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

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Original languageEnglish
Pages (from-to)42-54
Journal / PublicationEuropean Journal of Operational Research
Issue number1
Publication statusPublished - 1 Apr 2010
Externally publishedYes


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. © 2009 Elsevier B.V. All rights reserved.

Research Area(s)

  • Competitive-cooperative co-evolution, Multi-objective optimization, Particle swarm optimization

Citation Format(s)