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
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Abstract	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 of the current set of non-dominated solutions in the external archive is small. This article proposes that the gbests or lbests should be chosen from the top fronts in a non-domination sorted external archive of reasonably large size. In addition, a novel two local bests (lbest) based MOPSO (2LB-MOPSO) version is proposed to focus the search around small regions in the parameter space in the vicinity of the best existing fronts unlike the current MOPSO variants in which the pbest and gbest (or lbest) of a particle can be located far apart in the parameter space thereby potentially resulting in a chaotic search behaviour. Comparative evaluation using 19 multi-objectives test problems and 11 state-of-the-art multi-objective evolutionary algorithms ranks overall the 2LB-MOPSO as the best while two state-of-the-art MOPSO algorithms are ranked the worst with respect to other multi-objective evolutionary algorithms.
AbstractList	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 of the current set of non-dominated solutions in the external archive is small. This article proposes that the gbests or lbests should be chosen from the top fronts in a non-domination sorted external archive of reasonably large size. In addition, a novel two local bests (lbest) based MOPSO (2LB-MOPSO) version is proposed to focus the search around small regions in the parameter space in the vicinity of the best existing fronts unlike the current MOPSO variants in which the pbest and gbest (or lbest) of a particle can be located far apart in the parameter space thereby potentially resulting in a chaotic search behaviour. Comparative evaluation using 19 multi-objectives test problems and 11 state-of-the-art multi-objective evolutionary algorithms ranks overall the 2LB-MOPSO as the best while two state-of-the-art MOPSO algorithms are ranked the worst with respect to other multi-objective evolutionary algorithms. [PUBLICATION ABSTRACT] 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 of the current set of non-dominated solutions in the external archive is small. This article proposes that the gbests or lbests should be chosen from the top fronts in a non-domination sorted external archive of reasonably large size. In addition, a novel two local bests (lbest) based MOPSO (2LB-MOPSO) version is proposed to focus the search around small regions in the parameter space in the vicinity of the best existing fronts unlike the current MOPSO variants in which the pbest and gbest (or lbest) of a particle can be located far apart in the parameter space thereby potentially resulting in a chaotic search behaviour. Comparative evaluation using 19 multi-objectives test problems and 11 state-of-the-art multi-objective evolutionary algorithms ranks overall the 2LB-MOPSO as the best while two state-of-the-art MOPSO algorithms are ranked the worst with respect to other multi-objective evolutionary algorithms.
Author	Zhao, S. -Z. Suganthan, P. N.
Author_xml	– sequence: 1 givenname: S. -Z. surname: Zhao fullname: Zhao, S. -Z. organization: School of Electrical and Electronic Engineering , Nanyang Technological University – sequence: 2 givenname: P. N. surname: Suganthan fullname: Suganthan, P. N. email: epnsugan@ntu.edu.sg organization: School of Electrical and Electronic Engineering , Nanyang Technological University
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Snippet	The global best (gbest) or local best (lbest) of every particle in state-of-the-art multi-objective particle swarm optimization (MOPSO) implementations is... The global best (gbest) or local best (lbest) of every particle in state-of-the- art multi-objective particle swarm optimization (MOPSO) implementations is...
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SubjectTerms	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)
Title	Two-lbests based multi-objective particle swarm optimizer
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