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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Bibliographic Details
Published in:Engineering optimization Vol. 43; no. 1; pp. 1 - 17
Main Authors: Zhao, S. -Z., Suganthan, P. N.
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 01.01.2011
Taylor & Francis Ltd
Subjects:
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 Access:Get full text
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Summary: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.
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ISSN:0305-215X
1029-0273
DOI:10.1080/03052151003686716