Recent approaches to global optimization problems through Particle Swarm Optimization

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, In...

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Vydané v:Natural computing Ročník 1; číslo 2-3; s. 235 - 306
Hlavní autori: Parsopoulos, K.E., Vrahatis, M.N.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Nature B.V 01.06.2002
Predmet:
Algorithms
Changing environments
Evolution
Integer programming
Mathematical analysis
Optimization
Particulate composites
Studies
ISSN:1567-7818, 1572-9796
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Shrnutí: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 super(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.
Bibliografia:SourceType-Scholarly Journals-1
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ISSN:1567-7818
1572-9796
DOI:10.1023/A:1016568309421