A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design

Multi-objective particle swarm optimization (MOPSO) is an optimization technique inspired by bird flocking, which has been steadily gaining attention from the research community because of its high convergence speed. On the other hand, in the face of increasing complexity and dimensionality of today...

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Vydáno v:European journal of operational research Ročník 202; číslo 1; s. 42 - 54
Hlavní autoři: Goh, C.K., Tan, K.C., Liu, D.S., Chiam, S.C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.04.2010
Elsevier
Elsevier Sequoia S.A
Edice:European Journal of Operational Research
Témata:
Applied sciences
Competition
Competitive–cooperative co-evolution
Cooperation
Decision theory. Utility theory
Efficiency
Exact sciences and technology
Multi-objective optimization
Multi-objective optimization Particle swarm optimization Competitive-cooperative co-evolution
Operational research and scientific management
Operational research. Management science
Optimization algorithms
Optimization techniques
Particle swarm optimization
Simulation
Studies
Particle swarm optimization
Multi-objective optimization
Competitive–cooperative co-evolution
Dimensionality
Evolutionary algorithm
Multiobjective programming
Evolution equation
Modeling
Convergence speed
Vertebrata
Competitive-cooperative co-evolution
High speed
Problem solving
Swarm intelligence
Metric
Aves
ISSN:0377-2217, 1872-6860
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Shrnutí:Multi-objective particle swarm optimization (MOPSO) is an optimization technique inspired by bird flocking, which has been steadily gaining attention from the research community because of its high convergence speed. On the other hand, in the face of increasing complexity and dimensionality of today’s application coupled with its tendency of premature convergence due to the high convergence speeds, there is a need to improve the efficiency and effectiveness of MOPSO. In this paper a competitive and cooperative co-evolutionary approach is adapted for multi-objective particle swarm optimization algorithm design, which appears to have considerable potential for solving complex optimization problems by explicitly modeling the co-evolution of competing and cooperating species. The competitive and cooperative co-evolution model helps to produce the reasonable problem decompositions by exploiting any correlation, interdependency between components of the problem. The proposed competitive and cooperative co-evolutionary multi-objective particle swarm optimization algorithm (CCPSO) is validated through comparisons with existing state-of-the-art multi-objective algorithms using established benchmarks and metrics. Simulation results demonstrated that CCPSO shows competitive, if not better, performance as compared to the other algorithms.
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2009.05.005