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: In this paper, a heuristic particle swarm ant colony optimization (HPSACO) is presented for optimum design of trusses, which is based on the particle swarm optimizer with passive congregation (PSOPC), ant colony optimizer and harmony search scheme.
452 citations
TL;DR: It has been detected that coupling emergent results in different areas, like those of PSO and complex dynamics, can improve the quality of results in some optimization problems.
Abstract: This paper proposes new particle swarm optimization (PSO) methods that use chaotic maps for parameter adaptation. This has been done by using of chaotic number generators each time a random number is needed by the classical PSO algorithm. Twelve chaos-embedded PSO methods have been proposed and eight chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of PSO and complex dynamics, can improve the quality of results in some optimization problems. It has been also shown that, some of the proposed methods have somewhat increased the solution quality, that is in some cases they improved the global searching capability by escaping the local solutions.
451 citations
04 May 1998
TL;DR: A multimodal problem generator was used to test three versions of a genetic algorithm and the binary particle swarm algorithm in a factorial time-series experiment.
Abstract: A multimodal problem generator was used to test three versions of a genetic algorithm and the binary particle swarm algorithm in a factorial time-series experiment. Specific strengths and weaknesses of the various algorithms were identified.
450 citations
06 Nov 2005
TL;DR: A general formulation of MO optimization is given in this chapter, the Pareto optimality concepts introduced, and solution approaches with examples of MO problems in the power systems field are given.
Abstract: The goal of this chapter is to give fundamental knowledge on solving multi-objective optimization problems. The focus is on the intelligent metaheuristic approaches (evolutionary algorithms or swarm-based techniques). The focus is on techniques for efficient generation of the Pareto frontier. A general formulation of MO optimization is given in this chapter, the Pareto optimality concepts introduced, and solution approaches with examples of MO problems in the power systems field are given
448 citations
TL;DR: In this article, a hybrid particle swarm optimization (HPSO) was proposed for a practical distribution state estimation, which considers nonlinear characteristics of the practical equipment and actual limited measurements in distribution systems.
Abstract: This paper proposes a hybrid particle swarm optimization (HPSO) for a practical distribution state estimation. The proposed method considers nonlinear characteristics of the practical equipment and actual limited measurements in distribution systems. The method can estimate load and distributed generation output values at each node by minimizing the difference between measured and calculated voltages and currents. The feasibility of the proposed method is demonstrated and compared with an original particle swarm optimization-based method on practical distribution system models. Effectiveness of the constriction factor approach of particle swarm optimization is also investigated. The results indicate the applicability of the proposed state estimation method to the practical distribution systems.
447 citations