Particle swarm optimization with oscillation control
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ArticleDescription: 1 archivo (438,3 kB)Subject(s): Online resources: Summary: Particle Swarm Optimization (PSO) is a metaheuristic that has been successfully applied to linear and non-linear optimization problems in functions with discrete and continuous domains. This paper presents a new variation of this algorithm - called oscPSO - that improves the inherent search capacity of the original (canonical) version of the PSO algorithm. This version uses a deterministic local search method whose use depends on the movement patterns of the particles in each dimension of the problem. The method proposed was assessed by means of a set of complex test functions, and the performance of this version was compared with that of the original version of the PSO algorithm. In all cases, the oscPSO variation equaled or surpassed the performance of the canonical version of the algorithm.
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Particle Swarm Optimization (PSO) is a metaheuristic that has been successfully applied to linear and non-linear optimization problems in functions with discrete and continuous domains. This paper presents a new variation of this algorithm - called oscPSO - that improves the inherent search capacity of the original (canonical) version of the PSO algorithm. This version uses a deterministic local search method whose use depends on the movement patterns of the particles in each dimension of the problem. The method proposed was assessed by means of a set of complex test functions, and the performance of this version was compared with that of the original version of the PSO algorithm. In all cases, the oscPSO variation equaled or surpassed the performance of the canonical version of the algorithm.
Annual conference on genetic and evolutionary computation GECCO 09 (11º : 2009 : Montreal, Canadá) Proccedings ACM, Nueva York, 2009, pp.1751-1752.