文件名称:Particle-Swarm-Optimization
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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.
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.
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art%3A10.1023%2FA%3A1016568309421.pdf