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dc.titleA competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design
dc.contributor.authorGoh, C.K.
dc.contributor.authorTan, K.C.
dc.contributor.authorLiu, D.S.
dc.contributor.authorChiam, S.C.
dc.identifier.citationGoh, C.K., Tan, K.C., Liu, D.S., Chiam, S.C. (2010-04-01). A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design. European Journal of Operational Research 202 (1) : 42-54. ScholarBank@NUS Repository.
dc.description.abstractMulti-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.
dc.subjectCompetitive-cooperative co-evolution
dc.subjectMulti-objective optimization
dc.subjectParticle swarm optimization
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.sourcetitleEuropean Journal of Operational Research
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