposed method shows the better results than other thresholding methods. These results include misclassification error accuracy (88%), non-uniformity accuracy (89%), and the accuracy of region's area error (89%).
A Degree Distribution Optimization Algorithm for Image Transmission
NASA Astrophysics Data System (ADS)
Jiang, Wei; Yang, Junjie
2016-09-01
Luby Transform (LT) code is the first practical implementation of digital fountain code. The coding behavior of LT code is mainly decided by the degree distribution which determines the relationship between source data and codewords. Two degree distributions are suggested by Luby. They work well in typical situations but not optimally in case of finite encoding symbols. In this work, the degree distribution optimization algorithm is proposed to explore the potential of LT code. Firstly selection scheme of sparse degrees for LT codes is introduced. Then probability distribution is optimized according to the selected degrees. In image transmission, bit stream is sensitive to the channel noise and even a single bit error may cause the loss of synchronization between the encoder and the decoder. Therefore the proposed algorithm is designed for image transmission situation. Moreover, optimal class partition is studied for image transmission with unequal error protection. The experimental results are quite promising. Compared with LT code with robust soliton distribution, the proposed algorithm improves the final quality of recovered images obviously with the same overhead.
Hierarchical artificial bee colony algorithm for RFID network planning optimization.
Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong
2014-01-01
This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.
Optimization of an antenna array using genetic algorithms
Kiehbadroudinezhad, Shahideh; Noordin, Nor Kamariah; Sali, A.; Abidin, Zamri Zainal
2014-06-01
An array of antennas is usually used in long distance communication. The observation of celestial objects necessitates a large array of antennas, such as the Giant Metrewave Radio Telescope (GMRT). Optimizing this kind of array is very important when observing a high performance system. The genetic algorithm (GA) is an optimization solution for these kinds of problems that reconfigures the position of antennas to increase the u-v coverage plane or decrease the sidelobe levels (SLLs). This paper presents how to optimize a correlator antenna array using the GA. A brief explanation about the GA and operators used in this paper (mutation and crossover) is provided. Then, the results of optimization are discussed. The results show that the GA provides efficient and optimum solutions among a pool of candidate solutions in order to achieve the desired array performance for the purposes of radio astronomy. The proposed algorithm is able to distribute the u-v plane more efficiently than GMRT with a more than 95% distribution ratio at snapshot, and to fill the u-v plane from a 20% to more than 68% filling ratio as the number of generations increases in the hour tracking observations. Finally, the algorithm is able to reduce the SLL to –21.75 dB.
Preliminary flight evaluation of an engine performance optimization algorithm
NASA Technical Reports Server (NTRS)
Lambert, H. H.; Gilyard, G. B.; Chisholm, J. D.; Kerr, L. J.
1991-01-01
A performance seeking control (PSC) algorithm has undergone initial flight test evaluation in subsonic operation of a PW 1128 engined F-15. This algorithm is designed to optimize the quasi-steady performance of an engine for three primary modes: (1) minimum fuel consumption; (2) minimum fan turbine inlet temperature (FTIT); and (3) maximum thrust. The flight test results have verified a thrust specific fuel consumption reduction of 1 pct., up to 100 R decreases in FTIT, and increases of as much as 12 pct. in maximum thrust. PSC technology promises to be of value in next generation tactical and transport aircraft.
Acceleration of quantum optimal control theory algorithms with mixing strategies.
Castro, Alberto; Gross, E K U
2009-05-01
We propose the use of mixing strategies to accelerate the convergence of the common iterative algorithms utilized in quantum optimal control theory (QOCT). We show how the nonlinear equations of QOCT can be viewed as a "fixed-point" nonlinear problem. The iterative algorithms for this class of problems may benefit from mixing strategies, as it happens, e.g., in the quest for the ground-state density in Kohn-Sham density-functional theory. We demonstrate, with some numerical examples, how the same mixing schemes utilized in this latter nonlinear problem may significantly accelerate the QOCT iterative procedures.
Optimization of multicast optical networks with genetic algorithm
NASA Astrophysics Data System (ADS)
Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng
2007-11-01
In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.
Global structual optimizations of surface systems with a genetic algorithm
Chuang, Feng-Chuan
2005-01-01
Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al_{n} algorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a genetic algorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a genetic algorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of √3 x √3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems.
Multidisciplinary Design, Analysis, and Optimization Tool Development Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Pak, Chan-gi; Li, Wesley
2009-01-01
Multidisciplinary design, analysis, and optimization using a genetic algorithm is being developed at the National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) to automate analysis and design process by leveraging existing tools to enable true multidisciplinary optimization in the preliminary design stage of subsonic, transonic, supersonic, and hypersonic aircraft. This is a promising technology, but faces many challenges in large-scale, real-world application. This report describes current approaches, recent results, and challenges for multidisciplinary design, analysis, and optimization as demonstrated by experience with the Ikhana fire pod design.!
NASA Astrophysics Data System (ADS)
Nourelfath, M.; Nahas, N.; Montreuil, B.
2007-12-01
This article uses a hybrid optimization approach to solve the discrete facility layout problem (FLP), modelled as a quadratic assignment problem (QAP). The idea of this approach design is inspired by the ant colony meta-heuristic optimization method, combined with the extended great deluge (EGD) local search technique. Comparative computational experiments are carried out on benchmarks taken from the QAP-library and from real life problems. The performance of the proposed algorithm is compared to construction and improvement heuristics such as H63, HC63-66, CRAFT and Bubble Search, as well as other existing meta-heuristics developed in the literature based on simulated annealing (SA), tabu search and genetic algorithms (GAs). This algorithm is compared also to other ant colony implementations for QAP. The experimental results show that the proposed ant colony optimization/extended great deluge (ACO/EGD) performs significantly better than the existing construction and improvement algorithms. The experimental results indicate also that the ACO/EGD heuristic methodology offers advantages over other algorithms based on meta-heuristics in terms of solution quality.
A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization.
Zhu, Binglian; Zhu, Wenyong; Liu, Zijuan; Duan, Qingyan; Cao, Long
2016-01-01
This paper proposes a novel quantum-behaved bat algorithm with the direction of mean best position (QMBA). In QMBA, the position of each bat is mainly updated by the current optimal solution in the early stage of searching and in the late search it also depends on the mean best position which can enhance the convergence speed of the algorithm. During the process of searching, quantum behavior of bats is introduced which is beneficial to jump out of local optimal solution and make the quantum-behaved bats not easily fall into local optimal solution, and it has better ability to adapt complex environment. Meanwhile, QMBA makes good use of statistical information of best position which bats had experienced to generate better quality solutions. This approach not only inherits the characteristic of quick convergence, simplicity, and easy implementation of original bat algorithm, but also increases the diversity of population and improves the accuracy of solution. Twenty-four benchmark test functions are tested and compared with other variant bat algorithms for numerical optimization the simulation results show that this approach is simple and efficient and can achieve a more accurate solution.
A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization
Zhu, Wenyong; Liu, Zijuan; Duan, Qingyan; Cao, Long
2016-01-01
This paper proposes a novel quantum-behaved bat algorithm with the direction of mean best position (QMBA). In QMBA, the position of each bat is mainly updated by the current optimal solution in the early stage of searching and in the late search it also depends on the mean best position which can enhance the convergence speed of the algorithm. During the process of searching, quantum behavior of bats is introduced which is beneficial to jump out of local optimal solution and make the quantum-behaved bats not easily fall into local optimal solution, and it has better ability to adapt complex environment. Meanwhile, QMBA makes good use of statistical information of best position which bats had experienced to generate better quality solutions. This approach not only inherits the characteristic of quick convergence, simplicity, and easy implementation of original bat algorithm, but also increases the diversity of population and improves the accuracy of solution. Twenty-four benchmark test functions are tested and compared with other variant bat algorithms for numerical optimization the simulation results show that this approach is simple and efficient and can achieve a more accurate solution. PMID:27293424
Global Precipitation Measurement: GPM Microwave Imager (GMI) Algorithm Development Approach
NASA Technical Reports Server (NTRS)
Stocker, Erich Franz
2009-01-01
This slide presentation reviews the approach to the development of the Global Precipitation Measurement algorithm. This presentation includes information about the responsibilities for the development of the algorithm, and the calibration. Also included is information about the orbit, and the sun angle. The test of the algorithm code will be done with synthetic data generated from the Precipitation Processing System (PPS).
An optimal control approach to probabilistic Boolean networks
NASA Astrophysics Data System (ADS)
Liu, Qiuli
2012-12-01
External control of some genes in a genetic regulatory network is useful for avoiding undesirable states associated with some diseases. For this purpose, a number of stochastic optimal control approaches have been proposed. Probabilistic Boolean networks (PBNs) as powerful tools for modeling gene regulatory systems have attracted considerable attention in systems biology. In this paper, we deal with a problem of optimal intervention in a PBN with the help of the theory of discrete time Markov decision process. Specifically, we first formulate a control model for a PBN as a first passage model for discrete time Markov decision processes and then find, using a value iteration algorithm, optimal effective treatments with the minimal expected first passage time over the space of all possible treatments. In order to demonstrate the feasibility of our approach, an example is also displayed.
Integer programming model for optimizing bus timetable using genetic algorithm
NASA Astrophysics Data System (ADS)
Wihartiko, F. D.; Buono, A.; Silalahi, B. P.
2017-01-01
Bus timetable gave an information for passengers to ensure the availability of bus services. Timetable optimal condition happened when bus trips frequency could adapt and suit with passenger demand. In the peak time, the number of bus trips would be larger than the off-peak time. If the number of bus trips were more frequent than the optimal condition, it would make a high operating cost for bus operator. Conversely, if the number of trip was less than optimal condition, it would make a bad quality service for passengers. In this paper, the bus timetabling problem would be solved by integer programming model with modified genetic algorithm. Modification was placed in the chromosomes design, initial population recovery technique, chromosomes reconstruction and chromosomes extermination on specific generation. The result of this model gave the optimal solution with accuracy 99.1%.
All-Optical Implementation of the Ant Colony Optimization Algorithm
Hu, Wenchao; Wu, Kan; Shum, Perry Ping; Zheludev, Nikolay I.; Soci, Cesare
2016-01-01
We report all-optical implementation of the optimization algorithm for the famous “ant colony” problem. Ant colonies progressively optimize pathway to food discovered by one of the ants through identifying the discovered route with volatile chemicals (pheromones) secreted on the way back from the food deposit. Mathematically this is an important example of graph optimization problem with dynamically changing parameters. Using an optical network with nonlinear waveguides to represent the graph and a feedback loop, we experimentally show that photons traveling through the network behave like ants that dynamically modify the environment to find the shortest pathway to any chosen point in the graph. This proof-of-principle demonstration illustrates how transient nonlinearity in the optical system can be exploited to tackle complex optimization problems directly, on the hardware level, which may be used for self-routing of optical signals in transparent communication networks and energy flow in photonic systems. PMID:27222098
All-Optical Implementation of the Ant Colony Optimization Algorithm
NASA Astrophysics Data System (ADS)
Hu, Wenchao; Wu, Kan; Shum, Perry Ping; Zheludev, Nikolay I.; Soci, Cesare
2016-05-01
We report all-optical implementation of the optimization algorithm for the famous “ant colony” problem. Ant colonies progressively optimize pathway to food discovered by one of the ants through identifying the discovered route with volatile chemicals (pheromones) secreted on the way back from the food deposit. Mathematically this is an important example of graph optimization problem with dynamically changing parameters. Using an optical network with nonlinear waveguides to represent the graph and a feedback loop, we experimentally show that photons traveling through the network behave like ants that dynamically modify the environment to find the shortest pathway to any chosen point in the graph. This proof-of-principle demonstration illustrates how transient nonlinearity in the optical system can be exploited to tackle complex optimization problems directly, on the hardware level, which may be used for self-routing of optical signals in transparent communication networks and energy flow in photonic systems.
A new algorithm for the robust optimization of rotor-bearing systems
NASA Astrophysics Data System (ADS)
Lopez, R. H.; Ritto, T. G.; Sampaio, Rubens; Souza de Cursi, J. E.
2014-08-01
This article presents a new algorithm for the robust optimization of rotor-bearing systems. The goal of the optimization problem is to find the values of a set of parameters for which the natural frequencies of the system are as far away as possible from the rotational speeds of the machine. To accomplish this, the penalization proposed by Ritto, Lopez, Sampaio, and Souza de Cursi in 2011 is employed. Since the rotor-bearing system is subject to uncertainties, such a penalization is modelled as a random variable. The robust optimization is performed by minimizing the expected value and variance of the penalization, resulting in a multi-objective optimization problem (MOP). The objective function of this MOP is known to be non-convex and it is shown that its resulting Pareto front (PF) is also non-convex. Thus, a new algorithm is proposed for solving the MOP: the normal boundary intersection (NBI) is employed to discretize the PF handling its non-convexity, and a global optimization algorithm based on a restart procedure and local searches are employed to minimize the NBI subproblems tackling the non-convexity of the objective function. A numerical analysis section shows the advantage of using the proposed algorithm over the weighted-sum (WS) and NSGA-II approaches. In comparison with the WS, the proposed approach obtains a much more even and useful set of Pareto points. Compared with the NSGA-II approach, the proposed algorithm provides a better approximation of the PF requiring much lower computational cost.
CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET
Bajwa, Khalid Bashir; Khan, Salabat; Chaudary, Nadeem Majeed; Akram, Adeel
2016-01-01
A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO. PMID:27149517
CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET.
Aadil, Farhan; Bajwa, Khalid Bashir; Khan, Salabat; Chaudary, Nadeem Majeed; Akram, Adeel
2016-01-01
A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO.
Random search optimization based on genetic algorithm and discriminant function
NASA Technical Reports Server (NTRS)
Kiciman, M. O.; Akgul, M.; Erarslanoglu, G.
1990-01-01
The general problem of optimization with arbitrary merit and constraint functions, which could be convex, concave, monotonic, or non-monotonic, is treated using stochastic methods. To improve the efficiency of the random search methods, a genetic algorithm for the search phase and a discriminant function for the constraint-control phase were utilized. The validity of the technique is demonstrated by comparing the results to published test problem results. Numerical experimentation indicated that for cases where a quick near optimum solution is desired, a general, user-friendly optimization code can be developed without serious penalties in both total computer time and accuracy.
Optimization of broadband semiconductor chirped mirrors with genetic algorithm
NASA Astrophysics Data System (ADS)
Dems, Maciej; Wnuk, Paweł; Wasylczyk, Piotr; Zinkiewicz, Łukasz; Wójcik-Jedlińska, Anna; Regiński, Kazimierz; Hejduk, Krzysztof; Jasik, Agata
2016-10-01
Genetic algorithm was applied for optimization of dispersion properties in semiconductor Bragg reflectors for applications in femtosecond lasers. Broadband, large negative group-delay dispersion was achieved in the optimized design: The group-delay dispersion (GDD) as large as -3500 fs2 was theoretically obtained over a 10-nm bandwidth. The designed structure was manufactured and tested, providing GDD -3320 fs2 over a 7-nm bandwidth. The mirror performance was verified in semiconductor structures grown with molecular beam epitaxy. The mirror was tested in a passively mode-locked Yb:KYW laser.
Genetic Algorithm Application in Optimization of Wireless Sensor Networks
Norouzi, Ali; Zaim, A. Halim
2014-01-01
There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235
Implementation and Optimization of Image Processing Algorithms on Embedded GPU
NASA Astrophysics Data System (ADS)
Singhal, Nitin; Yoo, Jin Woo; Choi, Ho Yeol; Park, In Kyu
In this paper, we analyze the key factors underlying the implementation, evaluation, and optimization of image processing and computer vision algorithms on embedded GPU using OpenGL ES 2.0 shader model. First, we present the characteristics of the embedded GPU and its inherent advantage when compared to embedded CPU. Additionally, we propose techniques to achieve increased performance with optimized shader design. To show the effectiveness of the proposed techniques, we employ cartoon-style non-photorealistic rendering (NPR), speeded-up robust feature (SURF) detection, and stereo matching as our example algorithms. Performance is evaluated in terms of the execution time and speed-up achieved in comparison with the implementation on embedded CPU.
New pole placement algorithm - Polynomial matrix approach
NASA Technical Reports Server (NTRS)
Shafai, B.; Keel, L. H.
1990-01-01
A simple and direct pole-placement algorithm is introduced for dynamical systems having a block companion matrix A. The algorithm utilizes well-established properties of matrix polynomials. Pole placement is achieved by appropriately assigning coefficient matrices of the corresponding matrix polynomial. This involves only matrix additions and multiplications without requiring matrix inversion. A numerical example is given for the purpose of illustration.
A hierarchical evolutionary algorithm for multiobjective optimization in IMRT
Holdsworth, Clay; Kim, Minsun; Liao, Jay; Phillips, Mark H.
2010-01-01
Purpose: The current inverse planning methods for intensity modulated radiation therapy (IMRT) are limited because they are not designed to explore the trade-offs between the competing objectives of tumor and normal tissues. The goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: A hierarchical evolutionary multiobjective algorithm designed to quickly generate a small diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the optimal trade-offs in any radiation therapy plan was developed. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. The population size is not fixed, but a specialized niche effect, domination advantage, is used to control the population and plan diversity. The number of fitness objectives is kept to a minimum for greater selective pressure, but the number of genes is expanded for flexibility that allows a better approximation of the Pareto front. Results: The MOEA improvements were evaluated for two example prostate cases with one target and two organs at risk (OARs). The population of plans generated by the modified MOEA was closer to the Pareto front than populations of plans generated using a standard genetic algorithm package. Statistical significance of the method was established by compiling the results of 25 multiobjective optimizations using each method. From these sets of 12–15 plans, any random plan selected from a MOEA
Optimizing phase-estimation algorithms for diamond spin magnetometry
NASA Astrophysics Data System (ADS)
Nusran, N. M.; Dutt, M. V. Gurudev
2014-07-01
We present a detailed theoretical and numerical study discussing the application and optimization of phase-estimation algorithms (PEAs) to diamond spin magnetometry. We compare standard Ramsey magnetometry, the nonadaptive PEA (NAPEA), and quantum PEA (QPEA) incorporating error checking. Our results show that the NAPEA requires lower measurement fidelity, has better dynamic range, and greater consistency in sensitivity. We elucidate the importance of dynamic range to Ramsey magnetic imaging with diamond spins, and introduce the application of PEAs to time-dependent magnetometry.
NASA Astrophysics Data System (ADS)
Ausaf, Muhammad Farhan; Gao, Liang; Li, Xinyu
2015-12-01
For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.
Threshold matrix for digital halftoning by genetic algorithm optimization
NASA Astrophysics Data System (ADS)
Alander, Jarmo T.; Mantere, Timo J.; Pyylampi, Tero
1998-10-01
Digital halftoning is used both in low and high resolution high quality printing technologies. Our method is designed to be mainly used for low resolution ink jet marking machines to produce both gray tone and color images. The main problem with digital halftoning is pink noise caused by the human eye's visual transfer function. To compensate for this the random dot patterns used are optimized to contain more blue than pink noise. Several such dot pattern generator threshold matrices have been created automatically by using genetic algorithm optimization, a non-deterministic global optimization method imitating natural evolution and genetics. A hybrid of genetic algorithm with a search method based on local backtracking was developed together with several fitness functions evaluating dot patterns for rectangular grids. By modifying the fitness function, a family of dot generators results, each with its particular statistical features. Several versions of genetic algorithms, backtracking and fitness functions were tested to find a reasonable combination. The generated threshold matrices have been tested by simulating a set of test images using the Khoros image processing system. Even though the work was focused on developing low resolution marking technology, the resulting family of dot generators can be applied also in other halftoning application areas including high resolution printing technology.
Learning approach to sampling optimization: Applications in astrodynamics
NASA Astrophysics Data System (ADS)
Henderson, Troy Allen
A new, novel numerical optimization algorithm is developed, tested, and used to solve difficult numerical problems from the field of astrodynamics. First, a brief review of optimization theory is presented and common numerical optimization techniques are discussed. Then, the new method, called the Learning Approach to Sampling Optimization (LA) is presented. Simple, illustrative examples are given to further emphasize the simplicity and accuracy of the LA method. Benchmark functions in lower dimensions are studied and the LA is compared, in terms of performance, to widely used methods. Three classes of problems from astrodynamics are then solved. First, the N-impulse orbit transfer and rendezvous problems are solved by using the LA optimization technique along with derived bounds that make the problem computationally feasible. This marriage between analytical and numerical methods allows an answer to be found for an order of magnitude greater number of impulses than are currently published. Next, the N-impulse work is applied to design periodic close encounters (PCE) in space. The encounters are defined as an open rendezvous, meaning that two spacecraft must be at the same position at the same time, but their velocities are not necessarily equal. The PCE work is extended to include N-impulses and other constraints, and new examples are given. Finally, a trajectory optimization problem is solved using the LA algorithm and comparing performance with other methods based on two models---with varying complexity---of the Cassini-Huygens mission to Saturn. The results show that the LA consistently outperforms commonly used numerical optimization algorithms.
Applying Genetic Algorithms To Query Optimization in Document Retrieval.
ERIC Educational Resources Information Center
Horng, Jorng-Tzong; Yeh, Ching-Chang
2000-01-01
Proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Discusses Chinese text retrieval, term frequency rating formulas, vector space models, bigrams, the PAT-tree structure for information retrieval, query vectors, and relevance feedback. (Author/LRW)
Freier, Lars; von Lieres, Eric
2016-12-23
Biotechnological separation processes are routinely designed and optimized using parallel high-throughput experiments and/or serial experiments. Well-characterized processes can further be optimized using mechanistic models. In all these cases - serial/parallel experiments and modeling - iterative strategies are customarily applied for planning novel experiments/simulations based on the previously acquired knowledge. Process optimization is typically complicated by conflicting design targets, such as productivity and yield. We address these issues by introducing a novel algorithm that combines recently developed approaches for utilizing statistical regression models in multi-objective optimization. The proposed algorithm is demonstrated by simultaneous optimization of elution gradient and pooling strategy for chromatographic separation of a three-component system with respect to purity, yield, and processing time. Gaussian Process Regression Models (GPM) are used for estimating functional relationships between design variables (gradient, pooling) and performance indicators (purity, yield, time). The Pareto front is iteratively approximated by planning new experiments such as to maximize the Expected Hypervolume Improvement (EHVI) as determined from the GPM by Markov Chain Monte Carlo (MCMC) sampling. A comprehensive Monte-Carlo study with in-silico data illustrates efficiency, effectiveness and robustness of the presented Multi-Objective Global Optimization (MOGO) algorithm in determining best compromises between conflicting objectives with comparably very low experimental effort.
Quantum-based algorithm for optimizing artificial neural networks.
Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang
2013-08-01
This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.
NASA Astrophysics Data System (ADS)
López-Medina, Mario E.; Vázquez-Montiel, Sergio; Herrera-Vázquez, Joel
2008-04-01
The Genetic Algorithms, GAs, are a method of global optimization that we use in the stage of optimization in the design of optical systems. In the case of optical design and optimization, the efficiency and convergence speed of GAs are related with merit function, crossover operator, and mutation operator. In this study we present a comparison between several genetic algorithms implementations using different optical systems, like achromatic cemented doublet, air spaced doublet and telescopes. We do the comparison varying the type of design parameters and the number of parameters to be optimized. We also implement the GAs using discreet parameters with binary chains and with continuous parameter using real numbers in the chromosome; analyzing the differences in the time taken to find the solution and the precision in the results between discreet and continuous parameters. Additionally, we use different merit function to optimize the same optical system. We present the obtained results in tables, graphics and a detailed example; and of the comparison we conclude which is the best way to implement GAs for design and optimization optical system. The programs developed for this work were made using the C programming language and OSLO for the simulation of the optical systems.
Chaos Time Series Prediction Based on Membrane Optimization Algorithms
Li, Meng; Yi, Liangzhong; Pei, Zheng; Gao, Zhisheng
2015-01-01
This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ, m) and least squares support vector machine (LS-SVM) (γ, σ) by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM) broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). PMID:25874249
Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao
2014-09-01
Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.
Resistive Network Optimal Power Flow: Uniqueness and Algorithms
Tan, CW; Cai, DWH; Lou, X
2015-01-01
The optimal power flow (OPF) problem minimizes the power loss in an electrical network by optimizing the voltage and power delivered at the network buses, and is a nonconvex problem that is generally hard to solve. By leveraging a recent development on the zero duality gap of OPF, we propose a second-order cone programming convex relaxation of the resistive network OPF, and study the uniqueness of the optimal solution using differential topology, especially the Poincare-Hopf Index Theorem. We characterize the global uniqueness for different network topologies, e.g., line, radial, and mesh networks. This serves as a starting point to design distributed local algorithms with global behaviors that have low complexity, are computationally fast, and can run under synchronous and asynchronous settings in practical power grids.
Shan, Hai; Yasuda, Toshiyuki; Ohkura, Kazuhiro
2015-06-01
The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively.
A Genetic Algorithm Approach to Nonlinear Least Squares Estimation
ERIC Educational Resources Information Center
Olinsky, Alan D.; Quinn, John T.; Mangiameli, Paul M.; Chen, Shaw K.
2004-01-01
A common type of problem encountered in mathematics is optimizing nonlinear functions. Many popular algorithms that are currently available for finding nonlinear least squares estimators, a special class of nonlinear problems, are sometimes inadequate. They might not converge to an optimal value, or if they do, it could be to a local rather than…
Coil optimization for electromagnetic levitation using a genetic like algorithm
NASA Astrophysics Data System (ADS)
Royer, Z. L.; Tackes, C.; LeSar, R.; Napolitano, R. E.
2013-06-01
The technique of electromagnetic levitation (EML) provides a means for thermally processing an electrically conductive specimen in a containerless manner. For the investigation of metallic liquids and related melting or freezing transformations, the elimination of substrate-induced nucleation affords access to much higher undercooling than otherwise attainable. With heating and levitation both arising from the currents induced by the coil, the performance of any EML system depends on controlling the balance between lifting forces and heating effects, as influenced by the levitation coil geometry. In this work, a genetic algorithm is developed and utilized to optimize the design of electromagnetic levitation coils. The optimization is targeted specifically to reduce the steady-state temperature of the stably levitated metallic specimen. Reductions in temperature of nominally 70 K relative to that obtained with the initial design are achieved through coil optimization, and the results are compared with experiments for aluminum. Additionally, the optimization method is shown to be robust, generating a small range of converged results from a variety of initial starting conditions. While our optimization criterion was set to achieve the lowest possible sample temperature, the method is general and can be used to optimize for other criteria as well.
A heterogeneous algorithm for PDT dose optimization for prostate
NASA Astrophysics Data System (ADS)
Altschuler, Martin D.; Zhu, Timothy C.; Hu, Yida; Finlay, Jarod C.; Dimofte, Andreea; Wang, Ken; Li, Jun; Cengel, Keith; Malkowicz, S. B.; Hahn, Stephen M.
2009-02-01
The object of this study is to develop optimization procedures that account for both the optical heterogeneity as well as photosensitizer (PS) drug distribution of the patient prostate and thereby enable delivery of uniform photodynamic dose to that gland. We use the heterogeneous optical properties measured for a patient prostate to calculate a light fluence kernel (table). PS distribution is then multiplied with the light fluence kernel to form the PDT dose kernel. The Cimmino feasibility algorithm, which is fast, linear, and always converges reliably, is applied as a search tool to choose the weights of the light sources to optimize PDT dose. Maximum and minimum PDT dose limits chosen for sample points in the prostate constrain the solution for the source strengths of the cylindrical diffuser fibers (CDF). We tested the Cimmino optimization procedures using the light fluence kernel generated for heterogeneous optical properties, and compared the optimized treatment plans with those obtained using homogeneous optical properties. To study how different photosensitizer distributions in the prostate affect optimization, comparisons of light fluence rate and PDT dose distributions were made with three distributions of photosensitizer: uniform, linear spatial distribution, and the measured PS distribution. The study shows that optimization of individual light source positions and intensities are feasible for the heterogeneous prostate during PDT.
A heterogeneous algorithm for PDT dose optimization for prostate
Altschuler, Martin D.; Zhu, Timothy C.; Hu, Yida; Finlay, Jarod C.; Dimofte, Andreea; Wang, Ken; Li, Jun; Cengel, Keith; Malkowicz, S.B.; Hahn, Stephen M.
2015-01-01
The object of this study is to develop optimization procedures that account for both the optical heterogeneity as well as photosensitizer (PS) drug distribution of the patient prostate and thereby enable delivery of uniform photodynamic dose to that gland. We use the heterogeneous optical properties measured for a patient prostate to calculate a light fluence kernel (table). PS distribution is then multiplied with the light fluence kernel to form the PDT dose kernel. The Cimmino feasibility algorithm, which is fast, linear, and always converges reliably, is applied as a search tool to choose the weights of the light sources to optimize PDT dose. Maximum and minimum PDT dose limits chosen for sample points in the prostate constrain the solution for the source strengths of the cylindrical diffuser fibers (CDF). We tested the Cimmino optimization procedures using the light fluence kernel generated for heterogeneous optical properties, and compared the optimized treatment plans with those obtained using homogeneous optical properties. To study how different photosensitizer distributions in the prostate affect optimization, comparisons of light fluence rate and PDT dose distributions were made with three distributions of photosensitizer: uniform, linear spatial distribution, and the measured PS distribution. The study shows that optimization of individual light source positions and intensities are feasible for the heterogeneous prostate during PDT. PMID:25914793
Zhang, Yan-jun; Zhang, Shu-guo; Fu, Guang-wei; Li, Da; Liu, Yin; Bi, Wei-hong
2012-04-01
This paper presents a novel algorithm which blends optimize particle swarm optimization (PSO) algorithm and Levenberg-Marquardt (LM) algorithm according to the probability. This novel algorithm can be used for Pseudo-Voigt type of Brillouin scattering spectrum to improve the degree of fitting and precision of shift extraction. This algorithm uses PSO algorithm as the main frame. First, PSO algorithm is used in global search, after a certain number of optimization every time there generates a random probability rand (0, 1). If rand (0, 1) is less than or equal to the predetermined probability P, the optimal solution obtained by PSO algorithm will be used as the initial value of LM algorithm. Then LM algorithm is used in local depth search and the solution of LM algorithm is used to replace the previous PSO algorithm for optimal solutions. Again the PSO algorithm is used for global search. If rand (0, 1) was greater than P, PSO algorithm is still used in search, waiting the next optimization to generate random probability rand (0, 1) to judge. Two kinds of algorithms are alternatively used to obtain ideal global optimal solution. Simulation analysis and experimental results show that the new algorithm overcomes the shortcomings of single algorithm and improves the degree of fitting and precision of frequency shift extraction in Brillouin scattering spectrum, and fully prove that the new method is practical and feasible.
An implementable algorithm for the optimal design centering, tolerancing, and tuning problem
Polak, E.
1982-05-01
An implementable master algorithm for solving optimal design centering, tolerancing, and tuning problems is presented. This master algorithm decomposes the original nondifferentiable optimization problem into a sequence of ordinary nonlinear programming problems. The master algorithm generates sequences with accumulation points that are feasible and satisfy a new optimality condition, which is shown to be stronger than the one previously used for these problems.
NASA Astrophysics Data System (ADS)
Kiesewetter, Simon; Drummond, Peter D.
2017-03-01
A variance reduction method for stochastic integration of Fokker-Planck equations is derived. This unifies the cumulant hierarchy and stochastic equation approaches to obtaining moments, giving a performance superior to either. We show that the brute force method of reducing sampling error by just using more trajectories in a sampled stochastic equation is not the best approach. The alternative of using a hierarchy of moment equations is also not optimal, as it may converge to erroneous answers. Instead, through Bayesian conditioning of the stochastic noise on the requirement that moment equations are satisfied, we obtain improved results with reduced sampling errors for a given number of stochastic trajectories. The method used here converges faster in time-step than Ito-Euler algorithms. This parallel optimized sampling (POS) algorithm is illustrated by several examples, including a bistable nonlinear oscillator case where moment hierarchies fail to converge.
Hybrid binary GA-EDA algorithms for complex “black-box” optimization problems
NASA Astrophysics Data System (ADS)
Sopov, E.
2017-02-01
Genetic Algorithms (GAs) have proved their efficiency solving many complex optimization problems. GAs can be also applied for “black-box” problems, because they realize the “blind” search and do not require any specific information about features of search space and objectives. It is clear that a GA uses the “Trial-and-Error” strategy to explorer search space, and collects some statistical information that is stored in the form of genes in the population. Estimation of Distribution Algorithms (EDA) have very similar realization as GAs, but use an explicit representation of search experience in the form of the statistical probabilities distribution. In this study we discus some approaches for improving the standard GA performance by combining the binary GA with EDA. Finally, a novel approach for the large-scale global optimization is proposed. The experimental results and comparison with some well-studied techniques are presented and discussed.
A non-linear camera calibration with modified teaching-learning-based optimization algorithm
NASA Astrophysics Data System (ADS)
Zhang, Buyang; Yang, Hua; Yang, Shuo
2015-12-01
In this paper, we put forward a novel approach based on hierarchical teaching-and-learning-based optimization (HTLBO) algorithm for nonlinear camera calibration. This algorithm simulates the teaching-learning ability of teachers and learners of a classroom. Different from traditional calibration approach, the proposed technique can find the nearoptimal solution without the need of accurate initial parameters estimation (with only very loose parameter bounds). With the introduction of cascade of teaching, the convergence speed is rapid and the global search ability is improved. Results from our study demonstrate the excellent performance of the proposed technique in terms of convergence, accuracy, and robustness. The HTLBO can also be used to solve many other complex non-linear calibration optimization problems for its good portability.
Multi-objective optimization with estimation of distribution algorithm in a noisy environment.
Shim, Vui Ann; Tan, Kay Chen; Chia, Jun Yong; Al Mamun, Abdullah
2013-01-01
Many real-world optimization problems are subjected to uncertainties that may be characterized by the presence of noise in the objective functions. The estimation of distribution algorithm (EDA), which models the global distribution of the population for searching tasks, is one of the evolutionary computation techniques that deals with noisy information. This paper studies the potential of EDAs; particularly an EDA based on restricted Boltzmann machines that handles multi-objective optimization problems in a noisy environment. Noise is introduced to the objective functions in the form of a Gaussian distribution. In order to reduce the detrimental effect of noise, a likelihood correction feature is proposed to tune the marginal probability distribution of each decision variable. The EDA is subsequently hybridized with a particle swarm optimization algorithm in a discrete domain to improve its search ability. The effectiveness of the proposed algorithm is examined via eight benchmark instances with different characteristics and shapes of the Pareto optimal front. The scalability, hybridization, and computational time are rigorously studied. Comparative studies show that the proposed approach outperforms other state of the art algorithms.
Optimization design of satellite separation systems based on Multi-Island Genetic Algorithm
NASA Astrophysics Data System (ADS)
Hu, Xingzhi; Chen, Xiaoqian; Zhao, Yong; Yao, Wen
2014-03-01
The separation systems are crucial for the launch of satellites. With respect to the existing design issues of satellite separation systems, an optimization design approach based on Multi-Island Genetic Algorithm is proposed, and a hierarchical optimization of system mass and separation angular velocity is designed. Multi-Island Genetic Algorithm is studied for the problem and the optimization parameters are discussed. Dynamic analysis of ADAMS used to validate the designs is integrated with iSIGHT. Then the optimization method is employed for a typical problem using the helical compression spring mechanism, and the corresponding objective functions are derived. It turns out that the mass of compression spring catapult is decreased by 30.7% after optimization and the angular velocity can be minimized considering spring stiffness errors. Moreover, ground tests and on-orbit flight indicate that the error of separation speed is controlled within 1% and the angular velocity is reduced by nearly 90%, which proves the design result and the optimization approach.
NASA Astrophysics Data System (ADS)
Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Norman, R.; Yuan, Y. B.; Fritzer, J.; Schwaerz, M.; Zhang, K.
2015-08-01
We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS)-based radio occultation (RO) measurements. The new algorithm estimates background and observation error covariance matrices with geographically varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAllenging Minisatellite Payload (CHAMP) and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant reduction of random errors (standard deviations) of optimized bending angles down to about half of their size or more; (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3) improved retrieval of refractivity and temperature profiles; and (4) realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well-characterized and high-quality atmospheric profiles over the entire stratosphere.
NASA Astrophysics Data System (ADS)
Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Norman, R.; Yuan, Y. B.; Fritzer, J.; Schwaerz, M.; Zhang, K.
2015-01-01
We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS) based radio occultation (RO) measurements. The new algorithm estimates background and observation error covariance matrices with geographically-varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAMP and COSMIC measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant reduction in random errors (standard deviations) of optimized bending angles down to about two-thirds of their size or more; (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3) improved retrieval of refractivity and temperature profiles; (4) produces realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well characterized and high-quality atmospheric profiles over the entire stratosphere.
Sizing of complex structure by the integration of several different optimal design algorithms
NASA Technical Reports Server (NTRS)
Sobieszczanski, J.
1974-01-01
Practical design of large-scale structures can be accomplished with the aid of the digital computer by bringing together in one computer program algorithms of nonlinear mathematical programing and optimality criteria with weight-strength and other so-called engineering methods. Applications of this approach to aviation structures are discussed with a detailed description of how the total problem of structural sizing can be broken down into subproblems for best utilization of each algorithm and for efficient organization of the program into iterative loops. Typical results are examined for a number of examples.
Optimal fractional delay-IIR filter design using cuckoo search algorithm.
Kumar, Manjeet; Rawat, Tarun Kumar
2015-11-01
This paper applied a novel global meta-heuristic optimization algorithm, cuckoo search algorithm (CSA) to determine optimal coefficients of a fractional delay-infinite impulse response (FD-IIR) filter and trying to meet the ideal frequency response characteristics. Since fractional delay-IIR filter design is a multi-modal optimization problem, it cannot be computed efficiently using conventional gradient based optimization techniques. A weighted least square (WLS) based fitness function is used to improve the performance to a great extent. FD-IIR filters of different orders have been designed using the CSA. The simulation results of the proposed CSA based approach have been compared to those of well accepted evolutionary algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performance of the CSA based FD-IIR filter is superior to those obtained by GA and PSO. The simulation and statistical results affirm that the proposed approach using CSA outperforms GA and PSO, not only in the convergence rate but also in optimal performance of the designed FD-IIR filter (i.e., smaller magnitude error, smaller phase error, higher percentage improvement in magnitude and phase error, fast convergence rate). The absolute magnitude and phase error obtained for the designed 5th order FD-IIR filter are as low as 0.0037 and 0.0046, respectively. The percentage improvement in magnitude error for CSA based 5th order FD-IIR design with respect to GA and PSO are 80.93% and 74.83% respectively, and phase error are 76.04% and 71.25%, respectively.
A multiple objective optimization approach to quality control
NASA Technical Reports Server (NTRS)
Seaman, Christopher Michael
1991-01-01
The use of product quality as the performance criteria for manufacturing system control is explored. The goal in manufacturing, for economic reasons, is to optimize product quality. The problem is that since quality is a rather nebulous product characteristic, there is seldom an analytic function that can be used as a measure. Therefore standard control approaches, such as optimal control, cannot readily be applied. A second problem with optimizing product quality is that it is typically measured along many dimensions: there are many apsects of quality which must be optimized simultaneously. Very often these different aspects are incommensurate and competing. The concept of optimality must now include accepting tradeoffs among the different quality characteristics. These problems are addressed using multiple objective optimization. It is shown that the quality control problem can be defined as a multiple objective optimization problem. A controller structure is defined using this as the basis. Then, an algorithm is presented which can be used by an operator to interactively find the best operating point. Essentially, the algorithm uses process data to provide the operator with two pieces of information: (1) if it is possible to simultaneously improve all quality criteria, then determine what changes to the process input or controller parameters should be made to do this; and (2) if it is not possible to improve all criteria, and the current operating point is not a desirable one, select a criteria in which a tradeoff should be made, and make input changes to improve all other criteria. The process is not operating at an optimal point in any sense if no tradeoff has to be made to move to a new operating point. This algorithm ensures that operating points are optimal in some sense and provides the operator with information about tradeoffs when seeking the best operating point. The multiobjective algorithm was implemented in two different injection molding scenarios
Efficient and scalable Pareto optimization by evolutionary local selection algorithms.
Menczer, F; Degeratu, M; Street, W N
2000-01-01
Local selection is a simple selection scheme in evolutionary computation. Individual fitnesses are accumulated over time and compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. Local selection, coupled with fitness functions stemming from the consumption of finite shared environmental resources, maintains diversity in a way similar to fitness sharing. However, it is more efficient than fitness sharing and lends itself to parallel implementations for distributed tasks. While local selection is not prone to premature convergence, it applies minimal selection pressure to the population. Local selection is, therefore, particularly suited to Pareto optimization or problem classes where diverse solutions must be covered. This paper introduces ELSA, an evolutionary algorithm employing local selection and outlines three experiments in which ELSA is applied to multiobjective problems: a multimodal graph search problem, and two Pareto optimization problems. In all these experiments, ELSA significantly outperforms other well-known evolutionary algorithms. The paper also discusses scalability, parameter dependence, and the potential distributed applications of the algorithm.
Perspectives on optimization under uncertainty: Algorithms and applications.
Swiler, Laura Painton; Wojtkiewicz, Steven F., Jr.; Eldred, Michael Scott; Giunta, Anthony Andrew; Trucano, Timothy Guy
2005-12-01
This paper provides an overview of several approaches to formulating and solving optimization under uncertainty (OUU) engineering design problems. In addition, the topic of high-performance computing and OUU is addressed, with a discussion of the coarse- and fine-grained parallel computing opportunities in the various OUU problem formulations. The OUU approaches covered here are: sampling-based OUU, surrogate model-based OUU, analytic reliability-based OUU (also known as reliability-based design optimization), polynomial chaos-based OUU, and stochastic perturbation-based OUU.
Robustness, generality and efficiency of optimization algorithms in practical applications
NASA Technical Reports Server (NTRS)
Thanedar, P. B.; Arora, J. S.; Li, G. Y.; Lin, T. C.
1990-01-01
The theoretical foundations of two approaches, sequential quadratic programming (SQP) and optimality criteria (OC), are analyzed and compared, with emphasis on the critical importance of parameters such as accuracy, generality, robustness, efficiency, and ease of use in large scale structural optimization. A simplified fighter wing and active control of space structures are considered with other example problems. When applied to general system identification problems, the OC methods are shown to lose simplicity and demonstrate lack of generality, accuracy and robustness. It is concluded that the SQP method with a potential constraint strategy is a better choice as compared to the currently prevalent mathematical programming and OC approaches.
Forging tool shape optimization using pseudo inverse approach and adaptive incremental approach
NASA Astrophysics Data System (ADS)
Halouani, A.; Meng, F. J.; Li, Y. M.; Labergère, C.; Abbès, B.; Lafon, P.; Guo, Y. Q.
2013-05-01
This paper presents a simplified finite element method called "Pseudo Inverse Approach" (PIA) for tool shape design and optimization in multi-step cold forging processes. The approach is based on the knowledge of the final part shape. Some intermediate configurations are introduced and corrected by using a free surface method to consider the deformation paths without contact treatment. A robust direct algorithm of plasticity is implemented by using the equivalent stress notion and tensile curve. Numerical tests have shown that the PIA is very fast compared to the incremental approach. The PIA is used in an optimization procedure to automatically design the shapes of the preform tools. Our objective is to find the optimal preforms which minimize the equivalent plastic strain and punch force. The preform shapes are defined by B-Spline curves. A simulated annealing algorithm is adopted for the optimization procedure. The forging results obtained by the PIA are compared to those obtained by the incremental approach to show the efficiency and accuracy of the PIA.
Quantum Resonance Approach to Combinatorial Optimization
NASA Technical Reports Server (NTRS)
Zak, Michail
1997-01-01
It is shown that quantum resonance can be used for combinatorial optimization. The advantage of the approach is in independence of the computing time upon the dimensionality of the problem. As an example, the solution to a constraint satisfaction problem of exponential complexity is demonstrated.
The Optimal Treatment Approach to Needs Assessment.
ERIC Educational Resources Information Center
Cox, Gary B.; And Others
1979-01-01
The Optimal Treatment approach to needs assessment consists of comparing the most desirable set of services for a client with the services actually received. Discrepancies due to unavailable resources are noted and aggregated across clients. Advantages and disadvantages of this and other needs assessment procedures are considered. (Author/RL)
Genetic Algorithm Optimized Triply Compensated Pulses in NMR Spectroscopy
Manu, V. S.; Veglia, Gianluigi
2015-01-01
Sensitivity and resolution in NMR experiments are affected by magnetic field inhomogeneities (of both external and RF), errors in pulse calibration, and offset effects due to finite length of RF pulses. To remedy these problems, built-in compensation mechanisms for these experimental imperfections are often necessary. Here, we propose a new family of phase-modulated constant-amplitude broadband pulses with high compensation for RF inhomogeneity and heteronuclear coupling evolution. These pulses were optimized using a genetic algorithm (GA), which consists in a global optimization method inspired by Nature’s evolutionary processes. The newly designed π and π/2 pulses belong to the ‘Type A’ (or general rotors) symmetric composite pulses. These GA-optimized pulses are relatively short compared to other general rotors and can be used for excitation and inversion, as well as refocusing pulses in spin-echo experiments. The performance of the GA-optimized pulses was assessed in Magic Angle Spinning (MAS) solid-state NMR experiments using a crystalline U – 13C, 15N NAVL peptide as well as U – 13C, 15N microcrystalline ubiquitin. GA optimization of NMR pulse sequences opens a window for improving current experiments and designing new robust pulse sequences. PMID:26473327
Genetic algorithm optimized triply compensated pulses in NMR spectroscopy.
Manu, V S; Veglia, Gianluigi
2015-11-01
Sensitivity and resolution in NMR experiments are affected by magnetic field inhomogeneities (of both external and RF), errors in pulse calibration, and offset effects due to finite length of RF pulses. To remedy these problems, built-in compensation mechanisms for these experimental imperfections are often necessary. Here, we propose a new family of phase-modulated constant-amplitude broadband pulses with high compensation for RF inhomogeneity and heteronuclear coupling evolution. These pulses were optimized using a genetic algorithm (GA), which consists in a global optimization method inspired by Nature's evolutionary processes. The newly designed π and π/2 pulses belong to the 'type A' (or general rotors) symmetric composite pulses. These GA-optimized pulses are relatively short compared to other general rotors and can be used for excitation and inversion, as well as refocusing pulses in spin-echo experiments. The performance of the GA-optimized pulses was assessed in Magic Angle Spinning (MAS) solid-state NMR experiments using a crystalline U-(13)C, (15)N NAVL peptide as well as U-(13)C, (15)N microcrystalline ubiquitin. GA optimization of NMR pulse sequences opens a window for improving current experiments and designing new robust pulse sequences.
Multivariable optimization of liquid rocket engines using particle swarm algorithms
NASA Astrophysics Data System (ADS)
Jones, Daniel Ray
Liquid rocket engines are highly reliable, controllable, and efficient compared to other conventional forms of rocket propulsion. As such, they have seen wide use in the space industry and have become the standard propulsion system for launch vehicles, orbit insertion, and orbital maneuvering. Though these systems are well understood, historical optimization techniques are often inadequate due to the highly non-linear nature of the engine performance problem. In this thesis, a Particle Swarm Optimization (PSO) variant was applied to maximize the specific impulse of a finite-area combustion chamber (FAC) equilibrium flow rocket performance model by controlling the engine's oxidizer-to-fuel ratio and de Laval nozzle expansion and contraction ratios. In addition to the PSO-controlled parameters, engine performance was calculated based on propellant chemistry, combustion chamber pressure, and ambient pressure, which are provided as inputs to the program. The performance code was validated by comparison with NASA's Chemical Equilibrium with Applications (CEA) and the commercially available Rocket Propulsion Analysis (RPA) tool. Similarly, the PSO algorithm was validated by comparison with brute-force optimization, which calculates all possible solutions and subsequently determines which is the optimum. Particle Swarm Optimization was shown to be an effective optimizer capable of quick and reliable convergence for complex functions of multiple non-linear variables.
A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm
Wan, Youchuan; Ye, Zhiwei
2016-01-01
Texture image classification is an important topic in many applications in machine vision and image analysis. Texture feature extracted from the original texture image by using “Tuned” mask is one of the simplest and most effective methods. However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) easily fall into the local optimum. A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper. In the proposed approach, “Tuned” mask is viewed as a constrained optimization problem and the optimal “Tuned” mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA). The optimal “Tuned” mask is achieved through the convergence of GSA. The proposed approach has been, respectively, tested on some public texture and remote sensing images. The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO), and artificial immune algorithm (AIA). Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison. Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy. PMID:28090204
An optimal algorithm for computing all subtree repeats in trees
Flouri, T.; Kobert, K.; Pissis, S. P.; Stamatakis, A.
2014-01-01
Given a labelled tree T, our goal is to group repeating subtrees of T into equivalence classes with respect to their topologies and the node labels. We present an explicit, simple and time-optimal algorithm for solving this problem for unrooted unordered labelled trees and show that the running time of our method is linear with respect to the size of T. By unordered, we mean that the order of the adjacent nodes (children/neighbours) of any node of T is irrelevant. An unrooted tree T does not have a node that is designated as root and can also be referred to as an undirected tree. We show how the presented algorithm can easily be modified to operate on trees that do not satisfy some or any of the aforementioned assumptions on the tree structure; for instance, how it can be applied to rooted, ordered or unlabelled trees. PMID:24751873
Multidisciplinary Design, Analysis, and Optimization Tool Development using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Pak, Chan-gi; Li, Wesley
2008-01-01
Multidisciplinary design, analysis, and optimization using a genetic algorithm is being developed at the National Aeronautics and Space A dministration Dryden Flight Research Center to automate analysis and design process by leveraging existing tools such as NASTRAN, ZAERO a nd CFD codes to enable true multidisciplinary optimization in the pr eliminary design stage of subsonic, transonic, supersonic, and hypers onic aircraft. This is a promising technology, but faces many challe nges in large-scale, real-world application. This paper describes cur rent approaches, recent results, and challenges for MDAO as demonstr ated by our experience with the Ikhana fire pod design.
Application of an evolutionary algorithm in the optimal design of micro-sensor.
Lu, Qibing; Wang, Pan; Guo, Sihai; Sheng, Buyun; Liu, Xingxing; Fan, Zhun
2015-01-01
This paper introduces an automatic bond graph design method based on genetic programming for the evolutionary design of micro-resonator. First, the system-level behavioral model is discussed, which based on genetic programming and bond graph. Then, the geometry parameters of components are automatically optimized, by using the genetic algorithm with constraints. To illustrate this approach, a typical device micro-resonator is designed as an example in biomedicine. This paper provides a new idea for the automatic optimization design of biomedical sensors by evolutionary calculation.
Robust Optimization Design Algorithm for High-Frequency TWTs
NASA Technical Reports Server (NTRS)
Wilson, Jeffrey D.; Chevalier, Christine T.
2010-01-01
Traveling-wave tubes (TWTs), such as the Ka-band (26-GHz) model recently developed for the Lunar Reconnaissance Orbiter, are essential as communication amplifiers in spacecraft for virtually all near- and deep-space missions. This innovation is a computational design algorithm that, for the first time, optimizes the efficiency and output power of a TWT while taking into account the effects of dimensional tolerance variations. Because they are primary power consumers and power generation is very expensive in space, much effort has been exerted over the last 30 years to increase the power efficiency of TWTs. However, at frequencies higher than about 60 GHz, efficiencies of TWTs are still quite low. A major reason is that at higher frequencies, dimensional tolerance variations from conventional micromachining techniques become relatively large with respect to the circuit dimensions. When this is the case, conventional design- optimization procedures, which ignore dimensional variations, provide inaccurate designs for which the actual amplifier performance substantially under-performs that of the design. Thus, this new, robust TWT optimization design algorithm was created to take account of and ameliorate the deleterious effects of dimensional variations and to increase efficiency, power, and yield of high-frequency TWTs. This design algorithm can help extend the use of TWTs into the terahertz frequency regime of 300-3000 GHz. Currently, these frequencies are under-utilized because of the lack of efficient amplifiers, thus this regime is known as the "terahertz gap." The development of an efficient terahertz TWT amplifier could enable breakthrough applications in space science molecular spectroscopy, remote sensing, nondestructive testing, high-resolution "through-the-wall" imaging, biomedical imaging, and detection of explosives and toxic biochemical agents.
Constrained nonlinear optimization approaches to color-signal separation.
Chang, P R; Hsieh, T H
1995-01-01
Separating a color signal into illumination and surface reflectance components is a fundamental issue in color reproduction and constancy. This can be carried out by minimizing the error in the least squares (LS) fit of the product of the illumination and the surface spectral reflectance to the actual color signal. When taking in account the physical realizability constraints on the surface reflectance and illumination, the feasible solutions to the nonlinear LS problem should satisfy a number of linear inequalities. Four distinct novel optimization algorithms are presented to employ these constraints to minimize the nonlinear LS fitting error. The first approach, which is based on Ritter's superlinear convergent method (Luengerger, 1980), provides a computationally superior algorithm to find the minimum solution to the nonlinear LS error problem subject to linear inequality constraints. Unfortunately, this gradient-like algorithm may sometimes be trapped at a local minimum or become unstable when the parameters involved in the algorithm are not tuned properly. The remaining three methods are based on the stable and promising global minimizer called simulated annealing. The annealing algorithm can always find the global minimum solution with probability one, but its convergence is slow. To tackle this, a cost-effective variable-separable formulation based on the concept of Golub and Pereyra (1973) is adopted to reduce the nonlinear LS problem to be a small-scale nonlinear LS problem. The computational efficiency can be further improved when the original Boltzman generating distribution of the classical annealing is replaced by the Cauchy distribution.
NASA Astrophysics Data System (ADS)
Langton, John T.; Caroli, Joseph A.; Rosenberg, Brad
2008-04-01
To support an Effects Based Approach to Operations (EBAO), Intelligence, Surveillance, and Reconnaissance (ISR) planners must optimize collection plans within an evolving battlespace. A need exists for a decision support tool that allows ISR planners to rapidly generate and rehearse high-performing ISR plans that balance multiple objectives and constraints to address dynamic collection requirements for assessment. To meet this need we have designed an evolutionary algorithm (EA)-based "Integrated ISR Plan Analysis and Rehearsal System" (I2PARS) to support Effects-based Assessment (EBA). I2PARS supports ISR mission planning and dynamic replanning to coordinate assets and optimize their routes, allocation and tasking. It uses an evolutionary algorithm to address the large parametric space of route-finding problems which is sometimes discontinuous in the ISR domain because of conflicting objectives such as minimizing asset utilization yet maximizing ISR coverage. EAs are uniquely suited for generating solutions in dynamic environments and also allow user feedback. They are therefore ideal for "streaming optimization" and dynamic replanning of ISR mission plans. I2PARS uses the Non-dominated Sorting Genetic Algorithm (NSGA-II) to automatically generate a diverse set of high performing collection plans given multiple objectives, constraints, and assets. Intended end users of I2PARS include ISR planners in the Combined Air Operations Centers and Joint Intelligence Centers. Here we show the feasibility of applying the NSGA-II algorithm and EAs in general to the ISR planning domain. Unique genetic representations and operators for optimization within the ISR domain are presented along with multi-objective optimization criteria for ISR planning. Promising results of the I2PARS architecture design, early software prototype, and limited domain testing of the new algorithm are discussed. We also present plans for future research and development, as well as technology
Control optimization, stabilization and computer algorithms for aircraft applications
NASA Technical Reports Server (NTRS)
Athans, M. (Editor); Willsky, A. S. (Editor)
1982-01-01
The analysis and design of complex multivariable reliable control systems are considered. High performance and fault tolerant aircraft systems are the objectives. A preliminary feasibility study of the design of a lateral control system for a VTOL aircraft that is to land on a DD963 class destroyer under high sea state conditions is provided. Progress in the following areas is summarized: (1) VTOL control system design studies; (2) robust multivariable control system synthesis; (3) adaptive control systems; (4) failure detection algorithms; and (5) fault tolerant optimal control theory.
Population Induced Instabilities in Genetic Algorithms for Constrained Optimization
NASA Astrophysics Data System (ADS)
Vlachos, D. S.; Parousis-Orthodoxou, K. J.
2013-02-01
Evolutionary computation techniques, like genetic algorithms, have received a lot of attention as optimization techniques but, although they exhibit a very promising potential in curing the problem, they have not produced a significant breakthrough in the area of systematic treatment of constraints. There are two mainly ways of handling the constraints: the first is to produce an infeasibility measure and add it to the general cost function (the well known penalty methods) and the other is to modify the mutation and crossover operation in a way that they only produce feasible members. Both methods have their drawbacks and are strongly correlated to the problem that they are applied. In this work, we propose a different treatment of the constraints: we induce instabilities in the evolving population, in a way that infeasible solution cannot survive as they are. Preliminary results are presented in a set of well known from the literature constrained optimization problems.
Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster
NASA Technical Reports Server (NTRS)
Story, George
2014-01-01
Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and later on solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. A remaining issue is the cost of hybrids vs the existing launch propulsion systems. This paper will review the known state of the art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.
Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster
NASA Technical Reports Server (NTRS)
Story, George
2015-01-01
Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. One remaining issue is the cost of hybrids versus the existing launch propulsion systems. This paper will review the known state-of-the-art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.
NASA Astrophysics Data System (ADS)
Singh, R.; Verma, H. K.
2013-12-01
This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.
Optimal field-scale groundwater remediation using neural networks and the genetic algorithm
Rogers, L.L.; Dowla, F.U.; Johnson, V.M.
1993-05-01
We present a new approach for field-scale nonlinear management of groundwater remediation. First, an artificial neural network (ANN) is trained to predict the outcome of a groundwater transport simulation. Then a genetic algorithm (GA) searches through possible pumping realizations, evaluating the fitness of each with a prediction from the trained ANN. Traditional approaches rely on optimization algorithms requiring sequential calls of the groundwater transport simulation. Our approach processes the transport simulations in parallel and ``recycles`` the knowledge base of these simulations, greatly reducing the computational and real-time burden, often the primary impediment to developing field-scale management models. We present results from a Superfund site suggesting that such management techniques can reduce cleanup costs by over a hundred million dollars.
An algorithm for solving the system-level problem in multilevel optimization
NASA Technical Reports Server (NTRS)
Balling, R. J.; Sobieszczanski-Sobieski, J.
1994-01-01
A multilevel optimization approach which is applicable to nonhierarchic coupled systems is presented. The approach includes a general treatment of design (or behavior) constraints and coupling constraints at the discipline level through the use of norms. Three different types of norms are examined: the max norm, the Kreisselmeier-Steinhauser (KS) norm, and the 1(sub p) norm. The max norm is recommended. The approach is demonstrated on a class of hub frame structures which simulate multidisciplinary systems. The max norm is shown to produce system-level constraint functions which are non-smooth. A cutting-plane algorithm is presented which adequately deals with the resulting corners in the constraint functions. The algorithm is tested on hub frames with increasing number of members (which simulate disciplines), and the results are summarized.
NASA Astrophysics Data System (ADS)
Jude Hemanth, Duraisamy; Umamaheswari, Subramaniyan; Popescu, Daniela Elena; Naaji, Antoanela
2016-01-01
Image steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT) and Finite Ridgelet Transform (FRIT) are used in combination with GA and PSO to improve the efficiency of the image steganography system.
NASA Astrophysics Data System (ADS)
Mousavi, Seyed Hosein; Nazemi, Ali; Hafezalkotob, Ashkan
2015-12-01
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.
Hu, Shaoxing; Xu, Shike; Wang, Duhu; Zhang, Aiwu
2015-11-11
Aiming at addressing the problem of high computational cost of the traditional Kalman filter in SINS/GPS, a practical optimization algorithm with offline-derivation and parallel processing methods based on the numerical characteristics of the system is presented in this paper. The algorithm exploits the sparseness and/or symmetry of matrices to simplify the computational procedure. Thus plenty of invalid operations can be avoided by offline derivation using a block matrix technique. For enhanced efficiency, a new parallel computational mechanism is established by subdividing and restructuring calculation processes after analyzing the extracted "useful" data. As a result, the algorithm saves about 90% of the CPU processing time and 66% of the memory usage needed in a classical Kalman filter. Meanwhile, the method as a numerical approach needs no precise-loss transformation/approximation of system modules and the accuracy suffers little in comparison with the filter before computational optimization. Furthermore, since no complicated matrix theories are needed, the algorithm can be easily transplanted into other modified filters as a secondary optimization method to achieve further efficiency.
Hu, Shaoxing; Xu, Shike; Wang, Duhu; Zhang, Aiwu
2015-01-01
Aiming at addressing the problem of high computational cost of the traditional Kalman filter in SINS/GPS, a practical optimization algorithm with offline-derivation and parallel processing methods based on the numerical characteristics of the system is presented in this paper. The algorithm exploits the sparseness and/or symmetry of matrices to simplify the computational procedure. Thus plenty of invalid operations can be avoided by offline derivation using a block matrix technique. For enhanced efficiency, a new parallel computational mechanism is established by subdividing and restructuring calculation processes after analyzing the extracted “useful” data. As a result, the algorithm saves about 90% of the CPU processing time and 66% of the memory usage needed in a classical Kalman filter. Meanwhile, the method as a numerical approach needs no precise-loss transformation/approximation of system modules and the accuracy suffers little in comparison with the filter before computational optimization. Furthermore, since no complicated matrix theories are needed, the algorithm can be easily transplanted into other modified filters as a secondary optimization method to achieve further efficiency. PMID:26569247
Optimization of a Lunar Pallet Lander Reinforcement Structure Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Burt, Adam O.; Hull, Patrick V.
2014-01-01
This paper presents a design automation process using optimization via a genetic algorithm to design the conceptual structure of a Lunar Pallet Lander. The goal is to determine a design that will have the primary natural frequencies at or above a target value as well as minimize the total mass. Several iterations of the process are presented. First, a concept optimization is performed to determine what class of structure would produce suitable candidate designs. From this a stiffened sheet metal approach was selected leading to optimization of beam placement through generating a two-dimensional mesh and varying the physical location of reinforcing beams. Finally, the design space is reformulated as a binary problem using 1-dimensional beam elements to truncate the design space to allow faster convergence and additional mechanical failure criteria to be included in the optimization responses. Results are presented for each design space configuration. The final flight design was derived from these results.
Genetic Algorithm (GA)-Based Inclinometer Layout Optimization.
Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo
2015-04-17
This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors.
Optimizing quantum gas production by an evolutionary algorithm
NASA Astrophysics Data System (ADS)
Lausch, T.; Hohmann, M.; Kindermann, F.; Mayer, D.; Schmidt, F.; Widera, A.
2016-05-01
We report on the application of an evolutionary algorithm (EA) to enhance performance of an ultra-cold quantum gas experiment. The production of a ^{87}rubidium Bose-Einstein condensate (BEC) can be divided into fundamental cooling steps, specifically magneto-optical trapping of cold atoms, loading of atoms to a far-detuned crossed dipole trap, and finally the process of evaporative cooling. The EA is applied separately for each of these steps with a particular definition for the feedback, the so-called fitness. We discuss the principles of an EA and implement an enhancement called differential evolution. Analyzing the reasons for the EA to improve, e.g., the atomic loading rates and increase the BEC phase-space density, yields an optimal parameter set for the BEC production and enables us to reduce the BEC production time significantly. Furthermore, we focus on how additional information about the experiment and optimization possibilities can be extracted and how the correlations revealed allow for further improvement. Our results illustrate that EAs are powerful optimization tools for complex experiments and exemplify that the application yields useful information on the dependence of these experiments on the optimized parameters.
Genetic Algorithm (GA)-Based Inclinometer Layout Optimization
Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo
2015-01-01
This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors. PMID:25897500
A hybrid optimization approach in non-isothermal glass molding
NASA Astrophysics Data System (ADS)
Vu, Anh-Tuan; Kreilkamp, Holger; Krishnamoorthi, Bharathwaj Janaki; Dambon, Olaf; Klocke, Fritz
2016-10-01
Intensively growing demands on complex yet low-cost precision glass optics from the today's photonic market motivate the development of an efficient and economically viable manufacturing technology for complex shaped optics. Against the state-of-the-art replication-based methods, Non-isothermal Glass Molding turns out to be a promising innovative technology for cost-efficient manufacturing because of increased mold lifetime, less energy consumption and high throughput from a fast process chain. However, the selection of parameters for the molding process usually requires a huge effort to satisfy precious requirements of the molded optics and to avoid negative effects on the expensive tool molds. Therefore, to reduce experimental work at the beginning, a coupling CFD/FEM numerical modeling was developed to study the molding process. This research focuses on the development of a hybrid optimization approach in Non-isothermal glass molding. To this end, an optimal configuration with two optimization stages for multiple quality characteristics of the glass optics is addressed. The hybrid Back-Propagation Neural Network (BPNN)-Genetic Algorithm (GA) is first carried out to realize the optimal process parameters and the stability of the process. The second stage continues with the optimization of glass preform using those optimal parameters to guarantee the accuracy of the molded optics. Experiments are performed to evaluate the effectiveness and feasibility of the model for the process development in Non-isothermal glass molding.
Improved optimization algorithm for proximal point-based dictionary updating methods
NASA Astrophysics Data System (ADS)
Zhao, Changchen; Hwang, Wen-Liang; Lin, Chun-Liang; Chen, Weihai
2016-09-01
Proximal K-singular value decomposition (PK-SVD) is a dictionary updating algorithm that incorporates proximal point method into K-SVD. The attempt of combining proximal method and K-SVD has achieved promising result in such areas as sparse approximation, image denoising, and image compression. However, the optimization procedure of PK-SVD is complicated and, therefore, limits the algorithm in both theoretical analysis and practical use. This article proposes a simple but effective optimization approach to the formulation of PK-SVD. We cast this formulation as a fitting problem and relax the constraint on the direction of the k'th row in the sparse coefficient matrix. This relaxation strengthens the regularization effect of the proximal point. The proposed algorithm needs fewer steps to implement and further boost the performance of PK-SVD while maintaining the same computational complexity. Experimental results demonstrate that the proposed algorithm outperforms conventional algorithms in reconstruction error, recovery rate, and convergence speed for sparse approximation and achieves better results in image denoising.
Portfolio optimization using median-variance approach
NASA Astrophysics Data System (ADS)
Wan Mohd, Wan Rosanisah; Mohamad, Daud; Mohamed, Zulkifli
2013-04-01
Optimization models have been applied in many decision-making problems particularly in portfolio selection. Since the introduction of Markowitz's theory of portfolio selection, various approaches based on mathematical programming have been introduced such as mean-variance, mean-absolute deviation, mean-variance-skewness and conditional value-at-risk (CVaR) mainly to maximize return and minimize risk. However most of the approaches assume that the distribution of data is normal and this is not generally true. As an alternative, in this paper, we employ the median-variance approach to improve the portfolio optimization. This approach has successfully catered both types of normal and non-normal distribution of data. With this actual representation, we analyze and compare the rate of return and risk between the mean-variance and the median-variance based portfolio which consist of 30 stocks from Bursa Malaysia. The results in this study show that the median-variance approach is capable to produce a lower risk for each return earning as compared to the mean-variance approach.
Efficient algorithms for future aircraft design: Contributions to aerodynamic shape optimization
NASA Astrophysics Data System (ADS)
Hicken, Jason Edward
Advances in numerical optimization have raised the possibility that efficient and novel aircraft configurations may be "discovered" by an algorithm. To begin exploring this possibility, a fast and robust set of tools for aerodynamic shape optimization is developed. Parameterization and mesh-movement are integrated to accommodate large changes in the geometry. This integrated approach uses a coarse B-spline control grid to represent the geometry and move the computational mesh; consequently, the mesh-movement algorithm is two to three orders faster than a node-based linear elasticity approach, without compromising mesh quality. Aerodynamic analysis is performed using a flow solver for the Euler equations. The governing equations are discretized using summation-by-parts finite-difference operators and simultaneous approximation terms, which permit C0 mesh continuity at block interfaces. The discretization results in a set of nonlinear algebraic equations, which are solved using an efficient parallel Newton-Krylov-Schur strategy. A gradient-based optimization algorithm is adopted. The gradient is evaluated using adjoint variables for the flow and mesh equations in a sequential approach. The flow adjoint equations are solved using a novel variant of the Krylov solver GCROT. This variant of GCROT is flexible to take advantage of non-stationary preconditioners and is shown to outperform restarted flexible GMRES. The aerodynamic optimizer is applied to several studies of induced-drag minimization. An elliptical lift distribution is recovered by varying spanwise twist, thereby validating the algorithm. Planform optimization based on the Euler equations produces a nonelliptical lift distribution, in contrast with the predictions of lifting-line theory. A study of spanwise vertical shape optimization confirms that a winglet-up configuration is more efficient than a winglet-down configuration. A split-tip geometry is used to explore nonlinear wake-wing interactions: the
2012-01-01
Background Multi-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes. However, considering the huge number of possible ways to mix multiple drugs at different concentrations, it is practically difficult to identify the optimal drug combination through exhaustive testing. Results In this paper, we propose a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm, that can provide an efficient and systematic way for optimizing multi-drug cocktails. The ARU algorithm iteratively updates the drug combination to improve its response, where the update is made by comparing the response of the current combination with that of a reference combination, based on which the beneficial update direction is predicted. The reference combination is continuously updated based on the drug response values observed in the past, thereby adapting to the underlying drug response function. To demonstrate the effectiveness of the proposed algorithm, we evaluated its performance based on various multi-dimensional drug functions and compared it with existing algorithms. Conclusions Simulation results show that the ARU algorithm significantly outperforms existing stochastic search algorithms, including the Gur Game algorithm. In fact, the ARU algorithm can more effectively identify potent drug combinations and it typically spends fewer iterations for finding effective combinations. Furthermore, the ARU algorithm is robust to random fluctuations and noise in the measured drug response, which makes the algorithm well-suited for practical drug optimization applications. PMID:23134742
An adaptive /N-body algorithm of optimal order
NASA Astrophysics Data System (ADS)
Pruett, C. David; Rudmin, Joseph W.; Lacy, Justin M.
2003-05-01
Picard iteration is normally considered a theoretical tool whose primary utility is to establish the existence and uniqueness of solutions to first-order systems of ordinary differential equations (ODEs). However, in 1996, Parker and Sochacki [Neural, Parallel, Sci. Comput. 4 (1996)] published a practical numerical method for a certain class of ODEs, based upon modified Picard iteration, that generates the Maclaurin series of the solution to arbitrarily high order. The applicable class of ODEs consists of first-order, autonomous systems whose right-hand side functions (generators) are projectively polynomial; that is, they can be written as polynomials in the unknowns. The class is wider than might be expected. The method is ideally suited to the classical N-body problem, which is projectively polynomial. Here, we recast the N-body problem in polynomial form and develop a Picard-based algorithm for its solution. The algorithm is highly accurate, parameter-free, and simultaneously adaptive in time and order. Test cases for both benign and chaotic N-body systems reveal that optimal order is dynamic. That is, in addition to dependency upon N and the desired accuracy, optimal order depends upon the configuration of the bodies at any instant.
Optimizing ion channel models using a parallel genetic algorithm on graphical processors.
Ben-Shalom, Roy; Aviv, Amit; Razon, Benjamin; Korngreen, Alon
2012-01-01
We have recently shown that we can semi-automatically constrain models of voltage-gated ion channels by combining a stochastic search algorithm with ionic currents measured using multiple voltage-clamp protocols. Although numerically successful, this approach is highly demanding computationally, with optimization on a high performance Linux cluster typically lasting several days. To solve this computational bottleneck we converted our optimization algorithm for work on a graphical processing unit (GPU) using NVIDIA's CUDA. Parallelizing the process on a Fermi graphic computing engine from NVIDIA increased the speed ∼180 times over an application running on an 80 node Linux cluster, considerably reducing simulation times. This application allows users to optimize models for ion channel kinetics on a single, inexpensive, desktop "super computer," greatly reducing the time and cost of building models relevant to neuronal physiology. We also demonstrate that the point of algorithm parallelization is crucial to its performance. We substantially reduced computing time by solving the ODEs (Ordinary Differential Equations) so as to massively reduce memory transfers to and from the GPU. This approach may be applied to speed up other data intensive applications requiring iterative solutions of ODEs.
Constant-complexity stochastic simulation algorithm with optimal binning
Sanft, Kevin R.; Othmer, Hans G.
2015-08-21
At the molecular level, biochemical processes are governed by random interactions between reactant molecules, and the dynamics of such systems are inherently stochastic. When the copy numbers of reactants are large, a deterministic description is adequate, but when they are small, such systems are often modeled as continuous-time Markov jump processes that can be described by the chemical master equation. Gillespie’s Stochastic Simulation Algorithm (SSA) generates exact trajectories of these systems, but the amount of computational work required for each step of the original SSA is proportional to the number of reaction channels, leading to computational complexity that scales linearly with the problem size. The original SSA is therefore inefficient for large problems, which has prompted the development of several alternative formulations with improved scaling properties. We describe an exact SSA that uses a table data structure with event time binning to achieve constant computational complexity with respect to the number of reaction channels for weakly coupled reaction networks. We present a novel adaptive binning strategy and discuss optimal algorithm parameters. We compare the computational efficiency of the algorithm to existing methods and demonstrate excellent scaling for large problems. This method is well suited for generating exact trajectories of large weakly coupled models, including those that can be described by the reaction-diffusion master equation that arises from spatially discretized reaction-diffusion processes.
Quantum-inspired immune clonal algorithm for global optimization.
Jiao, Licheng; Li, Yangyang; Gong, Maoguo; Zhang, Xiangrong
2008-10-01
Based on the concepts and principles of quantum computing, a novel immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), is proposed to deal with the problem of global optimization. In QICA, the antibody is proliferated and divided into a set of subpopulation groups. The antibodies in a subpopulation group are represented by multistate gene quantum bits. In the antibody's updating, the general quantum rotation gate strategy and the dynamic adjusting angle mechanism are applied to accelerate convergence. The quantum not gate is used to realize quantum mutation to avoid premature convergences. The proposed quantum recombination realizes the information communication between subpopulation groups to improve the search efficiency. Theoretical analysis proves that QICA converges to the global optimum. In the first part of the experiments, 10 unconstrained and 13 constrained benchmark functions are used to test the performance of QICA. The results show that QICA performs much better than the other improved genetic algorithms in terms of the quality of solution and computational cost. In the second part of the experiments, QICA is applied to a practical problem (i.e., multiuser detection in direct-sequence code-division multiple-access systems) with a satisfying result.
Constant-complexity stochastic simulation algorithm with optimal binning.
Sanft, Kevin R; Othmer, Hans G
2015-08-21
At the molecular level, biochemical processes are governed by random interactions between reactant molecules, and the dynamics of such systems are inherently stochastic. When the copy numbers of reactants are large, a deterministic description is adequate, but when they are small, such systems are often modeled as continuous-time Markov jump processes that can be described by the chemical master equation. Gillespie's Stochastic Simulation Algorithm (SSA) generates exact trajectories of these systems, but the amount of computational work required for each step of the original SSA is proportional to the number of reaction channels, leading to computational complexity that scales linearly with the problem size. The original SSA is therefore inefficient for large problems, which has prompted the development of several alternative formulations with improved scaling properties. We describe an exact SSA that uses a table data structure with event time binning to achieve constant computational complexity with respect to the number of reaction channels for weakly coupled reaction networks. We present a novel adaptive binning strategy and discuss optimal algorithm parameters. We compare the computational efficiency of the algorithm to existing methods and demonstrate excellent scaling for large problems. This method is well suited for generating exact trajectories of large weakly coupled models, including those that can be described by the reaction-diffusion master equation that arises from spatially discretized reaction-diffusion processes.
GMG: A Guaranteed, Efficient Global Optimization Algorithm for Remote Sensing.
D'Helon, CD
2004-08-18
The monocular passive ranging (MPR) problem in remote sensing consists of identifying the precise range of an airborne target (missile, plane, etc.) from its observed radiance. This inverse problem may be set as a global optimization problem (GOP) whereby the difference between the observed and model predicted radiances is minimized over the possible ranges and atmospheric conditions. Using additional information about the error function between the predicted and observed radiances of the target, we developed GMG, a new algorithm to find the Global Minimum with a Guarantee. The new algorithm transforms the original continuous GOP into a discrete search problem, thereby guaranteeing to find the position of the global minimum in a reasonably short time. The algorithm is first applied to the golf course problem, which serves as a litmus test for its performance in the presence of both complete and degraded additional information. GMG is further assessed on a set of standard benchmark functions and then applied to various realizations of the MPR problem.
Simplification of multiple Fourier series - An example of algorithmic approach
NASA Technical Reports Server (NTRS)
Ng, E. W.
1981-01-01
This paper describes one example of multiple Fourier series which originate from a problem of spectral analysis of time series data. The example is exercised here with an algorithmic approach which can be generalized for other series manipulation on a computer. The generalized approach is presently pursued towards applications to a variety of multiple series and towards a general purpose algorithm for computer algebra implementation.
In-Space Radiator Shape Optimization using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Hull, Patrick V.; Kittredge, Ken; Tinker, Michael; SanSoucie, Michael
2006-01-01
Future space exploration missions will require the development of more advanced in-space radiators. These radiators should be highly efficient and lightweight, deployable heat rejection systems. Typical radiators for in-space heat mitigation commonly comprise a substantial portion of the total vehicle mass. A small mass savings of even 5-10% can greatly improve vehicle performance. The objective of this paper is to present the development of detailed tools for the analysis and design of in-space radiators using evolutionary computation techniques. The optimality criterion is defined as a two-dimensional radiator with a shape demonstrating the smallest mass for the greatest overall heat transfer, thus the end result is a set of highly functional radiator designs. This cross-disciplinary work combines topology optimization and thermal analysis design by means of a genetic algorithm The proposed design tool consists of the following steps; design parameterization based on the exterior boundary of the radiator, objective function definition (mass minimization and heat loss maximization), objective function evaluation via finite element analysis (thermal radiation analysis) and optimization based on evolutionary algorithms. The radiator design problem is defined as follows: the input force is a driving temperature and the output reaction is heat loss. Appropriate modeling of the space environment is added to capture its effect on the radiator. The design parameters chosen for this radiator shape optimization problem fall into two classes, variable height along the width of the radiator and a spline curve defining the -material boundary of the radiator. The implementation of multiple design parameter schemes allows the user to have more confidence in the radiator optimization tool upon demonstration of convergence between the two design parameter schemes. This tool easily allows the user to manipulate the driving temperature regions thus permitting detailed design of in
Optimal Scheduling for Geosynchronous Space Object Follow-up Observations Using a Genetic Algorithm
NASA Astrophysics Data System (ADS)
Hinze, A.; Fiedler, H.; Schildknecht, T.
2016-09-01
Optical observations for space debris in the geosynchronous region have been performed for many years. During this time, observation strategies, processing techniques and cataloguing approaches were successfully developed. Nevertheless, the importance of protecting this orbit region from space debris requires continuous monitoring in order to support collision avoidance operations. So-called follow-up observations providing information for orbit improvement estimations are necessary to maintain high accuracy of the cataloged objects. Those serve a two-fold: For one, the orbits have to be accurate enough to be able to re-observe the object after a time of no observations, that is keeping it in the catalogue, secondly, the importance of protecting active space assets from space debris requires even higher accuracy of the catalogue orbits. Due to limited observation resources and because a space debris object in the geostationary orbit region may only be observed for a limited period of time per the observation night and telescope, efficient scheduling of follow-up observations is a key element. This paper presents an optimal scheduling algorithm for a robotic optical telescope network using a genetic algorithm that has been applied providing optimal solutions for catalogue maintenance. As optimization parameter the information content of the orbit has been used. It is shown that information content utilizing the orbit's covariance and the information gain in an expected update is a useful optimization measure. Finally, simulations with simulated data of space debris objects are used to study the effectivity of the scheduling algorithm.
Saha, S K; Dutta, R; Choudhury, R; Kar, R; Mandal, D; Ghoshal, S P
2013-01-01
In this paper, opposition-based harmony search has been applied for the optimal design of linear phase FIR filters. RGA, PSO, and DE have also been adopted for the sake of comparison. The original harmony search algorithm is chosen as the parent one, and opposition-based approach is applied. During the initialization, randomly generated population of solutions is chosen, opposite solutions are also considered, and the fitter one is selected as a priori guess. In harmony memory, each such solution passes through memory consideration rule, pitch adjustment rule, and then opposition-based reinitialization generation jumping, which gives the optimum result corresponding to the least error fitness in multidimensional search space of FIR filter design. Incorporation of different control parameters in the basic HS algorithm results in the balancing of exploration and exploitation of search space. Low pass, high pass, band pass, and band stop FIR filters are designed with the proposed OHS and other aforementioned algorithms individually for comparative optimization performance. A comparison of simulation results reveals the optimization efficacy of the OHS over the other optimization techniques for the solution of the multimodal, nondifferentiable, nonlinear, and constrained FIR filter design problems.
Cao, Daliang; Earl, Matthew A; Luan, Shuang; Shepard, David M
2006-04-01
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 were 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 Pinnacle3 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.
Jang, In Gwun; Kim, Il Yong; Kwak, Byung Ban
2009-01-01
In bone-remodeling studies, it is believed that the morphology of bone is affected by its internal mechanical loads. From the 1970s, high computing power enabled quantitative studies in the simulation of bone remodeling or bone adaptation. Among them, Huiskes et al. (1987, "Adaptive Bone Remodeling Theory Applied to Prosthetic Design Analysis," J. Biomech. Eng., 20, pp. 1135-1150) proposed a strain energy density based approach to bone remodeling and used the apparent density for the characterization of internal bone morphology. The fundamental idea was that bone density would increase when strain (or strain energy density) is higher than a certain value and bone resorption would occur when the strain (or strain energy density) quantities are lower than the threshold. Several advanced algorithms were developed based on these studies in an attempt to more accurately simulate physiological bone-remodeling processes. As another approach, topology optimization originally devised in structural optimization has been also used in the computational simulation of the bone-remodeling process. The topology optimization method systematically and iteratively distributes material in a design domain, determining an optimal structure that minimizes an objective function. In this paper, we compared two seemingly different approaches in different fields-the strain energy density based bone-remodeling algorithm (biomechanical approach) and the compliance based structural topology optimization method (mechanical approach)-in terms of mathematical formulations, numerical difficulties, and behavior of their numerical solutions. Two numerical case studies were conducted to demonstrate their similarity and difference, and then the solution convergences were discussed quantitatively.
Optimal sliding guidance algorithm for Mars powered descent phase
NASA Astrophysics Data System (ADS)
Wibben, Daniel R.; Furfaro, Roberto
2016-02-01
Landing on large planetary bodies (e.g. Mars) with pinpoint accuracy presents a set of new challenges that must be addressed. One such challenge is the development of new guidance algorithms that exhibit a higher degree of robustness and flexibility. In this paper, the Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) optimal sliding guidance (OSG) scheme is applied to the Mars powered descent phase. This guidance algorithm has been specifically designed to combine techniques from both optimal and sliding control theories to generate an acceleration command based purely on the current estimated spacecraft state and desired final target state. Consequently, OSG yields closed-loop trajectories that do not need a reference trajectory. The guidance algorithm has its roots in the generalized ZEM/ZEV feedback guidance and its mathematical equations are naturally derived by defining a non-linear sliding surface as a function of the terms Zero-Effort-Miss and Zero-Effort-Velocity. With the addition of the sliding mode and using Lyapunov theory for non-autonomous systems, one can formally prove that the developed OSG law is globally finite-time stable to unknown but bounded perturbations. Here, the focus is on comparing the generalized ZEM/ZEV feedback guidance with the OSG law to explicitly demonstrate the benefits of the sliding mode augmentation. Results show that the sliding guidance provides a more robust solution in off-nominal scenarios while providing similar fuel consumption when compared to the non-sliding guidance command. Further, a Monte Carlo analysis is performed to examine the performance of the OSG law under perturbed conditions.
A Global Optimization Approach to Multi-Polarity Sentiment Analysis
Li, Xinmiao; Li, Jing; Wu, Yukeng
2015-01-01
Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis. The effectiveness of applying a global optimization approach to sentiment analysis remains unclear. We propose a global optimization-based sentiment analysis (PSOGO-Senti) approach to improve sentiment analysis with IG for feature selection and SVM as the learning engine. The PSOGO-Senti approach utilizes a particle swarm optimization algorithm to obtain a global optimal combination of feature dimensions and parameters in the SVM. We evaluate the PSOGO-Senti model on two datasets from different fields. The experimental results showed that the PSOGO-Senti model can improve binary and multi-polarity Chinese sentiment analysis. We compared the optimal feature subset selected by PSOGO-Senti with the features in the sentiment dictionary. The results of this comparison indicated that PSOGO-Senti can effectively remove redundant and noisy features and can select a domain-specific feature subset with a higher-explanatory power for a particular sentiment analysis task. The experimental results showed that the PSOGO-Senti approach is effective and robust for sentiment analysis tasks in different domains. By comparing the improvements of two-polarity, three-polarity and five-polarity sentiment analysis results, we found that the five-polarity sentiment analysis delivered the largest improvement. The improvement of the two-polarity sentiment analysis was the smallest. We conclude that the PSOGO-Senti achieves higher improvement for a more complicated sentiment analysis task. We also compared the results of PSOGO-Senti with those of the genetic algorithm (GA) and grid search method. From
Optimization of Operation Sequence in CNC Machine Tools Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Abu Qudeiri, Jaber; Yamamoto, Hidehiko; Ramli, Rizauddin
The productivity of machine tools is significantly improved by using microcomputer based CAD/CAM systems for NC program generation. Currently, many commercial CAD/CAM packages that provide automatic NC programming have been developed and applied to various cutting processes. Many cutting processes machined by CNC machine tools. In this paper, we attempt to find an efficient solution approach to determine the best sequence of operations for a set of operations that located in asymmetrical locations and different levels. In order to find the best sequence of operations that achieves the shortest cutting tool travel path (CTTP), genetic algorithm is introduced. After the sequence is optimized, the G-codes that use to code for the travel time is created. CTTP can be formulated as a special case of the traveling salesman problem (TSP). The incorporation of genetic algorithm and TSP can be included in the commercial CAD/CAM packages to optimize the CTTP during automatic generation of NC programs.
Adjoint-Based Algorithms for Adaptation and Design Optimizations on Unstructured Grids
NASA Technical Reports Server (NTRS)
Nielsen, Eric J.
2006-01-01
Schemes based on discrete adjoint algorithms present several exciting opportunities for significantly advancing the current state of the art in computational fluid dynamics. Such methods provide an extremely efficient means for obtaining discretely consistent sensitivity information for hundreds of design variables, opening the door to rigorous, automated design optimization of complex aerospace configuration using the Navier-Stokes equation. Moreover, the discrete adjoint formulation provides a mathematically rigorous foundation for mesh adaptation and systematic reduction of spatial discretization error. Error estimates are also an inherent by-product of an adjoint-based approach, valuable information that is virtually non-existent in today's large-scale CFD simulations. An overview of the adjoint-based algorithm work at NASA Langley Research Center is presented, with examples demonstrating the potential impact on complex computational problems related to design optimization as well as mesh adaptation.
A genetic engineering approach to genetic algorithms.
Gero, J S; Kazakov, V
2001-01-01
We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful genetic material. Compared to the standard GA, it provides some computational advantages as well as a tool for automatic generation of hierarchical genetic representations specifically tailored to suit certain classes of problems.
Multidisciplinary Approach to Linear Aerospike Nozzle Optimization
NASA Technical Reports Server (NTRS)
Korte, J. J.; Salas, A. O.; Dunn, H. J.; Alexandrov, N. M.; Follett, W. W.; Orient, G. E.; Hadid, A. H.
1997-01-01
A model of a linear aerospike rocket nozzle that consists of coupled aerodynamic and structural analyses has been developed. A nonlinear computational fluid dynamics code is used to calculate the aerodynamic thrust, and a three-dimensional fink-element model is used to determine the structural response and weight. The model will be used to demonstrate multidisciplinary design optimization (MDO) capabilities for relevant engine concepts, assess performance of various MDO approaches, and provide a guide for future application development. In this study, the MDO problem is formulated using the multidisciplinary feasible (MDF) strategy. The results for the MDF formulation are presented with comparisons against sequential aerodynamic and structural optimized designs. Significant improvements are demonstrated by using a multidisciplinary approach in comparison with the single- discipline design strategy.
NASA Astrophysics Data System (ADS)
Mandl, Christoph E.
1981-08-01
This paper presents a state of the art survey of network models and algorithms that can be used as planning tools in irrigation and wastewater systems. It is shown that the problem of designing or extending such systems basically leads to the same type of mathematical optimization model. The difficulty in solving this model lies mainly in the properties of the objective function. Trying to minimize construction and/or operating costs of a system typically results in a concave cost (objective) function due to economies of scale. A number of ways to attack such models are discussed and compared, including linear programing, integer programing, and specially designed exact and heuristic algorithms. The usefulness of each approach is evaluated in terms of the validity of the model, the computational complexity of the algorithm, the properties of the solution, the availability of software, and the capability for sensitivity analysis.
Kausar, A S M Zahid; Reza, Ahmed Wasif; Wo, Lau Chun; Ramiah, Harikrishnan
2014-01-01
Although ray tracing based propagation prediction models are popular for indoor radio wave propagation characterization, most of them do not provide an integrated approach for achieving the goal of optimum coverage, which is a key part in designing wireless network. In this paper, an accelerated technique of three-dimensional ray tracing is presented, where rough surface scattering is included for making a more accurate ray tracing technique. Here, the rough surface scattering is represented by microfacets, for which it becomes possible to compute the scattering field in all possible directions. New optimization techniques, like dual quadrant skipping (DQS) and closest object finder (COF), are implemented for fast characterization of wireless communications and making the ray tracing technique more efficient. In conjunction with the ray tracing technique, probability based coverage optimization algorithm is accumulated with the ray tracing technique to make a compact solution for indoor propagation prediction. The proposed technique decreases the ray tracing time by omitting the unnecessary objects for ray tracing using the DQS technique and by decreasing the ray-object intersection time using the COF technique. On the other hand, the coverage optimization algorithm is based on probability theory, which finds out the minimum number of transmitters and their corresponding positions in order to achieve optimal indoor wireless coverage. Both of the space and time complexities of the proposed algorithm surpass the existing algorithms. For the verification of the proposed ray tracing technique and coverage algorithm, detailed simulation results for different scattering factors, different antenna types, and different operating frequencies are presented. Furthermore, the proposed technique is verified by the experimental results.
Cancer Behavior: An Optimal Control Approach
Gutiérrez, Pedro J.; Russo, Irma H.; Russo, J.
2009-01-01
With special attention to cancer, this essay explains how Optimal Control Theory, mainly used in Economics, can be applied to the analysis of biological behaviors, and illustrates the ability of this mathematical branch to describe biological phenomena and biological interrelationships. Two examples are provided to show the capability and versatility of this powerful mathematical approach in the study of biological questions. The first describes a process of organogenesis, and the second the development of tumors. PMID:22247736
Parallel global optimization with the particle swarm algorithm.
Schutte, J F; Reinbolt, J A; Fregly, B J; Haftka, R T; George, A D
2004-12-07
Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor. To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the particle swarm optimization (PSO) algorithm. Parallel PSO performance was evaluated using two categories of optimization problems possessing multiple local minima-large-scale analytical test problems with computationally cheap function evaluations and medium-scale biomechanical system identification problems with computationally expensive function evaluations. For load-balanced analytical test problems formulated using 128 design variables, speedup was close to ideal and parallel efficiency above 95% for up to 32 nodes on a Beowulf cluster. In contrast, for load-imbalanced biomechanical system identification problems with 12 design variables, speedup plateaued and parallel efficiency decreased almost linearly with increasing number of nodes. The primary factor affecting parallel performance was the synchronization requirement of the parallel algorithm, which dictated that each iteration must wait for completion of the slowest fitness evaluation. When the analytical problems were solved using a fixed number of swarm iterations, a single population of 128 particles produced a better convergence rate than did multiple independent runs performed using sub-populations (8 runs with 16 particles, 4 runs with 32 particles, or 2 runs with 64 particles). These results suggest that (1) parallel PSO exhibits excellent parallel performance under load-balanced conditions, (2) an asynchronous implementation would be valuable for real-life problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available.
A guided search genetic algorithm using mined rules for optimal affective product design
NASA Astrophysics Data System (ADS)
Fung, Chris K. Y.; Kwong, C. K.; Chan, Kit Yan; Jiang, H.
2014-08-01
Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design.
A comparison of three optimization algorithms for intensity modulated radiation therapy.
Pflugfelder, Daniel; Wilkens, Jan J; Nill, Simeon; Oelfke, Uwe
2008-01-01
In intensity modulated treatment techniques, the modulation of each treatment field is obtained using an optimization algorithm. Multiple optimization algorithms have been proposed in the literature, e.g. steepest descent, conjugate gradient, quasi-Newton methods to name a few. The standard optimization algorithm in our in-house inverse planning tool KonRad is a quasi-Newton algorithm. Although this algorithm yields good results, it also has some drawbacks. Thus we implemented an improved optimization algorithm based on the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) routine. In this paper the improved optimization algorithm is described. To compare the two algorithms, several treatment plans are optimized using both algorithms. This included photon (IMRT) as well as proton (IMPT) intensity modulated therapy treatment plans. To present the results in a larger context the widely used conjugate gradient algorithm was also included into this comparison. On average, the improved optimization algorithm was six times faster to reach the same objective function value. However, it resulted not only in an acceleration of the optimization. Due to the faster convergence, the improved optimization algorithm usually terminates the optimization process at a lower objective function value. The average of the observed improvement in the objective function value was 37%. This improvement is clearly visible in the corresponding dose-volume-histograms. The benefit of the improved optimization algorithm is particularly pronounced in proton therapy plans. The conjugate gradient algorithm ranked in between the other two algorithms with an average speedup factor of two and an average improvement of the objective function value of 30%.
PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning
Fiege, Jason; McCurdy, Boyd; Potrebko, Peter; Champion, Heather; Cull, Andrew
2011-09-15
Purpose: In radiation therapy treatment planning, the clinical objectives of uniform high dose to the planning target volume (PTV) and low dose to the organs-at-risk (OARs) are invariably in conflict, often requiring compromises to be made between them when selecting the best treatment plan for a particular patient. In this work, the authors introduce Pareto-Aware Radiotherapy Evolutionary Treatment Optimization (pareto), a multiobjective optimization tool to solve for beam angles and fluence patterns in intensity-modulated radiation therapy (IMRT) treatment planning. Methods: pareto is built around a powerful multiobjective genetic algorithm (GA), which allows us to treat the problem of IMRT treatment plan optimization as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. We have employed a simple parameterized beam fluence representation with a realistic dose calculation approach, incorporating patient scatter effects, to demonstrate feasibility of the proposed approach on two phantoms. The first phantom is a simple cylindrical phantom containing a target surrounded by three OARs, while the second phantom is more complex and represents a paraspinal patient. Results: pareto results in a large database of Pareto nondominated solutions that represent the necessary trade-offs between objectives. The solution quality was examined for several PTV and OAR fitness functions. The combination of a conformity-based PTV fitness function and a dose-volume histogram (DVH) or equivalent uniform dose (EUD) -based fitness function for the OAR produced relatively uniform and conformal PTV doses, with well-spaced beams. A penalty function added to the fitness functions eliminates hotspots. Comparison of resulting DVHs to those from treatment plans developed with a single-objective fluence optimizer (from a commercial treatment planning system) showed good correlation. Results also indicated that pareto shows
Optimal groundwater remediation using artificial neural networks and the genetic algorithm
Rogers, Leah L.
1992-08-01
An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ``recycle`` or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models.
Genetic Algorithm Approaches to Prebiobiotic Chemistry Modeling
NASA Technical Reports Server (NTRS)
Lohn, Jason; Colombano, Silvano
1997-01-01
We model an artificial chemistry comprised of interacting polymers by specifying two initial conditions: a distribution of polymers and a fixed set of reversible catalytic reactions. A genetic algorithm is used to find a set of reactions that exhibit a desired dynamical behavior. Such a technique is useful because it allows an investigator to determine whether a specific pattern of dynamics can be produced, and if it can, the reaction network found can be then analyzed. We present our results in the context of studying simplified chemical dynamics in theorized protocells - hypothesized precursors of the first living organisms. Our results show that given a small sample of plausible protocell reaction dynamics, catalytic reaction sets can be found. We present cases where this is not possible and also analyze the evolved reaction sets.
Phase retrieval with transverse translation diversity: a nonlinear optimization approach.
Guizar-Sicairos, Manuel; Fienup, James R
2008-05-12
We develop and test a nonlinear optimization algorithm for solving the problem of phase retrieval with transverse translation diversity, where the diverse far-field intensity measurements are taken after translating the object relative to a known illumination pattern. Analytical expressions for the gradient of a squared-error metric with respect to the object, illumination and translations allow joint optimization of the object and system parameters. This approach achieves superior reconstructions, with respect to a previously reported technique [H. M. L. Faulkner and J. M. Rodenburg, Phys. Rev. Lett. 93, 023903 (2004)], when the system parameters are inaccurately known or in the presence of noise. Applicability of this method for samples that are smaller than the illumination pattern is explored.
[Optimization approach to inverse problems in near-infrared optical tomography].
Li, Weitao; Wang, Huinan; Qian, Zhiyu
2008-04-01
In this paper, we introduce an optimization approach to the inverse model of near-infrared optical tomography (NIR OT), which can reconstruct the optical properties, namely the absorption and scattering coefficients of thick tissue such as brain and breast tissues. A modeling and simulation tool, named Femlab and based on finite element methods, has been tested wherein the forward models are based on the diffusion equation. Then the inverse model is soved; this is regarded as an optimization approach, including the tests on difference between the measured data and the predicted data, and the optimization methods of optical properties. The algorithms used for optimization are multi-species Genetic Algorithms based on multi-encoding. At last, the whole strategy for the Femlab and optimization approach is given. The strategy is proved to be sufficient by the simulation results.
Optimal synchronization of Kuramoto oscillators: A dimensional reduction approach
NASA Astrophysics Data System (ADS)
Pinto, Rafael S.; Saa, Alberto
2015-12-01
A recently proposed dimensional reduction approach for studying synchronization in the Kuramoto model is employed to build optimal network topologies to favor or to suppress synchronization. The approach is based in the introduction of a collective coordinate for the time evolution of the phase locked oscillators, in the spirit of the Ott-Antonsen ansatz. We show that the optimal synchronization of a Kuramoto network demands the maximization of the quadratic function ωTL ω , where ω stands for the vector of the natural frequencies of the oscillators and L for the network Laplacian matrix. Many recently obtained numerical results can be reobtained analytically and in a simpler way from our maximization condition. A computationally efficient hill climb rewiring algorithm is proposed to generate networks with optimal synchronization properties. Our approach can be easily adapted to the case of the Kuramoto models with both attractive and repulsive interactions, and again many recent numerical results can be rederived in a simpler and clearer analytical manner.
Harmony search optimization algorithm for a novel transportation problem in a consolidation network
NASA Astrophysics Data System (ADS)
Davod Hosseini, Seyed; Akbarpour Shirazi, Mohsen; Taghi Fatemi Ghomi, Seyed Mohammad
2014-11-01
This article presents a new harmony search optimization algorithm to solve a novel integer programming model developed for a consolidation network. In this network, a set of vehicles is used to transport goods from suppliers to their corresponding customers via two transportation systems: direct shipment and milk run logistics. The objective of this problem is to minimize the total shipping cost in the network, so it tries to reduce the number of required vehicles using an efficient vehicle routing strategy in the solution approach. Solving several numerical examples confirms that the proposed solution approach based on the harmony search algorithm performs much better than CPLEX in reducing both the shipping cost in the network and computational time requirement, especially for realistic size problem instances.
Optimal design of low-density SNP arrays for genomic prediction: algorithm and applications
Technology Transfer Automated Retrieval System (TEKTRAN)
Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for their optimal design. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optim...
Optimal algorithm for fluorescence suppression of modulated Raman spectroscopy.
Mazilu, Michael; De Luca, Anna Chiara; Riches, Andrew; Herrington, C Simon; Dholakia, Kishan
2010-05-24
Raman spectroscopy permits probing of the molecular and chemical properties of the analyzed sample. However, its applicability has been seriously limited to specific applications by the presence of a strong fluorescence background. In our recent paper [Anal. Chem. 82, 738 (2010)], we reported a new modulation method for separating Raman scattering from fluorescence. By continuously changing the excitation wavelength, we demonstrated that it is possible to continuously shift the Raman peaks while the fluorescence background remains essentially constant. In this way, our method allows separation of the modulated Raman peaks from the static fluorescence background with important advantages when compared to previous work using only two [Appl. Spectrosc. 46, 707 (1992)] or a few shifted excitation wavelengths [Opt. Express 16, 10975 (2008)]. The purpose of the present work is to demonstrate a significant improvement of the efficacy of the modulated method by using different processing algorithms. The merits of each algorithm (Standard Deviation analysis, Fourier Filtering, Least-Squares fitting and Principal Component Analysis) are discussed and the dependence of the modulated Raman signal on several parameters, such as the amplitude and the modulation rate of the Raman excitation wavelength, is analyzed. The results of both simulation and experimental data demonstrate that Principal Component Analysis is the best processing algorithm. It improves the signal-to-noise ratio in the treated Raman spectra, reducing required acquisition times. Additionally, this approach does not require any synchronization procedure, reduces user intervention and renders it suitable for real-time applications.
GenMin: An enhanced genetic algorithm for global optimization
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Lagaris, I. E.
2008-06-01
A new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++. Program summaryProgram title: GenMin Catalogue identifier: AEAR_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAR_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 35 810 No. of bytes in distributed program, including test data, etc.: 436 613 Distribution format: tar.gz Programming language: GNU-C++, GNU-C, GNU Fortran 77 Computer: The tool is designed to be portable in all systems running the GNU C++ compiler Operating system: The tool is designed to be portable in all systems running the GNU C++ compiler RAM: 200 KB Word size: 32 bits Classification: 4.9 Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a least squares type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero). Solution method: Grammatical evolution and a stopping rule. Running time: Depending on the
An optimization-based iterative algorithm for recovering fluorophore location
NASA Astrophysics Data System (ADS)
Yi, Huangjian; Peng, Jinye; Jin, Chen; He, Xiaowei
2015-10-01
Fluorescence molecular tomography (FMT) is a non-invasive technique that allows three-dimensional visualization of fluorophore in vivo in small animals. In practical applications of FMT, however, there are challenges in the image reconstruction since it is a highly ill-posed problem due to the diffusive behaviour of light transportation in tissue and the limited measurement data. In this paper, we presented an iterative algorithm based on an optimization problem for three dimensional reconstruction of fluorescent target. This method alternates weighted algebraic reconstruction technique (WART) with steepest descent method (SDM) for image reconstruction. Numerical simulations experiments and physical phantom experiment are performed to validate our method. Furthermore, compared to conjugate gradient method, the proposed method provides a better three-dimensional (3D) localization of fluorescent target.
Chiral metamaterial design using optimized pixelated inclusions with genetic algorithm
NASA Astrophysics Data System (ADS)
Akturk, Cemal; Karaaslan, Muharrem; Ozdemir, Ersin; Ozkaner, Vedat; Dincer, Furkan; Bakir, Mehmet; Ozer, Zafer
2015-03-01
Chiral metamaterials have been a research area for many researchers due to their polarization rotation properties on electromagnetic waves. However, most of the proposed chiral metamaterials are designed depending on experience or time-consuming inefficient simulations. A method is investigated for designing a chiral metamaterial with a strong and natural chirality admittance by optimizing a grid of metallic pixels through both sides of a dielectric sheet placed perpendicular to the incident wave by using a genetic algorithm (GA) technique based on finite element method solver. The effective medium parameters are obtained by using constitutive equations and S parameters. The proposed methodology is very efficient for designing a chiral metamaterial with the desired effective medium parameters. By using GA-based topology, it is proven that a chiral metamaterial can be designed and manufactured more easily and with a low cost.
Liu, Liqiang; Dai, Yuntao; Gao, Jinyu
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm.
A sensitivity equation approach to shape optimization in fluid flows
NASA Technical Reports Server (NTRS)
Borggaard, Jeff; Burns, John
1994-01-01
A sensitivity equation method to shape optimization problems is applied. An algorithm is developed and tested on a problem of designing optimal forebody simulators for a 2D, inviscid supersonic flow. The algorithm uses a BFGS/Trust Region optimization scheme with sensitivities computed by numerically approximating the linear partial differential equations that determine the flow sensitivities. Numerical examples are presented to illustrate the method.
Guo, Liyong; Yan, Zhiqiang; Zheng, Xiliang; Hu, Liang; Yang, Yongliang; Wang, Jin
2014-07-01
In protein-ligand docking, an optimization algorithm is used to find the best binding pose of a ligand against a protein target. This algorithm plays a vital role in determining the docking accuracy. To evaluate the relative performance of different optimization algorithms and provide guidance for real applications, we performed a comparative study on six efficient optimization algorithms, containing two evolutionary algorithm (EA)-based optimizers (LGA, DockDE) and four particle swarm optimization (PSO)-based optimizers (SODock, varCPSO, varCPSO-ls, FIPSDock), which were implemented into the protein-ligand docking program AutoDock. We unified the objective functions by applying the same scoring function, and built a new fitness accuracy as the evaluation criterion that incorporates optimization accuracy, robustness, and efficiency. The varCPSO and varCPSO-ls algorithms show high efficiency with fast convergence speed. However, their accuracy is not optimal, as they cannot reach very low energies. SODock has the highest accuracy and robustness. In addition, SODock shows good performance in efficiency when optimizing drug-like ligands with less than ten rotatable bonds. FIPSDock shows excellent robustness and is close to SODock in accuracy and efficiency. In general, the four PSO-based algorithms show superior performance than the two EA-based algorithms, especially for highly flexible ligands. Our method can be regarded as a reference for the validation of new optimization algorithms in protein-ligand docking.
Approach to Complex Upper Extremity Injury: An Algorithm
Ng, Zhi Yang; Askari, Morad; Chim, Harvey
2015-01-01
Patients with complex upper extremity injuries represent a unique subset of the trauma population. In addition to extensive soft tissue defects affecting the skin, bone, muscles and tendons, or the neurovasculature in various combinations, there is usually concomitant involvement of other body areas and organ systems with the potential for systemic compromise due to the underlying mechanism of injury and resultant sequelae. In turn, this has a direct impact on the definitive reconstructive plan. Accurate assessment and expedient treatment is thus necessary to achieve optimal surgical outcomes with the primary goal of limb salvage and functional restoration. Nonetheless, the characteristics of these injuries places such patients at an increased risk of complications ranging from limb ischemia, recalcitrant infections, failure of bony union, intractable pain, and most devastatingly, limb amputation. In this article, the authors present an algorithmic approach toward complex injuries of the upper extremity with due consideration for the various reconstructive modalities and timing of definitive wound closure for the best possible clinical outcomes. PMID:25685098
Liu, Long; Sun, Jun; Xu, Wenbo; Du, Guocheng; Chen, Jian
2009-01-01
Hyaluronic acid (HA) is a natural biopolymer with unique physiochemical and biological properties and finds a wide range of applications in biomedical and cosmetic fields. It is important to increase HA production to meet the increasing HA market demand. This work is aimed to model and optimize the amino acids addition to enhance HA production of Streptococcus zooepidemicus with radial basis function (RBF) neural network coupling quantum-behaved particle swarm optimization (QPSO) algorithm. In the RBF-QPSO approach, RBF neural network is used as a bioprocess modeling tool and QPSO algorithm is applied to conduct the optimization with the established RBF neural network black model as the objective function. The predicted maximum HA yield was 6.92 g/L under the following conditions: arginine 0.062 g/L, cysteine 0.036 g/L, and lysine 0.043 g/L. The optimal amino acids addition allowed HA yield increased from 5.0 g/L of the control to 6.7 g/L in the validation experiments. Moreover, the modeling and optimization capacity of the RBF-QPSO approach was compared with that of response surface methodology (RSM). It was indicated that the RBF-QPSO approach gave a slightly better modeling and optimization result compared with RSM. The developed RBF-QPSO approach in this work may be helpful for the modeling and optimization of the other multivariable, nonlinear, time-variant bioprocesses.
Source mask optimization using real-coded genetic algorithms
NASA Astrophysics Data System (ADS)
Yang, Chaoxing; Wang, Xiangzhao; Li, Sikun; Erdmann, Andreas
2013-04-01
Source mask optimization (SMO) is considered to be one of the technologies to push conventional 193nm lithography to its ultimate limits. In comparison with other SMO methods that use an inverse problem formulation, SMO based on genetic algorithm (GA) requires very little knowledge of the process, and has the advantage of flexible problem formulation. Recent publications on SMO using a GA employ a binary-coded GA. In general, the performance of a GA depends not only on the merit or fitness function, but also on the parameters, operators and their algorithmic implementation. In this paper, we propose a SMO method using real-coded GA where the source and mask solutions are represented by floating point strings instead of bit strings. Besides from that, the selection, crossover, and mutation operators are replaced by corresponding floating-point versions. Both binary-coded and real-coded genetic algorithms were implemented in two versions of SMO and compared in numerical experiments, where the target patterns are staggered contact holes and a logic pattern with critical dimensions of 100 nm, respectively. The results demonstrate the performance improvement of the real-coded GA in comparison to the binary-coded version. Specifically, these improvements can be seen in a better convergence behavior. For example, the numerical experiments for the logic pattern showed that the average number of generations to converge to a proper fitness of 6.0 using the real-coded method is 61.8% (100 generations) less than that using binary-coded method.
A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.
Amoshahy, Mohammad Javad; Shamsi, Mousa; Sedaaghi, Mohammad Hossein
2016-01-01
Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO's parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate.
A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm
Shamsi, Mousa; Sedaaghi, Mohammad Hossein
2016-01-01
Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO’s parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate. PMID:27560945
Adaptation of a Fast Optimal Interpolation Algorithm to the Mapping of Oceangraphic Data
NASA Technical Reports Server (NTRS)
Menemenlis, Dimitris; Fieguth, Paul; Wunsch, Carl; Willsky, Alan
1997-01-01
A fast, recently developed, multiscale optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographic data. This algorithm produces solution and error estimates which are consistent with those obtained from exact least squares methods, but at a small fraction of the computational cost. Problems whose solution would be completely impractical using exact least squares, that is, problems with tens or hundreds of thousands of measurements and estimation grid points, can easily be solved on a small workstation using the multiscale algorithm. In contrast to methods previously proposed for solving large least squares problems, our approach provides estimation error statistics while permitting long-range correlations, using all measurements, and permitting arbitrary measurement locations. The multiscale algorithm itself, published elsewhere, is not the focus of this paper. However, the algorithm requires statistical models having a very particular multiscale structure; it is the development of a class of multiscale statistical models, appropriate for oceanographic mapping problems, with which we concern ourselves in this paper. The approach is illustrated by mapping temperature in the northeastern Pacific. The number of hydrographic stations is kept deliberately small to show that multiscale and exact least squares results are comparable. A portion of the data were not used in the analysis; these data serve to test the multiscale estimates. A major advantage of the present approach is the ability to repeat the estimation procedure a large number of times for sensitivity studies, parameter estimation, and model testing. We have made available by anonymous Ftp a set of MATLAB-callable routines which implement the multiscale algorithm and the statistical models developed in this paper.
Evolutionary Algorithms Approach to the Solution of Damage Detection Problems
NASA Astrophysics Data System (ADS)
Salazar Pinto, Pedro Yoajim; Begambre, Oscar
2010-09-01
In this work is proposed a new Self-Configured Hybrid Algorithm by combining the Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA). The aim of the proposed strategy is to increase the stability and accuracy of the search. The central idea is the concept of Guide Particle, this particle (the best PSO global in each generation) transmits its information to a particle of the following PSO generation, which is controlled by the GA. Thus, the proposed hybrid has an elitism feature that improves its performance and guarantees the convergence of the procedure. In different test carried out in benchmark functions, reported in the international literature, a better performance in stability and accuracy was observed; therefore the new algorithm was used to identify damage in a simple supported beam using modal data. Finally, it is worth noting that the algorithm is independent of the initial definition of heuristic parameters.
Genetic algorithm based image binarization approach and its quantitative evaluation via pooling
NASA Astrophysics Data System (ADS)
Hu, Huijun; Liu, Ya; Liu, Maofu
2015-12-01
The binarized image is very critical to image visual feature extraction, especially shape feature, and the image binarization approaches have been attracted more attentions in the past decades. In this paper, the genetic algorithm is applied to optimizing the binarization threshold of the strip steel defect image. In order to evaluate our genetic algorithm based image binarization approach in terms of quantity, we propose the novel pooling based evaluation metric, motivated by information retrieval community, to avoid the lack of ground-truth binary image. Experimental results show that our genetic algorithm based binarization approach is effective and efficiency in the strip steel defect images and our quantitative evaluation metric on image binarization via pooling is also feasible and practical.
New knowledge-based genetic algorithm for excavator boom structural optimization
NASA Astrophysics Data System (ADS)
Hua, Haiyan; Lin, Shuwen
2014-03-01
Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.
NASA Astrophysics Data System (ADS)
Ryzhikov, I. S.; Semenkin, E. S.; Akhmedova, Sh A.
2017-02-01
A novel order reduction method for linear time invariant systems is described. The method is based on reducing the initial problem to an optimization one, using the proposed model representation, and solving the problem with an efficient optimization algorithm. The proposed method of determining the model allows all the parameters of the model with lower order to be identified and by definition, provides the model with the required steady-state. As a powerful optimization tool, the meta-heuristic Co-Operation of Biology-Related Algorithms was used. Experimental results proved that the proposed approach outperforms other approaches and that the reduced order model achieves a high level of accuracy.
2015-01-01
Optimization and 2) hybrid metaheuristics algorithm comprising a combination of ACO, Genetic Algorithm (GA) and heuristics are proposed and tested on...Optimization, Split Delivery Vehicle Routing Problem, Genetic Algorithm 1. Introduction The Vehicle Routing Problem (VRP) is a prominent problem in the areas...several heuristic methods have been applied to solve the SDVRP, such as a construction heuristic (Wilck and Cavalier, 2012a), a genetic algorithm (Wilck
TH-C-BRD-10: An Evaluation of Three Robust Optimization Approaches in IMPT Treatment Planning
Cao, W; Randeniya, S; Mohan, R; Zaghian, M; Kardar, L; Lim, G; Liu, W
2014-06-15
Purpose: Various robust optimization approaches have been proposed to ensure the robustness of intensity modulated proton therapy (IMPT) in the face of uncertainty. In this study, we aim to investigate the performance of three classes of robust optimization approaches regarding plan optimality and robustness. Methods: Three robust optimization models were implemented in our in-house IMPT treatment planning system: 1) L2 optimization based on worst-case dose; 2) L2 optimization based on minmax objective; and 3) L1 optimization with constraints on all uncertain doses. The first model was solved by a L-BFGS algorithm; the second was solved by a gradient projection algorithm; and the third was solved by an interior point method. One nominal scenario and eight maximum uncertainty scenarios (proton range over and under 3.5%, and setup error of 5 mm for x, y, z directions) were considered in optimization. Dosimetric measurements of optimized plans from the three approaches were compared for four prostate cancer patients retrospectively selected at our institution. Results: For the nominal scenario, all three optimization approaches yielded the same coverage to the clinical treatment volume (CTV) and the L2 worst-case approach demonstrated better rectum and bladder sparing than others. For the uncertainty scenarios, the L1 approach resulted in the most robust CTV coverage against uncertainties, while the plans from L2 worst-case were less robust than others. In addition, we observed that the number of scanning spots with positive MUs from the L2 approaches was approximately twice as many as that from the L1 approach. This indicates that L1 optimization may lead to more efficient IMPT delivery. Conclusion: Our study indicated that the L1 approach best conserved the target coverage in the face of uncertainty but its resulting OAR sparing was slightly inferior to other two approaches.
A new algorithmic approach for fingers detection and identification
NASA Astrophysics Data System (ADS)
Mubashar Khan, Arslan; Umar, Waqas; Choudhary, Taimoor; Hussain, Fawad; Haroon Yousaf, Muhammad
2013-03-01
Gesture recognition is concerned with the goal of interpreting human gestures through mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Hand gesture detection in a real time environment, where the time and memory are important issues, is a critical operation. Hand gesture recognition largely depends on the accurate detection of the fingers. This paper presents a new algorithmic approach to detect and identify fingers of human hand. The proposed algorithm does not depend upon the prior knowledge of the scene. It detects the active fingers and Metacarpophalangeal (MCP) of the inactive fingers from an already detected hand. Dynamic thresholding technique and connected component labeling scheme are employed for background elimination and hand detection respectively. Algorithm proposed a new approach for finger identification in real time environment keeping the memory and time constraint as low as possible.
NASA Astrophysics Data System (ADS)
Kanagaraj, G.; Ponnambalam, S. G.; Jawahar, N.; Mukund Nilakantan, J.
2014-10-01
This article presents an effective hybrid cuckoo search and genetic algorithm (HCSGA) for solving engineering design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. The proposed algorithm, HCSGA, is first applied to 13 standard benchmark constrained optimization functions and subsequently used to solve three well-known design problems reported in the literature. The numerical results obtained by HCSGA show competitive performance with respect to recent algorithms for constrained design optimization problems.
Aerodynamics Design and Genetic Algorithms for Optimization of Airship Bodies
NASA Astrophysics Data System (ADS)
Nejati, Vahid; Matsuuchi, Kazuo
A special and effective aerodynamics calculation method has been applied for the flow field around a body of revolution to find the drag coefficient for a wide range of Reynolds numbers. The body profile is described by a first order continuous axial singularity distribution. The solution of the direct problem then gives the radius and inviscid velocity distribution. Viscous effects are considered by means of an integral boundary layer procedure, and for determination of the transition location the forced transition criterion is applied. By avoiding those profiles, which result in the separation of the boundary layer, the drag can be calculated at the end of the body by using Young's formula. In this study, a powerful optimization procedure known as a Genetic Algorithms (GA) is used for the first time in the shape optimization of airship hulls. GA represents a particular artificial intelligence technique for large spaces, striking a remarkable balance between exploration and exploitation of search space. This method could reach to minimum objective function through a better path, and also could minimize the drag coefficient faster for different Reynolds number regimes. It was found that GA is a powerful method for such multi-dimensional, multi-modal and nonlinear objective function.
Stochastic optimization algorithm for inverse modeling of air pollution
NASA Astrophysics Data System (ADS)
Yeo, Kyongmin; Hwang, Youngdeok; Liu, Xiao; Kalagnanam, Jayant
2016-11-01
A stochastic optimization algorithm to estimate a smooth source function from a limited number of observations is proposed in the context of air pollution, where the source-receptor relation is given by an advection-diffusion equation. First, a smooth source function is approximated by a set of Gaussian kernels on a rectangular mesh system. Then, the generalized polynomial chaos (gPC) expansion is used to represent the model uncertainty due to the choice of the mesh system. It is shown that the convolution of gPC basis and the Gaussian kernel provides hierarchical basis functions for a spectral function estimation. The spectral inverse model is formulated as a stochastic optimization problem. We propose a regularization strategy based on the hierarchical nature of the basis polynomials. It is shown that the spectral inverse model is capable of providing a good estimate of the source function even when the number of unknown parameters (m) is much larger the number of data (n), m/n > 50.
Guan, Xiangmin; Zhang, Xuejun; Zhu, Yanbo; Sun, Dengfeng; Lei, Jiaxing
2015-01-01
Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology. PMID:26180840
NASA Astrophysics Data System (ADS)
Ross, Steven M.
A method is presented to couple and solve the optimal control and the optimal estimation problems simultaneously, allowing systems with bearing-only sensors to maneuver to obtain observability for relative navigation without unnecessarily detracting from a primary mission. A fundamentally new approach to trajectory optimization and the dual control problem is presented, constraining polynomial approximations of the Fisher Information Matrix to provide an information gradient and allow prescription of the level of future estimation certainty required for mission accomplishment. Disturbances, modeling deficiencies, and corrupted measurements are addressed recursively using Radau pseudospectral collocation methods and sequential quadratic programming for the optimal path and an Unscented Kalman Filter for the target position estimate. The underlying real-time optimal control (RTOC) algorithm is developed, specifically addressing limitations of current techniques that lose error integration. The resulting guidance method can be applied to any bearing-only system, such as submarines using passive sonar, anti-radiation missiles, or small UAVs seeking to land on power lines for energy harvesting. System integration, variable timing methods, and discontinuity management techniques are provided for actual hardware implementation. Validation is accomplished with both simulation and flight test, autonomously landing a quadrotor helicopter on a wire.
Adjoint Algorithm for CAD-Based Shape Optimization Using a Cartesian Method
NASA Technical Reports Server (NTRS)
Nemec, Marian; Aftosmis, Michael J.
2004-01-01
Adjoint solutions of the governing flow equations are becoming increasingly important for the development of efficient analysis and optimization algorithms. A well-known use of the adjoint method is gradient-based shape optimization. Given an objective function that defines some measure of performance, such as the lift and drag functionals, its gradient is computed at a cost that is essentially independent of the number of design variables (geometric parameters that control the shape). More recently, emerging adjoint applications focus on the analysis problem, where the adjoint solution is used to drive mesh adaptation, as well as to provide estimates of functional error bounds and corrections. The attractive feature of this approach is that the mesh-adaptation procedure targets a specific functional, thereby localizing the mesh refinement and reducing computational cost. Our focus is on the development of adjoint-based optimization techniques for a Cartesian method with embedded boundaries.12 In contrast t o implementations on structured and unstructured grids, Cartesian methods decouple the surface discretization from the volume mesh. This feature makes Cartesian methods well suited for the automated analysis of complex geometry problems, and consequently a promising approach to aerodynamic optimization. Melvin et developed an adjoint formulation for the TRANAIR code, which is based on the full-potential equation with viscous corrections. More recently, Dadone and Grossman presented an adjoint formulation for the Euler equations. In both approaches, a boundary condition is introduced to approximate the effects of the evolving surface shape that results in accurate gradient computation. Central to automated shape optimization algorithms is the issue of geometry modeling and control. The need to optimize complex, "real-life" geometry provides a strong incentive for the use of parametric-CAD systems within the optimization procedure. In previous work, we presented
Rabotyagov, Sergey; Campbell, Todd; Valcu, Adriana; Gassman, Philip; Jha, Manoj; Schilling, Keith; Wolter, Calvin; Kling, Catherine
2012-12-09
Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,(5,12,20)) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization. Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods(3,4,9,10,13-15,17-19,22,23,25). In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model(7) with a
Rabotyagov, Sergey; Campbell, Todd; Valcu, Adriana; Gassman, Philip; Jha, Manoj; Schilling, Keith; Wolter, Calvin; Kling, Catherine
2012-01-01
Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,5,12,20) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization. Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods3,4,9,10,13-15,17-19,22,23,25. In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model7 with a
Optimized simulations of Olami-Feder-Christensen systems using parallel algorithms
NASA Astrophysics Data System (ADS)
Dominguez, Rachele; Necaise, Rance; Montag, Eric
The sequential nature of the Olami-Feder-Christensen (OFC) model for earthquake simulations limits the benefits of parallel computing approaches because of the frequent communication required between processors. We developed a parallel version of the OFC algorithm for multi-core processors. Our data, even for relatively small system sizes and low numbers of processors, indicates that increasing the number of processors provides significantly faster simulations; producing more efficient results than previous attempts that used network-based Beowulf clusters. Our algorithm optimizes performance by exploiting the multi-core processor architecture, minimizing communication time in contrast to the networked Beowulf-cluster approaches. Our multi-core algorithm is the basis for a new algorithm using GPUs that will drastically increase the number of processors available. Previous studies incorporating realistic structural features of faults into OFC models have revealed spatial and temporal patterns observed in real earthquake systems. The computational advances presented here will allow for studying interacting networks of faults, rather than individual faults, further enhancing our understanding of the relationship between the earth's structure and the triggering process. Support for this project comes from the Chenery Research Fund, the Rashkind Family Endowment, the Walter Williams Craigie Teaching Endowment, and the Schapiro Undergraduate Research Fellowship.
Optimizing communication satellites payload configuration with exact approaches
NASA Astrophysics Data System (ADS)
Stathakis, Apostolos; Danoy, Grégoire; Bouvry, Pascal; Talbi, El-Ghazali; Morelli, Gianluigi
2015-12-01
The satellite communications market is competitive and rapidly evolving. The payload, which is in charge of applying frequency conversion and amplification to the signals received from Earth before their retransmission, is made of various components. These include reconfigurable switches that permit the re-routing of signals based on market demand or because of some hardware failure. In order to meet modern requirements, the size and the complexity of current communication payloads are increasing significantly. Consequently, the optimal payload configuration, which was previously done manually by the engineers with the use of computerized schematics, is now becoming a difficult and time consuming task. Efficient optimization techniques are therefore required to find the optimal set(s) of switch positions to optimize some operational objective(s). In order to tackle this challenging problem for the satellite industry, this work proposes two Integer Linear Programming (ILP) models. The first one is single-objective and focuses on the minimization of the length of the longest channel path, while the second one is bi-objective and additionally aims at minimizing the number of switch changes in the payload switch matrix. Experiments are conducted on a large set of instances of realistic payload sizes using the CPLEX® solver and two well-known exact multi-objective algorithms. Numerical results demonstrate the efficiency and limitations of the ILP approach on this real-world problem.
An Airborne Conflict Resolution Approach Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Mondoloni, Stephane; Conway, Sheila
2001-01-01
An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.
Environmental Monitoring Networks Optimization Using Advanced Active Learning Algorithms
NASA Astrophysics Data System (ADS)
Kanevski, Mikhail; Volpi, Michele; Copa, Loris
2010-05-01
The problem of environmental monitoring networks optimization (MNO) belongs to one of the basic and fundamental tasks in spatio-temporal data collection, analysis, and modeling. There are several approaches to this problem, which can be considered as a design or redesign of monitoring network by applying some optimization criteria. The most developed and widespread methods are based on geostatistics (family of kriging models, conditional stochastic simulations). In geostatistics the variance is mainly used as an optimization criterion which has some advantages and drawbacks. In the present research we study an application of advanced techniques following from the statistical learning theory (SLT) - support vector machines (SVM) and the optimization of monitoring networks when dealing with a classification problem (data are discrete values/classes: hydrogeological units, soil types, pollution decision levels, etc.) is considered. SVM is a universal nonlinear modeling tool for classification problems in high dimensional spaces. The SVM solution is maximizing the decision boundary between classes and has a good generalization property for noisy data. The sparse solution of SVM is based on support vectors - data which contribute to the solution with nonzero weights. Fundamentally the MNO for classification problems can be considered as a task of selecting new measurement points which increase the quality of spatial classification and reduce the testing error (error on new independent measurements). In SLT this is a typical problem of active learning - a selection of the new unlabelled points which efficiently reduce the testing error. A classical approach (margin sampling) to active learning is to sample the points closest to the classification boundary. This solution is suboptimal when points (or generally the dataset) are redundant for the same class. In the present research we propose and study two new advanced methods of active learning adapted to the solution of
NASA Astrophysics Data System (ADS)
Praveena, P.; Vaisakh, K.; Rama Mohana Rao, S.
The Dynamic economic dispatch (DED) problem is an optimization problem with an objective to determine the optimal combination of power outputs for all generating units over a certain period of time in order to minimize the total fuel cost while satisfying dynamic operational constraints and load demand in each interval. Recently social foraging behavior of Escherichia coli bacteria has been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA) is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real-world optimization problems. This article comes up with a hybrid approach involving Particle Swarm Optimization (PSO) and BFO algorithms with varying chemo tactic step size for solving the DED problem of generating units considering valve-point effects. The proposed hybrid algorithm has been extensively compared with those methods reported in the literature. The new method is shown to be statistically significantly better on two test systems consisting of five and ten generating units.
Double-layer evolutionary algorithm for distributed optimization of particle detection on the Grid
NASA Astrophysics Data System (ADS)
Padée, Adam; Kurek, Krzysztof; Zaremba, Krzysztof
2013-08-01
Reconstruction of particle tracks from information collected by position-sensitive detectors is an important procedure in HEP experiments. It is usually controlled by a set of numerical parameters which have to be manually optimized. This paper proposes an automatic approach to this task by utilizing evolutionary algorithm (EA) operating on both real-valued and binary representations. Because of computational complexity of the task a special distributed architecture of the algorithm is proposed, designed to be run in grid environment. It is two-level hierarchical hybrid utilizing asynchronous master-slave EA on the level of clusters and island model EA on the level of the grid. The technical aspects of usage of production grid infrastructure are covered, including communication protocols on both levels. The paper deals also with the problem of heterogeneity of the resources, presenting efficiency tests on a benchmark function. These tests confirm that even relatively small islands (clusters) can be beneficial to the optimization process when connected to the larger ones. Finally a real-life usage example is presented, which is an optimization of track reconstruction in Large Angle Spectrometer of NA-58 COMPASS experiment held at CERN, using a sample of Monte Carlo simulated data. The overall reconstruction efficiency gain, achieved by the proposed method, is more than 4%, compared to the manually optimized parameters.
A Mechanobiology-based Algorithm to Optimize the Microstructure Geometry of Bone Tissue Scaffolds
Boccaccio, Antonio; Uva, Antonio Emmanuele; Fiorentino, Michele; Lamberti, Luciano; Monno, Giuseppe
2016-01-01
Complexity of scaffold geometries and biological mechanisms involved in the bone generation process make the design of scaffolds a quite challenging task. The most common approaches utilized in bone tissue engineering require costly protocols and time-consuming experiments. In this study we present an algorithm that, combining parametric finite element models of scaffolds with numerical optimization methods and a computational mechano-regulation model, is able to predict the optimal scaffold microstructure. The scaffold geometrical parameters are perturbed until the best geometry that allows the largest amounts of bone to be generated, is reached. We study the effects of the following factors: (1) the shape of the pores; (2) their spatial distribution; (3) the number of pores per unit area. The optimal dimensions of the pores have been determined for different values of scaffold Young's modulus and compression loading acting on the scaffold upper surface. Pores with rectangular section were predicted to lead to the formation of larger amounts of bone compared to square section pores; similarly, elliptic pores were predicted to allow the generation of greater amounts of bone compared to circular pores. The number of pores per unit area appears to have rather negligible effects on the bone regeneration process. Finally, the algorithm predicts that for increasing loads, increasing values of the scaffold Young's modulus are preferable. The results shown in the article represent a proof-of-principle demonstration of the possibility to optimize the scaffold microstructure geometry based on mechanobiological criteria. PMID:26722213
A Simulation Optimization Approach to Epidemic Forecasting
Nsoesie, Elaine O.; Beckman, Richard J.; Shashaani, Sara; Nagaraj, Kalyani S.; Marathe, Madhav V.
2013-01-01
Reliable forecasts of influenza can aid in the control of both seasonal and pandemic outbreaks. We introduce a simulation optimization (SIMOP) approach for forecasting the influenza epidemic curve. This study represents the final step of a project aimed at using a combination of simulation, classification, statistical and optimization techniques to forecast the epidemic curve and infer underlying model parameters during an influenza outbreak. The SIMOP procedure combines an individual-based model and the Nelder-Mead simplex optimization method. The method is used to forecast epidemics simulated over synthetic social networks representing Montgomery County in Virginia, Miami, Seattle and surrounding metropolitan regions. The results are presented for the first four weeks. Depending on the synthetic network, the peak time could be predicted within a 95% CI as early as seven weeks before the actual peak. The peak infected and total infected were also accurately forecasted for Montgomery County in Virginia within the forecasting period. Forecasting of the epidemic curve for both seasonal and pandemic influenza outbreaks is a complex problem, however this is a preliminary step and the results suggest that more can be achieved in this area. PMID:23826222
Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms
Casillas, Myrna V.; Puig, Vicenç; Garza-Castañón, Luis E.; Rosich, Albert
2013-01-01
This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach. PMID:24193099
NASA Astrophysics Data System (ADS)
Campo, Lorenzo; Castelli, Fabio; Caparrini, Francesca
2010-05-01
The modern distributed hydrological models allow the representation of the different surface and subsurface phenomena with great accuracy and high spatial and temporal resolution. Such complexity requires, in general, an equally accurate parametrization. A number of approaches have been followed in this respect, from simple local search method (like Nelder-Mead algorithm), that minimize a cost function representing some distance between model's output and available measures, to more complex approaches like dynamic filters (such as the Ensemble Kalman Filter) that carry on an assimilation of the observations. In this work the first approach was followed in order to compare the performances of three different direct search algorithms on the calibration of a distributed hydrological balance model. The direct search family can be defined as that category of algorithms that make no use of derivatives of the cost function (that is, in general, a black box) and comprehend a large number of possible approaches. The main benefit of this class of methods is that they don't require changes in the implementation of the numerical codes to be calibrated. The first algorithm is the classical Nelder-Mead, often used in many applications and utilized as reference. The second algorithm is a GSS (Generating Set Search) algorithm, built in order to guarantee the conditions of global convergence and suitable for a parallel and multi-start implementation, here presented. The third one is the EGO algorithm (Efficient Global Optimization), that is particularly suitable to calibrate black box cost functions that require expensive computational resource (like an hydrological simulation). EGO minimizes the number of evaluations of the cost function balancing the need to minimize a response surface that approximates the problem and the need to improve the approximation sampling where prediction error may be high. The hydrological model to be calibrated was MOBIDIC, a complete balance
Fisz, Jacek J
2006-12-07
The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi
A formulation of a matrix sparsity approach for the quantum ordered search algorithm
NASA Astrophysics Data System (ADS)
Parmar, Jupinder; Rahman, Saarim; Thiara, Jaskaran
One specific subset of quantum algorithms is Grovers Ordered Search Problem (OSP), the quantum counterpart of the classical binary search algorithm, which utilizes oracle functions to produce a specified value within an ordered database. Classically, the optimal algorithm is known to have a log2N complexity; however, Grovers algorithm has been found to have an optimal complexity between the lower bound of ((lnN‑1)/π≈0.221log2N) and the upper bound of 0.433log2N. We sought to lower the known upper bound of the OSP. With Farhi et al. MITCTP 2815 (1999), arXiv:quant-ph/9901059], we see that the OSP can be resolved into a translational invariant algorithm to create quantum query algorithm restraints. With these restraints, one can find Laurent polynomials for various k — queries — and N — database sizes — thus finding larger recursive sets to solve the OSP and effectively reducing the upper bound. These polynomials are found to be convex functions, allowing one to make use of convex optimization to find an improvement on the known bounds. According to Childs et al. [Phys. Rev. A 75 (2007) 032335], semidefinite programming, a subset of convex optimization, can solve the particular problem represented by the constraints. We were able to implement a program abiding to their formulation of a semidefinite program (SDP), leading us to find that it takes an immense amount of storage and time to compute. To combat this setback, we then formulated an approach to improve results of the SDP using matrix sparsity. Through the development of this approach, along with an implementation of a rudimentary solver, we demonstrate how matrix sparsity reduces the amount of time and storage required to compute the SDP — overall ensuring further improvements will likely be made to reach the theorized lower bound.
NASA Astrophysics Data System (ADS)
Ogren, Ryan M.
For this work, Hybrid PSO-GA and Artificial Bee Colony Optimization (ABC) algorithms are applied to the optimization of experimental diesel engine performance, to meet Environmental Protection Agency, off-road, diesel engine standards. This work is the first to apply ABC optimization to experimental engine testing. All trials were conducted at partial load on a four-cylinder, turbocharged, John Deere engine using neat-Biodiesel for PSO-GA and regular pump diesel for ABC. Key variables were altered throughout the experiments, including, fuel pressure, intake gas temperature, exhaust gas recirculation flow, fuel injection quantity for two injections, pilot injection timing and main injection timing. Both forms of optimization proved effective for optimizing engine operation. The PSO-GA hybrid was able to find a superior solution to that of ABC within fewer engine runs. Both solutions call for high exhaust gas recirculation to reduce oxide of nitrogen (NOx) emissions while also moving pilot and main fuel injections to near top dead center for improved tradeoffs between NOx and particulate matter.
Optimizations Of Coat-Hanger Die, Using Constraint Optimization Algorithm And Taguchi Method
NASA Astrophysics Data System (ADS)
Lebaal, Nadhir; Schmidt, Fabrice; Puissant, Stephan
2007-05-01
Polymer extrusion is one of the most important manufacturing methods used today. A flat die, is commonly used to extrude thin thermoplastics sheets. If the channel geometry in a flat die is not designed properly, the velocity at the die exit may be perturbed, which can affect the thickness across the width of the die. The ultimate goal of this work is to optimize the die channel geometry in a way that a uniform velocity distribution is obtained at the die exit. While optimizing the exit velocity distribution, we have coupled three-dimensional extrusion simulation software Rem3D®, with an automatic constraint optimization algorithm to control the maximum allowable pressure drop in the die; according to this constraint we can control the pressure in the die (decrease the pressure while minimizing the velocity dispersion across the die exit). For this purpose, we investigate the effect of the design variables in the objective and constraint function by using Taguchi method. In the second study we use the global response surface method with Kriging interpolation to optimize flat die geometry. Two optimization results are presented according to the imposed constraint on the pressure. The optimum is obtained with a very fast convergence (2 iterations). To respect the constraint while ensuring a homogeneous distribution of velocity, the results with a less severe constraint offers the best minimum.
Discrete-valued-pulse optimal control algorithms: Application to spin systems
NASA Astrophysics Data System (ADS)
Dridi, G.; Lapert, M.; Salomon, J.; Glaser, S. J.; Sugny, D.
2015-10-01
This article is aimed at extending the framework of optimal control techniques to the situation where the control field values are restricted to a finite set. We propose generalizations of the standard GRAPE algorithm suited to this constraint. We test the validity and the efficiency of this approach for the inversion of an inhomogeneous ensemble of spin systems with different offset frequencies. It is shown that a remarkable efficiency can be achieved even for a very limited number of discrete values. Some applications in nuclear magnetic resonance are discussed.
Searching for the Optimal Working Point of the MEIC at JLab Using an Evolutionary Algorithm
Balsa Terzic, Matthew Kramer, Colin Jarvis
2011-03-01
The Medium-energy Electron Ion Collider (MEIC), a proposed medium-energy ring-ring electron-ion collider based on CEBAF at Jefferson Lab. The collider luminosity and stability are sensitive to the choice of a working point - the betatron and synchrotron tunes of the two colliding beams. Therefore, a careful selection of the working point is essential for stable operation of the collider, as well as for achieving high luminosity. Here we describe a novel approach for locating an optimal working point based on evolutionary algorithm techniques.
NASA Astrophysics Data System (ADS)
Abdeljaber, Osama; Avci, Onur; Kiranyaz, Serkan; Inman, Daniel J.
2017-02-01
Vibration suppression remains a crucial issue in the design of structures and machines. Recent studies have shown that with the use of metamaterial inspired structures (or metastructures), considerable vibration attenuation can be achieved. Optimization of the internal geometry of metastructures maximizes the suppression performance. Zigzag inserts have been reported to be efficient for vibration attenuation. It has also been reported that the geometric parameters of the inserts affect the vibration suppression performance in a complex manner. In an attempt to find out the most efficient parameters, an optimization study has been conducted on the linear zigzag inserts and is presented here. The research reported in this paper aims at developing an automated method for determining the geometry of zigzag inserts through optimization. This genetic algorithm based optimization process searches for optimal zigzag designs which are properly tuned to suppress vibrations when inserted in a specific host structure (cantilever beam). The inserts adopted in this study consist of a cantilever zigzag structure with a mass attached to its unsupported tip. Numerical simulations are carried out to demonstrate the efficiency of the proposed zigzag optimization approach.
Simultaneous optimization of micro-heliostat geometry and field layout using a genetic algorithm
NASA Astrophysics Data System (ADS)
Lazardjani, Mani Yousefpour; Kronhardt, Valentina; Dikta, Gerhard; Göttsche, Joachim
2016-05-01
A new optimization tool for micro-heliostat (MH) geometry and field layout is presented. The method intends simultaneous performance improvement and cost reduction through iteration of heliostat geometry and field layout parameters. This tool was developed primarily for the optimization of a novel micro-heliostat concept, which was developed at Solar-Institut Jülich (SIJ). However, the underlying approach for the optimization can be used for any heliostat type. During the optimization the performance is calculated using the ray-tracing tool SolCal. The costs of the heliostats are calculated by use of a detailed cost function. A genetic algorithm is used to change heliostat geometry and field layout in an iterative process. Starting from an initial setup, the optimization tool generates several configurations of heliostat geometries and field layouts. For each configuration a cost-performance ratio is calculated. Based on that, the best geometry and field layout can be selected in each optimization step. In order to find the best configuration, this step is repeated until no significant improvement in the results is observed.
Optimization of the double dosimetry algorithm for interventional cardiologists
NASA Astrophysics Data System (ADS)
Chumak, Vadim; Morgun, Artem; Bakhanova, Elena; Voloskiy, Vitalii; Borodynchik, Elena
2014-11-01
A double dosimetry method is recommended in interventional cardiology (IC) to assess occupational exposure; yet currently there is no common and universal algorithm for effective dose estimation. In this work, flexible and adaptive algorithm building methodology was developed and some specific algorithm applicable for typical irradiation conditions of IC procedures was obtained. It was shown that the obtained algorithm agrees well with experimental measurements and is less conservative compared to other known algorithms.
Cordella, F; Zollo, L; Salerno, A; Guglielmelli, E; Siciliano, B
2011-01-01
Taking inspiration from neurophysiological studies on synergies in the human grasping action, this paper tries to demonstrate that it is possible to find a general rule for performing a stable, human-like cylindrical grasp with a robotic hand. To this purpose, the theoretical formulation and the experimental validation of a reach-and-grasp algorithm for determining the optimal hand position and the optimal finger configuration for grasping a cylindrical object with known features are presented. The proposed algorithm is based on the minimization of an objective function expressed by the sum of the distances of the hand joints from the object surface. Algorithm effectiveness has preliminarily been tested by means of simulation trials. Experimental trials on a real arm-hand robotic system have then been carried out in order to validate the approach and evaluate algorithm performance.
Wang, Hong; Wang, Xicheng; Li, Zheng; Li, Keqiu
2016-01-01
The metabolic network model allows for an in-depth insight into the molecular mechanism of a particular organism. Because most parameters of the metabolic network cannot be directly measured, they must be estimated by using optimization algorithms. However, three characteristics of the metabolic network model, i.e., high nonlinearity, large amount parameters, and huge variation scopes of parameters, restrict the application of many traditional optimization algorithms. As a result, there is a growing demand to develop efficient optimization approaches to address this complex problem. In this paper, a Kriging-based algorithm aiming at parameter estimation is presented for constructing the metabolic networks. In the algorithm, a new infill sampling criterion, named expected improvement and mutual information (EI&MI), is adopted to improve the modeling accuracy by selecting multiple new sample points at each cycle, and the domain decomposition strategy based on the principal component analysis is introduced to save computing time. Meanwhile, the convergence speed is accelerated by combining a single-dimensional optimization method with the dynamic coordinate perturbation strategy when determining the new sample points. Finally, the algorithm is applied to the arachidonic acid metabolic network to estimate its parameters. The obtained results demonstrate the effectiveness of the proposed algorithm in getting precise parameter values under a limited number of iterations.
Evaluation of a new approach for speech enhancement algorithms in hearing aids.
Montazeri, Vahid; Khoubrouy, Soudeh A; Panahi, Issa M S
2012-01-01
Several studies on hearing impaired people who use hearing aid reveal that speech enhancement algorithms implemented in hearing aids improve listening comfort. However, these algorithms do not improve speech intelligibility too much and in many cases they decrease the speech intelligibility, both in hearing-impaired and in normally hearing people. In fact, current approaches for development of the speech enhancement algorithms (e.g. minimum mean square error (MMSE)) are not optimal for intelligibility improvement. Some recent studies investigated the effect of different distortions on the enhanced speech and realized that by controlling the amplification distortion, the intelligibility improves dramatically. In this paper, we examined, subjectively and objectively, the effects of amplification distortion on the speech enhanced by two algorithms in three background noises at different SNR levels.
Ultra-fast fluence optimization for beam angle selection algorithms
NASA Astrophysics Data System (ADS)
Bangert, M.; Ziegenhein, P.; Oelfke, U.
2014-03-01
Beam angle selection (BAS) including fluence optimization (FO) is among the most extensive computational tasks in radiotherapy. Precomputed dose influence data (DID) of all considered beam orientations (up to 100 GB for complex cases) has to be handled in the main memory and repeated FOs are required for different beam ensembles. In this paper, the authors describe concepts accelerating FO for BAS algorithms using off-the-shelf multiprocessor workstations. The FO runtime is not dominated by the arithmetic load of the CPUs but by the transportation of DID from the RAM to the CPUs. On multiprocessor workstations, however, the speed of data transportation from the main memory to the CPUs is non-uniform across the RAM; every CPU has a dedicated memory location (node) with minimum access time. We apply a thread node binding strategy to ensure that CPUs only access DID from their preferred node. Ideal load balancing for arbitrary beam ensembles is guaranteed by distributing the DID of every candidate beam equally to all nodes. Furthermore we use a custom sorting scheme of the DID to minimize the overall data transportation. The framework is implemented on an AMD Opteron workstation. One FO iteration comprising dose, objective function, and gradient calculation takes between 0.010 s (9 beams, skull, 0.23 GB DID) and 0.070 s (9 beams, abdomen, 1.50 GB DID). Our overall FO time is < 1 s for small cases, larger cases take ~ 4 s. BAS runs including FOs for 1000 different beam ensembles take ~ 15-70 min, depending on the treatment site. This enables an efficient clinical evaluation of different BAS algorithms.
Optimized Uncertainty Quantification Algorithm Within a Dynamic Event Tree Framework
J. W. Nielsen; Akira Tokuhiro; Robert Hiromoto
2014-06-01
Methods for developing Phenomenological Identification and Ranking Tables (PIRT) for nuclear power plants have been a useful tool in providing insight into modelling aspects that are important to safety. These methods have involved expert knowledge with regards to reactor plant transients and thermal-hydraulic codes to identify are of highest importance. Quantified PIRT provides for rigorous method for quantifying the phenomena that can have the greatest impact. The transients that are evaluated and the timing of those events are typically developed in collaboration with the Probabilistic Risk Analysis. Though quite effective in evaluating risk, traditional PRA methods lack the capability to evaluate complex dynamic systems where end states may vary as a function of transition time from physical state to physical state . Dynamic PRA (DPRA) methods provide a more rigorous analysis of complex dynamic systems. A limitation of DPRA is its potential for state or combinatorial explosion that grows as a function of the number of components; as well as, the sampling of transition times from state-to-state of the entire system. This paper presents a method for performing QPIRT within a dynamic event tree framework such that timing events which result in the highest probabilities of failure are captured and a QPIRT is performed simultaneously while performing a discrete dynamic event tree evaluation. The resulting simulation results in a formal QPIRT for each end state. The use of dynamic event trees results in state explosion as the number of possible component states increases. This paper utilizes a branch and bound algorithm to optimize the solution of the dynamic event trees. The paper summarizes the methods used to implement the branch-and-bound algorithm in solving the discrete dynamic event trees.
The optimal extraction of feature algorithm based on KAZE
NASA Astrophysics Data System (ADS)
Yao, Zheyi; Gu, Guohua; Qian, Weixian; Wang, Pengcheng
2015-10-01
As a novel method of 2D features extraction algorithm over the nonlinear scale space, KAZE provide a special method. However, the computation of nonlinear scale space and the construction of KAZE feature vectors are more expensive than the SIFT and SURF significantly. In this paper, the given image is used to build the nonlinear space up to a maximum evolution time through the efficient Additive Operator Splitting (AOS) techniques and the variable conductance diffusion. Changing the parameter can improve the construction of nonlinear scale space and simplify the image conductivities for each dimension space, with the predigest computation. Then, the detection for points of interest can exhibit a maxima of the scale-normalized determinant with the Hessian response in the nonlinear scale space. At the same time, the detection of feature vectors is optimized by the Wavelet Transform method, which can avoid the second Gaussian smoothing in the KAZE Features and cut down the complexity of the algorithm distinctly in the building and describing vectors steps. In this way, the dominant orientation is obtained, similar to SURF, by summing the responses within a sliding circle segment covering an angle of π/3 in the circular area of radius 6σ with a sampling step of size σ one by one. Finally, the extraction in the multidimensional patch at the given scale, centered over the points of interest and rotated to align its dominant orientation to a canonical direction, is able to simplify the description of feature by reducing the description dimensions, just as the PCA-SIFT method. Even though the features are somewhat more expensive to compute than SIFT due to the construction of nonlinear scale space, but compared to SURF, the result revels a step forward in performance in detection, description and application against the previous ways by the following contrast experiments.
NASA Astrophysics Data System (ADS)
Tsoukalas, Ioannis; Kossieris, Panagiotis; Efstratiadis, Andreas; Makropoulos, Christos
2015-04-01
In water resources optimization problems, the calculation of the objective function usually presumes to first run a simulation model and then evaluate its outputs. In several cases, however, long simulation times may pose significant barriers to the optimization procedure. Often, to obtain a solution within a reasonable time, the user has to substantially restrict the allowable number of function evaluations, thus terminating the search much earlier than required by the problem's complexity. A promising novel strategy to address these shortcomings is the use of surrogate modelling techniques within global optimization algorithms. Here we introduce the Surrogate-Enhanced Evolutionary Annealing-Simplex (SE-EAS) algorithm that couples the strengths of surrogate modelling with the effectiveness and efficiency of the EAS method. The algorithm combines three different optimization approaches (evolutionary search, simulated annealing and the downhill simplex search scheme), in which key decisions are partially guided by numerical approximations of the objective function. The performance of the proposed algorithm is benchmarked against other surrogate-assisted algorithms, in both theoretical and practical applications (i.e. test functions and hydrological calibration problems, respectively), within a limited budget of trials (from 100 to 1000). Results reveal the significant potential of using SE-EAS in challenging optimization problems, involving time-consuming simulations.
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.
Thounaojam, Dalton Meitei; Khelchandra, Thongam; Manglem Singh, Kh; Roy, Sudipta
2016-01-01
This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.
Genetics algorithm optimization of DWT-DCT based image Watermarking
NASA Astrophysics Data System (ADS)
Budiman, Gelar; Novamizanti, Ledya; Iwut, Iwan
2017-01-01
Data hiding in an image content is mandatory for setting the ownership of the image. Two dimensions discrete wavelet transform (DWT) and discrete cosine transform (DCT) are proposed as transform method in this paper. First, the host image in RGB color space is converted to selected color space. We also can select the layer where the watermark is embedded. Next, 2D-DWT transforms the selected layer obtaining 4 subband. We select only one subband. And then block-based 2D-DCT transforms the selected subband. Binary-based watermark is embedded on the AC coefficients of each block after zigzag movement and range based pixel selection. Delta parameter replacing pixels in each range represents embedded bit. +Delta represents bit “1” and –delta represents bit “0”. Several parameters to be optimized by Genetics Algorithm (GA) are selected color space, layer, selected subband of DWT decomposition, block size, embedding range, and delta. The result of simulation performs that GA is able to determine the exact parameters obtaining optimum imperceptibility and robustness, in any watermarked image condition, either it is not attacked or attacked. DWT process in DCT based image watermarking optimized by GA has improved the performance of image watermarking. By five attacks: JPEG 50%, resize 50%, histogram equalization, salt-pepper and additive noise with variance 0.01, robustness in the proposed method has reached perfect watermark quality with BER=0. And the watermarked image quality by PSNR parameter is also increased about 5 dB than the watermarked image quality from previous method.
Ju, Chunhua
2013-01-01
Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods. PMID:24381525
NASA Technical Reports Server (NTRS)
Pulliam, T. H.; Nemec, M.; Holst, T.; Zingg, D. W.; Kwak, Dochan (Technical Monitor)
2002-01-01
A comparison between an Evolutionary Algorithm (EA) and an Adjoint-Gradient (AG) Method applied to a two-dimensional Navier-Stokes code for airfoil design is presented. Both approaches use a common function evaluation code, the steady-state explicit part of the code,ARC2D. The parameterization of the design space is a common B-spline approach for an airfoil surface, which together with a common griding approach, restricts the AG and EA to the same design space. Results are presented for a class of viscous transonic airfoils in which the optimization tradeoff between drag minimization as one objective and lift maximization as another, produces the multi-objective design space. Comparisons are made for efficiency, accuracy and design consistency.
Improved mine blast algorithm for optimal cost design of water distribution systems
NASA Astrophysics Data System (ADS)
Sadollah, Ali; Guen Yoo, Do; Kim, Joong Hoon
2015-12-01
The design of water distribution systems is a large class of combinatorial, nonlinear optimization problems with complex constraints such as conservation of mass and energy equations. Since feasible solutions are often extremely complex, traditional optimization techniques are insufficient. Recently, metaheuristic algorithms have been applied to this class of problems because they are highly efficient. In this article, a recently developed optimizer called the mine blast algorithm (MBA) is considered. The MBA is improved and coupled with the hydraulic simulator EPANET to find the optimal cost design for water distribution systems. The performance of the improved mine blast algorithm (IMBA) is demonstrated using the well-known Hanoi, New York tunnels and Balerma benchmark networks. Optimization results obtained using IMBA are compared to those using MBA and other optimizers in terms of their minimum construction costs and convergence rates. For the complex Balerma network, IMBA offers the cheapest network design compared to other optimization algorithms.
Multi-objective global optimization of a butterfly valve using genetic algorithms.
Corbera, Sergio; Olazagoitia, José Luis; Lozano, José Antonio
2016-07-01
A butterfly valve is a type of valve typically used for isolating or regulating flow where the closing mechanism takes the form of a disc. For a long time, the attention of many researchers has focused on carrying out structural (FEM) and computational fluid dynamics (CFD) analysis in order to increase the performance of this type of flow-control device. This paper proposes a novel multi-objective approach for the design optimization of a butterfly valve using advanced genetic algorithms based on Pareto dominance. Firstly, after defining the need for this study and analyzing previous papers on the subject, the initial butterfly valve is presented and the initial fluid and structural analysis are carried out. Secondly, the optimization problem is defined and the optimization strategy is presented. The design variables are identified and a parameterization model of the valve is made. Thirdly, initial design candidates are generated by DOE and design optimization using genetic algorithms is performed. In this part of the process structural and CFD analysis are calculated for each candidate simultaneously. The optimization process involves various types of software and Python scripts are needed for their interaction and the connection of all steps. Finally, a set of optimal solutions is obtained and the optimum design that provides a 65.4% stress reduction, a 5% mass reduction and a 11.3% flow increase is selected in accordance with manufacturer preferences. Validation of the results is provided by comparing experimental test results with the values obtained for the initial design. The results demonstrate the capability and potential of the proposed methodology.
Probabilistic-based approach to optimal filtering
Hannachi
2000-04-01
The signal-to-noise ratio maximizing approach in optimal filtering provides a robust tool to detect signals in the presence of colored noise. The method fails, however, when the data present a regimelike behavior. An approach is developed in this manuscript to recover local (in phase space) behavior in an intermittent regimelike behaving system. The method is first formulated in its general form within a Gaussian framework, given an estimate of the noise covariance, and demands that the signal corresponds to minimizing the noise probability distribution for any given value, i.e., on isosurfaces, of the data probability distribution. The extension to the non-Gaussian case is provided through the use of finite mixture models for data that show regimelike behavior. The method yields the correct signal when applied in a simplified manner to synthetic time series with and without regimes, compared to the signal-to-noise ratio approach, and helps identify the right frequency of the oscillation spells in the classical and variants of the Lorenz system.
A genetic algorithm for optimizing multi-pole Debye models of tissue dielectric properties.
Clegg, J; Robinson, M P
2012-10-07
Models of tissue dielectric properties (permittivity and conductivity) enable the interactions of tissues and electromagnetic fields to be simulated, which has many useful applications in microwave imaging, radio propagation, and non-ionizing radiation dosimetry. Parametric formulae are available, based on a multi-pole model of tissue dispersions, but although they give the dielectric properties over a wide frequency range, they do not convert easily to the time domain. An alternative is the multi-pole Debye model which works well in both time and frequency domains. Genetic algorithms are an evolutionary approach to optimization, and we found that this technique was effective at finding the best values of the multi-Debye parameters. Our genetic algorithm optimized these parameters to fit to either a Cole-Cole model or to measured data, and worked well over wide or narrow frequency ranges. Over 10 Hz-10 GHz the best fits for muscle, fat or bone were each found for ten dispersions or poles in the multi-Debye model. The genetic algorithm is a fast and effective method of developing tissue models that compares favourably with alternatives such as the rational polynomial fit.
A genetic algorithm for optimizing multi-pole Debye models of tissue dielectric properties
NASA Astrophysics Data System (ADS)
Clegg, J.; Robinson, M. P.
2012-10-01
Models of tissue dielectric properties (permittivity and conductivity) enable the interactions of tissues and electromagnetic fields to be simulated, which has many useful applications in microwave imaging, radio propagation, and non-ionizing radiation dosimetry. Parametric formulae are available, based on a multi-pole model of tissue dispersions, but although they give the dielectric properties over a wide frequency range, they do not convert easily to the time domain. An alternative is the multi-pole Debye model which works well in both time and frequency domains. Genetic algorithms are an evolutionary approach to optimization, and we found that this technique was effective at finding the best values of the multi-Debye parameters. Our genetic algorithm optimized these parameters to fit to either a Cole-Cole model or to measured data, and worked well over wide or narrow frequency ranges. Over 10 Hz-10 GHz the best fits for muscle, fat or bone were each found for ten dispersions or poles in the multi-Debye model. The genetic algorithm is a fast and effective method of developing tissue models that compares favourably with alternatives such as the rational polynomial fit.
Yang, Zhen-Lun; Wu, Angus; Min, Hua-Qing
2015-01-01
An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate.
Optimizing the lithography model calibration algorithms for NTD process
NASA Astrophysics Data System (ADS)
Hu, C. M.; Lo, Fred; Yang, Elvis; Yang, T. H.; Chen, K. C.
2016-03-01
As patterns shrink to the resolution limits of up-to-date ArF immersion lithography technology, negative tone development (NTD) process has been an increasingly adopted technique to get superior imaging quality through employing bright-field (BF) masks to print the critical dark-field (DF) metal and contact layers. However, from the fundamental materials and process interaction perspectives, several key differences inherently exist between NTD process and the traditional positive tone development (PTD) system, especially the horizontal/vertical resist shrinkage and developer depletion effects, hence the traditional resist parameters developed for the typical PTD process have no longer fit well in NTD process modeling. In order to cope with the inherent differences between PTD and NTD processes accordingly get improvement on NTD modeling accuracy, several NTD models with different combinations of complementary terms were built to account for the NTD-specific resist shrinkage, developer depletion and diffusion, and wafer CD jump induced by sub threshold assistance feature (SRAF) effects. Each new complementary NTD term has its definite aim to deal with the NTD-specific phenomena. In this study, the modeling accuracy is compared among different models for the specific patterning characteristics on various feature types. Multiple complementary NTD terms were finally proposed to address all the NTD-specific behaviors simultaneously and further optimize the NTD modeling accuracy. The new algorithm of multiple complementary NTD term tested on our critical dark-field layers demonstrates consistent model accuracy improvement for both calibration and verification.
Optimal placement of active material actuators using genetic algorithm
NASA Astrophysics Data System (ADS)
Johnson, Terrence; Frecker, Mary I.
2004-07-01
Actuators based on smart materials generally exhibit a tradeoff between force and stroke. Researchers have surrounded piezoelectric materials (PZT"s) with complaint structures to magnify either their geometric or mechanical advantage. Most of these designs are literally built around a particular piezoelectric device, so the design space consists of only the compliant mechanism. Materials scientists researchers have demonstrated the ability to pole a PZT in an arbitrary direction, and some engineers have taken advantage of this to build "shear mode" actuators. The goal of this work is to determine if the performance of compliant mechanisms improves by the inclusion of the piezoelectric polarization as a design variable. The polarization vector is varied via transformation matrixes, and the compliant actuator is modeled using the SIMP (Solid Isotropic Material with Penalization) or "power-law method." The concept of mutual potential energy is used to form an objective function to measure the piezoelectric actuator"s performance. The optimal topology of the compliant mechanism and orientation of the polarization method are determined using a sequential linear programming algorithm. This paper presents a demonstration problem that shows small changes in the polarization vector have a marginal effect on the optimum topology of the mechanism, but improves actuation.
Using Hypertext To Develop an Algorithmic Approach to Teaching Statistics.
ERIC Educational Resources Information Center
Halavin, James; Sommer, Charles
Hypertext and its more advanced form Hypermedia represent a powerful authoring tool with great potential for allowing statistics teachers to develop documents to assist students in an algorithmic fashion. An introduction to the use of Hypertext is presented, with an example of its use. Hypertext is an approach to information management in which…
ERIC Educational Resources Information Center
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
2016-01-01
In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…
Optimization of Ocean Color Algorithms: Application to Satellite Data Merging
NASA Technical Reports Server (NTRS)
Ritorena, Stephane; Siegel, David A.; Morel, Andre
2004-01-01
The objective of the program is to develop and validate a procedure for ocean color data merging, which is one of the major goals of the SIMBIOS project. As part of the SIMBIOS Program, we have developed a merging method for ocean color data. Conversely to other methods our approach does not combine end-products like the subsurface chlorophyll concentration (chl) from different sensors to generate a unified product. Instead, our procedure uses the normalized water-leaving radiances L((sub wN)(lambda)) from single or multiple sensors and uses them in the inversion of a semi-analytical ocean color model that allows the retrieval of several ocean color variables simultaneously. Beside ensuring simultaneity and consistency of the retrievals (all products are derived from a single algorithm), this model-based approach has various benefits over techniques that blend end-products (e.g. chlorophyll): 1) It works with single or multiple data sources regardless of their specific bands; 2) It exploits band redundancies and band differences; 3) It accounts for uncertainties in the L((sub wN)(lambda)) data; 4) It provides uncertainty estimates for the retrieved variables.
2004-09-01
optimization problems with stochastic objective functions and a mixture of design variable types. The generalized pattern search (GPS) class of algorithms is...provide computational enhancements to the basic algorithm. Im- plementation alternatives include the use of modern R&S procedures designed to provide...83 vii Page 4.3 Termination Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.4 Algorithm Design
Discrete bat algorithm for optimal problem of permutation flow shop scheduling.
Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang
2014-01-01
A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem.
A fast optimization algorithm for multicriteria intensity modulated proton therapy planning
Chen Wei; Craft, David; Madden, Thomas M.; Zhang, Kewu; Kooy, Hanne M.; Herman, Gabor T.
2010-09-15
Purpose: To describe a fast projection algorithm for optimizing intensity modulated proton therapy (IMPT) plans and to describe and demonstrate the use of this algorithm in multicriteria IMPT planning. Methods: The authors develop a projection-based solver for a class of convex optimization problems and apply it to IMPT treatment planning. The speed of the solver permits its use in multicriteria optimization, where several optimizations are performed which span the space of possible treatment plans. The authors describe a plan database generation procedure which is customized to the requirements of the solver. The optimality precision of the solver can be specified by the user. Results: The authors apply the algorithm to three clinical cases: A pancreas case, an esophagus case, and a tumor along the rib cage case. Detailed analysis of the pancreas case shows that the algorithm is orders of magnitude faster than industry-standard general purpose algorithms (MOSEK's interior point optimizer, primal simplex optimizer, and dual simplex optimizer). Additionally, the projection solver has almost no memory overhead. Conclusions: The speed and guaranteed accuracy of the algorithm make it suitable for use in multicriteria treatment planning, which requires the computation of several diverse treatment plans. Additionally, given the low memory overhead of the algorithm, the method can be extended to include multiple geometric instances and proton range possibilities, for robust optimization.
A new improved artificial bee colony algorithm for ship hull form optimization
NASA Astrophysics Data System (ADS)
Huang, Fuxin; Wang, Lijue; Yang, Chi
2016-04-01
The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.
Frost Formation: Optimizing solutions under a finite volume approach
NASA Astrophysics Data System (ADS)
Bartrons, E.; Perez-Segarra, C. D.; Oliet, C.
2016-09-01
A three-dimensional transient formulation of the frost formation process is developed by means of a finite volume approach. Emphasis is put on the frost surface boundary condition as well as the wide range of empirical correlations related to the thermophysical and transport properties of frost. A study of the numerical solution is made, establishing the parameters that ensure grid independence. Attention is given to the algorithm, the discretised equations and the code optimization through dynamic relaxation techniques. A critical analysis of four cases is carried out by comparing solutions of several empirical models against tested experiments. As a result, a discussion on the performance of such parameters is started and a proposal of the most suitable models is presented.
A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem.
Jiang, Zi-Bin; Yang, Qiong
2016-01-01
The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm. The continuous variant version of FOA has been proven to be a powerful evolutionary approach to determining the optima of a numerical function on a continuous definition domain. In this study, a discrete FOA (DFOA) is developed and applied to the traveling salesman problem (TSP), a common combinatorial problem. In the DFOA, the TSP tour is represented by an ordering of city indices, and the bio-inspired meta-heuristic search processes are executed with two elaborately designed main procedures: the smelling and tasting processes. In the smelling process, an effective crossover operator is used by the fruit fly group to search for the neighbors of the best-known swarm location. During the tasting process, an edge intersection elimination (EXE) operator is designed to improve the neighbors of the non-optimum food location in order to enhance the exploration performance of the DFOA. In addition, benchmark instances from the TSPLIB are classified in order to test the searching ability of the proposed algorithm. Furthermore, the effectiveness of the proposed DFOA is compared to that of other meta-heuristic algorithms. The results indicate that the proposed DFOA can be effectively used to solve TSPs, especially large-scale problems.
A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem
Jiang, Zi-bin; Yang, Qiong
2016-01-01
The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm. The continuous variant version of FOA has been proven to be a powerful evolutionary approach to determining the optima of a numerical function on a continuous definition domain. In this study, a discrete FOA (DFOA) is developed and applied to the traveling salesman problem (TSP), a common combinatorial problem. In the DFOA, the TSP tour is represented by an ordering of city indices, and the bio-inspired meta-heuristic search processes are executed with two elaborately designed main procedures: the smelling and tasting processes. In the smelling process, an effective crossover operator is used by the fruit fly group to search for the neighbors of the best-known swarm location. During the tasting process, an edge intersection elimination (EXE) operator is designed to improve the neighbors of the non-optimum food location in order to enhance the exploration performance of the DFOA. In addition, benchmark instances from the TSPLIB are classified in order to test the searching ability of the proposed algorithm. Furthermore, the effectiveness of the proposed DFOA is compared to that of other meta-heuristic algorithms. The results indicate that the proposed DFOA can be effectively used to solve TSPs, especially large-scale problems. PMID:27812175
Li, Miqing; Yang, Shengxiang; Zheng, Jinhua; Liu, Xiaohui
2014-01-01
The Euclidean minimum spanning tree (EMST), widely used in a variety of domains, is a minimum spanning tree of a set of points in space where the edge weight between each pair of points is their Euclidean distance. Since the generation of an EMST is entirely determined by the Euclidean distance between solutions (points), the properties of EMSTs have a close relation with the distribution and position information of solutions. This paper explores the properties of EMSTs and proposes an EMST-based evolutionary algorithm (ETEA) to solve multi-objective optimization problems (MOPs). Unlike most EMO algorithms that focus on the Pareto dominance relation, the proposed algorithm mainly considers distance-based measures to evaluate and compare individuals during the evolutionary search. Specifically, in ETEA, four strategies are introduced: (1) An EMST-based crowding distance (ETCD) is presented to estimate the density of individuals in the population; (2) A distance comparison approach incorporating ETCD is used to assign the fitness value for individuals; (3) A fitness adjustment technique is designed to avoid the partial overcrowding in environmental selection; (4) Three diversity indicators-the minimum edge, degree, and ETCD-with regard to EMSTs are applied to determine the survival of individuals in archive truncation. From a series of extensive experiments on 32 test instances with different characteristics, ETEA is found to be competitive against five state-of-the-art algorithms and its predecessor in providing a good balance among convergence, uniformity, and spread.
Calibration of neural networks using genetic algorithms, with application to optimal path planning
NASA Technical Reports Server (NTRS)
Smith, Terence R.; Pitney, Gilbert A.; Greenwood, Daniel
1987-01-01
Genetic algorithms (GA) are used to search the synaptic weight space of artificial neural systems (ANS) for weight vectors that optimize some network performance function. GAs do not suffer from some of the architectural constraints involved with other techniques and it is straightforward to incorporate terms into the performance function concerning the metastructure of the ANS. Hence GAs offer a remarkably general approach to calibrating ANS. GAs are applied to the problem of calibrating an ANS that finds optimal paths over a given surface. This problem involves training an ANS on a relatively small set of paths and then examining whether the calibrated ANS is able to find good paths between arbitrary start and end points on the surface.
Birkholz, Adam B.; Schlegel, H. Bernhard
2015-12-28
The development of algorithms to optimize reaction pathways between reactants and products is an active area of study. Existing algorithms typically describe the path as a discrete series of images (chain of states) which are moved downhill toward the path, using various reparameterization schemes, constraints, or fictitious forces to maintain a uniform description of the reaction path. The Variational Reaction Coordinate (VRC) method is a novel approach that finds the reaction path by minimizing the variational reaction energy (VRE) of Quapp and Bofill. The VRE is the line integral of the gradient norm along a path between reactants and products and minimization of VRE has been shown to yield the steepest descent reaction path. In the VRC method, we represent the reaction path by a linear expansion in a set of continuous basis functions and find the optimized path by minimizing the VRE with respect to the linear expansion coefficients. Improved convergence is obtained by applying constraints to the spacing of the basis functions and coupling the minimization of the VRE to the minimization of one or more points along the path that correspond to intermediates and transition states. The VRC method is demonstrated by optimizing the reaction path for the Müller-Brown surface and by finding a reaction path passing through 5 transition states and 4 intermediates for a 10 atom Lennard-Jones cluster.
Jevtić, Aleksandar; Gutiérrez, Alvaro
2011-01-01
Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the distributed bees algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA's control parameters by means of a genetic algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots' distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.
Jevtić, Aleksandar; Gutiérrez, Álvaro
2011-01-01
Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA’s control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots’ distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce. PMID:22346677
NASA Astrophysics Data System (ADS)
Hou, Rui; Yu, Junle
2011-12-01
Optical burst switching (OBS) has been regarded as the next generation optical switching technology. In this paper, the routing problem based on particle swarm optimization (PSO) algorithm in OBS has been studies and analyzed. Simulation results indicate that, the PSO based routing algorithm will optimal than the conversional shortest path first algorithm in space cost and calculation cost. Conclusions have certain theoretical significances for the improvement of OBS routing protocols.