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Recent approaches to global optimization problems through Particle Swarm Optimization

By K. E. Parsopoulos and M. N. Vrahatis

Abstract

This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and # 1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given

Topics: CWA – Conventional Weighted Aggregation, DE – Differential Evolution, DLS
Year: 2002
OAI identifier: oai:CiteSeerX.psu:10.1.1.19.4058
Provided by: CiteSeerX
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