The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Javier Alcaraz - One of the best experts on this subject based on the ideXlab platform.
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two new robust genetic Algorithms for the flowshop scheduling problem
Omega-international Journal of Management Science, 2006Co-Authors: Ruben Ruiz, Concepcion Maroto, Javier AlcarazAbstract:The flowshop scheduling problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic Algorithms are not suitable for this hard, combinatorial problem unless hybridization is used. This work proposes new genetic Algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known Algorithms. The optimization criterion considered is the minimization of the total completion time or makespan (Cmax). We show a robust genetic algorithm and a fast hybrid implementation. These Algorithms use new genetic operators, advanced techniques like hybridization with local search and an efficient population initialization as well as a new generational scheme. A complete evaluation of the different parameters and operators of the Algorithms by means of a Design of Experiments approach is also given. The algorithm's effectiveness is compared against 11 other methods, including genetic Algorithms, tabu search, simulated annealing and other advanced and recent techniques. For the evaluations we use Taillard's well known standard benchmark. The results show that the proposed Algorithms are very effective and at the same time are easy to implement.
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solving the flowshop scheduling problem with sequence dependent setup times using advanced metaheuristics
European Journal of Operational Research, 2005Co-Authors: Ruben Ruiz, Concepcion Maroto, Javier AlcarazAbstract:Abstract This paper deals with the permutation flowshop scheduling problem in which there are sequence dependent setup times on each machine, commonly known as the SDST flowshop. The optimisation criteria considered is the minimisation of the makespan or Cmax. Genetic Algorithms have been successfully applied to regular flowshops before, and the objective of this paper is to assess their effectiveness in a more realistic and complex environment. We present two advanced genetic Algorithms as well as several adaptations of existing advanced metaheuristics that have shown superior performance when applied to regular flowshops. We show a calibration of the genetic algorithm's parameters and operators by means of a Design of Experiments (DOE) approach. For evaluating the proposed Algorithms, we have coded several, if not all, known SDST flowshop specific Algorithms. All methods are tested against an augmented benchmark based on the instances of Taillard. The results show a clear superiority of the Algorithms proposed, especially for the genetic Algorithms, regardless of instance type and size.
Ruben Ruiz - One of the best experts on this subject based on the ideXlab platform.
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two new robust genetic Algorithms for the flowshop scheduling problem
Omega-international Journal of Management Science, 2006Co-Authors: Ruben Ruiz, Concepcion Maroto, Javier AlcarazAbstract:The flowshop scheduling problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic Algorithms are not suitable for this hard, combinatorial problem unless hybridization is used. This work proposes new genetic Algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known Algorithms. The optimization criterion considered is the minimization of the total completion time or makespan (Cmax). We show a robust genetic algorithm and a fast hybrid implementation. These Algorithms use new genetic operators, advanced techniques like hybridization with local search and an efficient population initialization as well as a new generational scheme. A complete evaluation of the different parameters and operators of the Algorithms by means of a Design of Experiments approach is also given. The algorithm's effectiveness is compared against 11 other methods, including genetic Algorithms, tabu search, simulated annealing and other advanced and recent techniques. For the evaluations we use Taillard's well known standard benchmark. The results show that the proposed Algorithms are very effective and at the same time are easy to implement.
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solving the flowshop scheduling problem with sequence dependent setup times using advanced metaheuristics
European Journal of Operational Research, 2005Co-Authors: Ruben Ruiz, Concepcion Maroto, Javier AlcarazAbstract:Abstract This paper deals with the permutation flowshop scheduling problem in which there are sequence dependent setup times on each machine, commonly known as the SDST flowshop. The optimisation criteria considered is the minimisation of the makespan or Cmax. Genetic Algorithms have been successfully applied to regular flowshops before, and the objective of this paper is to assess their effectiveness in a more realistic and complex environment. We present two advanced genetic Algorithms as well as several adaptations of existing advanced metaheuristics that have shown superior performance when applied to regular flowshops. We show a calibration of the genetic algorithm's parameters and operators by means of a Design of Experiments (DOE) approach. For evaluating the proposed Algorithms, we have coded several, if not all, known SDST flowshop specific Algorithms. All methods are tested against an augmented benchmark based on the instances of Taillard. The results show a clear superiority of the Algorithms proposed, especially for the genetic Algorithms, regardless of instance type and size.
Concepcion Maroto - One of the best experts on this subject based on the ideXlab platform.
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two new robust genetic Algorithms for the flowshop scheduling problem
Omega-international Journal of Management Science, 2006Co-Authors: Ruben Ruiz, Concepcion Maroto, Javier AlcarazAbstract:The flowshop scheduling problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic Algorithms are not suitable for this hard, combinatorial problem unless hybridization is used. This work proposes new genetic Algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known Algorithms. The optimization criterion considered is the minimization of the total completion time or makespan (Cmax). We show a robust genetic algorithm and a fast hybrid implementation. These Algorithms use new genetic operators, advanced techniques like hybridization with local search and an efficient population initialization as well as a new generational scheme. A complete evaluation of the different parameters and operators of the Algorithms by means of a Design of Experiments approach is also given. The algorithm's effectiveness is compared against 11 other methods, including genetic Algorithms, tabu search, simulated annealing and other advanced and recent techniques. For the evaluations we use Taillard's well known standard benchmark. The results show that the proposed Algorithms are very effective and at the same time are easy to implement.
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solving the flowshop scheduling problem with sequence dependent setup times using advanced metaheuristics
European Journal of Operational Research, 2005Co-Authors: Ruben Ruiz, Concepcion Maroto, Javier AlcarazAbstract:Abstract This paper deals with the permutation flowshop scheduling problem in which there are sequence dependent setup times on each machine, commonly known as the SDST flowshop. The optimisation criteria considered is the minimisation of the makespan or Cmax. Genetic Algorithms have been successfully applied to regular flowshops before, and the objective of this paper is to assess their effectiveness in a more realistic and complex environment. We present two advanced genetic Algorithms as well as several adaptations of existing advanced metaheuristics that have shown superior performance when applied to regular flowshops. We show a calibration of the genetic algorithm's parameters and operators by means of a Design of Experiments (DOE) approach. For evaluating the proposed Algorithms, we have coded several, if not all, known SDST flowshop specific Algorithms. All methods are tested against an augmented benchmark based on the instances of Taillard. The results show a clear superiority of the Algorithms proposed, especially for the genetic Algorithms, regardless of instance type and size.
Xin Yao - One of the best experts on this subject based on the ideXlab platform.
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benchmarking optimization Algorithms an open source framework for the traveling salesman problem
IEEE Computational Intelligence Magazine, 2014Co-Authors: Thomas Weise, Raymond Chiong, Jorg Lassig, Ke Tang, Shigeyoshi Tsutsui, Wenxiang Chen, Zbigniew Michalewicz, Xin YaoAbstract:We introduce an experimentation procedure for evaluating and comparing optimization Algorithms based on the Traveling Salesman Problem (TSP). We argue that end-of-run results alone do not give sufficient information about an algorithm's performance, so our approach analyzes the algorithm's progress over time. Comparisons of performance curves in diagrams can be formalized by comparing the areas under them. Algorithms can be ranked according to a performance metric. Rankings based on different metrics can then be aggregated into a global ranking, which provides a quick overview of the quality of Algorithms in comparison. An open source software framework, the TSP Suite, applies this experimental procedure to the TSP. The framework can support researchers in implementing TSP solvers, unit testing them, and running experiments in a parallel and distributed fashion. It also has an evaluator component, which implements the proposed evaluation process and produces detailed reports. We test the approach by using the TSP Suite to benchmark several local search and evolutionary computation methods. This results in a large set of baseline data, which will be made available to the research community. Our experiments show that the tested pure global optimization Algorithms are outperformed by local search, but the best results come from hybrid Algorithms.
Yunfang Zhu - One of the best experts on this subject based on the ideXlab platform.
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seeker optimization algorithm for digital iir filter design
IEEE Transactions on Industrial Electronics, 2010Co-Authors: Chaohua Dai, Weirong Chen, Yunfang ZhuAbstract:Since the error surface of digital infinite-impulse-response (IIR) filters is generally nonlinear and multimodal, global optimization techniques are required in order to avoid local minima. In this paper, a seeker-optimization-algorithm (SOA)-based evolutionary method is proposed for digital IIR filter design. SOA is based on the concept of simulating the act of human searching in which the search direction is based on the empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. The algorithm's performance is studied with comparison of three versions of differential evolution Algorithms, four versions of particle swarm optimization Algorithms, and genetic algorithm. The simulation results show that SOA is superior or comparable to the other Algorithms for the employed examples and can be efficiently used for IIR filter design.
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Seeker Optimization Algorithm for Optimal Reactive Power Dispatch
IEEE Transactions on Power Systems, 2009Co-Authors: Chaohua Dai, Weirong Chen, Yunfang Zhu, Xuexia ZhangAbstract:Optimal reactive power dispatch problem in power systems has thrown a growing influence on secure and economical operation of power systems. However, this issue is well known as a nonlinear, multimodal and mixed-variable problem. In the last decades, computation intelligence-based techniques, such as genetic Algorithms (GAs), differential evolution (DE) Algorithms and particle swarm optimization (PSO) Algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA)-based reactive power dispatch method is proposed. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple Fuzzy rule. In this study, the algorithm's performance is evaluated on benchmark function optimization. Then, the SOA is applied to optimal reactive power dispatch on standard IEEE 57- and 118-bus power systems, and compared with conventional nonlinear programming method, two versions of GAs, three versions of DE Algorithms and four versions of PSO Algorithms. The simulation results show that the proposed approach is superior to the other listed Algorithms and can be efficiently used for optimal reactive power dispatch.
