Two-lbests based multi-objective particle swarm optimizer

The global best (gbest) or local best (lbest) of every particle in state-of-the-art multi-objective particle swarm optimization (MOPSO) implementations is selected from the non-dominated solutions in the external archive. This approach emphasizes the elitism at the expense of diversity when the size...

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Veröffentlicht in:	Engineering optimization Jg. 43; H. 1; S. 1 - 17
Hauptverfasser:	Zhao, S. -Z., Suganthan, P. N.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Abingdon Taylor & Francis 01.01.2011 Taylor & Francis Ltd
Schlagworte:	Algorithms Archives Atoms & subatomic particles Chaos theory Comparative analysis Evolutionary algorithms Expenses external archive Genetic algorithms multi-objective evolutionary algorithm multi-objective particle swarm optimization Optimization Optimization algorithms Searching State of the art Tests two lbests multi-objective particle swarm optimization (2LB-MOPSO)
ISSN:	0305-215X, 1029-0273
Online-Zugang:	Volltext
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