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Title: A cooperative coevolutionary algorithm for multiobjective optimization
Authors: Tan, K.C. 
Chew, Y.H.
Lee, T.H. 
Yang, Y.J.
Keywords: Co-evolution
Evolutionary algorithm
Multi-objective optimization
Issue Date: 2003
Citation: Tan, K.C.,Chew, Y.H.,Lee, T.H.,Yang, Y.J. (2003). A cooperative coevolutionary algorithm for multiobjective optimization. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics 1 : 390-395. ScholarBank@NUS Repository.
Abstract: This paper presents a kind of cooperative co-evolutionary algorithm (CCEA) for multi-objective optimization (MOO). In this algorithm, solutions evolve in the form of cooperative subpopulations. An archive stores non-dominated solutions and helps to evaluate individuals in the subpopulations. The mechanism of niching is applied to maintain the diversity of solutions in the archive. Meanwhile, an extending operator is designed to mine information on solution distribution from the archive and guide the search to regions that are not explored enough. Extensive simulations are performed on different benchmark problems for various multi-objective evolutionary algorithms (MOEAs) and indicate that CCEA is strongly competitive with five recent well-known MOEAs in finding a good non-dominated solution set.
Source Title: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
ISSN: 08843627
Appears in Collections:Staff Publications

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