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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Veröffentlicht in:	Natural computing Jg. 1; H. 2-3; S. 235 - 306
Hauptverfasser:	Parsopoulos, K.E., Vrahatis, M.N.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Nature B.V 01.06.2002
Schlagworte:	Algorithms Changing environments Evolution Integer programming Mathematical analysis Optimization Particulate composites Studies
ISSN:	1567-7818, 1572-9796
Online-Zugang:	Volltext
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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 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.
AbstractList	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. 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 ^sup 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.[PUBLICATION ABSTRACT]
Author	Vrahatis, M.N. Parsopoulos, K.E.
Author_xml	– sequence: 1 givenname: K.E. surname: Parsopoulos fullname: Parsopoulos, K.E. – sequence: 2 givenname: M.N. surname: Vrahatis fullname: Vrahatis, M.N.
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Snippet	This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local...
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SubjectTerms	Algorithms Changing environments Evolution Integer programming Mathematical analysis Optimization Particulate composites Studies
Title	Recent approaches to global optimization problems through Particle Swarm Optimization
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