Please use this identifier to cite or link to this item: https://doi.org/10.1109/CEC.2007.4424878
Title: A cooperative coevolutionary algorithm for multiobjective particle swarm optimization
Authors: Tan, C.H.
Goh, C.K.
Tan, K.C. 
Tay, A. 
Issue Date: 2007
Source: Tan, C.H., Goh, C.K., Tan, K.C., Tay, A. (2007). A cooperative coevolutionary algorithm for multiobjective particle swarm optimization. 2007 IEEE Congress on Evolutionary Computation, CEC 2007 : 3180-3186. ScholarBank@NUS Repository. https://doi.org/10.1109/CEC.2007.4424878
Abstract: Coevolutionary architectures have been shown to be effective ways to improve the performance of multiobjective (MO) optimization problems. This paper presents a cooperative coevolutionary algorithm for multiobjective particle swarm optimization (COMOPSO), which applies the divide-and-conquer approach to decompose decision vectors into smaller components and evolves multiple solutions in the form of cooperative subswarms. Representatives from each evolving subswarm are combined to form the solution to the whole system. The fitness of each individual is related to its ability to collaborate with individuals from other species, thereby encouraging the development of cooperative strategies. An adaptive niche sharing algorithm is introduced to handle the selection of the niche radius in a dynamic manner. Coupled with the adaptive niche sharing algorithm, COMOPSO demonstrates its effectiveness and efficiency in evolving highly competitive solution sets against various MO algorithms on benchmark problems characterized by different difficulties with consistent results. ©2007 IEEE.
Source Title: 2007 IEEE Congress on Evolutionary Computation, CEC 2007
URI: http://scholarbank.nus.edu.sg/handle/10635/68764
ISBN: 1424413400
DOI: 10.1109/CEC.2007.4424878
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