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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| Published in | European journal of operational research Vol. 202; no. 1; pp. 42 - 54 |
|---|---|
| Main Authors | , , , |
| 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 | |
| Online Access | Get full text |
| ISSN | 0377-2217 1872-6860 1872-6860 |
| DOI | 10.1016/j.ejor.2009.05.005 |
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| Abstract | 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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| AbstractList | 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. 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. [PUBLICATION ABSTRACT] |
| Author | Chiam, S.C. Liu, D.S. Tan, K.C. Goh, C.K. |
| Author_xml | – sequence: 1 givenname: C.K. surname: Goh fullname: Goh, C.K. organization: Advanced Technology Centre, Rolls-Royce Singapore Pte Ltd, 50 Nanyang Avenue, Blk N2 B3C-05, Singapore 639798, Singapore – sequence: 2 givenname: K.C. surname: Tan fullname: Tan, K.C. email: eletankc@nus.edu.sg organization: Department of Electrical and Computer Engineering, National University of Singapore, 4, Engineering Drive 3, Singapore 117576, Singapore – sequence: 3 givenname: D.S. surname: Liu fullname: Liu, D.S. organization: Department of Electrical and Computer Engineering, National University of Singapore, 4, Engineering Drive 3, Singapore 117576, Singapore – sequence: 4 givenname: S.C. surname: Chiam fullname: Chiam, S.C. organization: Department of Electrical and Computer Engineering, National University of Singapore, 4, Engineering Drive 3, Singapore 117576, Singapore |
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| Keywords | 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 |
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| SubjectTerms | 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 |
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| Title | A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design |
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