Topic
Multi-swarm optimization
About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.
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TL;DR: MEPSO has pretty good performance on almost all testing problems adopted in this paper, and outperforms other algorithms when the dynamic environment is unimodal and changes severely, or has a great number of local optima as dynamic Rastrigin function does.
238 citations
27 Aug 2005
TL;DR: A penalty function approach is employed and the algorithm is modified to preserve feasibility of the encountered solutions to investigate the performance of the recently proposed Unified Particle Swarm Optimization method on constrained engineering optimization problems.
Abstract: We investigate the performance of the recently proposed Unified Particle Swarm Optimization method on constrained engineering optimization problems. For this purpose, a penalty function approach is employed and the algorithm is modified to preserve feasibility of the encountered solutions. The algorithm is illustrated on four well–known engineering problems with promising results. Comparisons with the standard local and global variant of Particle Swarm Optimization are reported and discussed.
237 citations
10 Oct 2005
TL;DR: This paper focuses on discussing two adaptive parameter control methods for QPSO, a quantum-behaved particle swarm optimization algorithm that outperforms traditional PSOs in search ability as well as having less parameter to control.
Abstract: Particle swarm optimization (PSO) is a population-based evolutionary search technique, which has comparable performance with genetic algorithm. The existing PSOs, however, are not global-convergence-guaranteed algorithms. In the previous work, we proposed quantum-behaved particle swarm optimization (QPSO) algorithm that outperforms traditional PSOs in search ability as well as having less parameter to control. This paper focuses on discussing two adaptive parameter control methods for QPSO. After the ideology of QPSO is formulated, the experiment results of stochastic simulation are given to show how to select the parameter value to guarantee the convergence of the particle in QPSO. Finally, two adaptive parameter control methods are presented and experiment results on benchmark functions testify their efficiency.
235 citations
TL;DR: In this article, the authors proposed a new and effective solution methodology to solve various sizes of instances in a closed-loop supply chain network optimization process, where both design and planning decision variables (location and allocation) are considered in the proposed network and two popular meta-heuristic algorithms are considered to develop a new elevated hybrid algorithm: the genetic algorithm and particle swarm optimization (PSO).
234 citations
TL;DR: A new computational method is presented, the goal attainment method, which overcomes some of the limitations and disadvantages of methods currently available and presents an integrated, multiobjective treatment of performance and sensitivity optimization based on a vector index approach.
Abstract: This short paper is concerned with computational methods for solving optimization problems with a vector-valued index function (vector optimization). It uses vector optimization as a tool for analyzing static control problems with performance and parameter sensitivity indices. The first part of this short paper presents a new computational method, the goal attainment method, which overcomes some of the limitations and disadvantages of methods currently available. The second part presents an integrated, multiobjective treatment of performance and sensitivity optimization based on a vector index approach. A numerical example in electric power system control is included, with analysis and results demonstrating the use of the goal attainment method and application of the approach to performance and sensitivity optimization.
234 citations