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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Bibliographic Details
Published in:Natural computing Vol. 1; no. 2-3; pp. 235 - 306
Main Authors: Parsopoulos, K.E., Vrahatis, M.N.
Format: Journal Article
Language:English
Published: Dordrecht Springer Nature B.V 01.06.2002
Subjects:
Algorithms
Changing environments
Evolution
Integer programming
Mathematical analysis
Optimization
Particulate composites
Studies
ISSN:1567-7818, 1572-9796
Online Access:Get full text
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Summary: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.
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ISSN:1567-7818
1572-9796
DOI:10.1023/A:1016568309421