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

• Published in: European journal of operational research Vol. 202; no. 1; pp. 42 - 54
• Main Authors: Goh, C.K., Tan, K.C., Liu, D.S., Chiam, S.C.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.04.2010; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: 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; 1872-6860
• DOI: 10.1016/j.ejor.2009.05.005

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.