Please use this identifier to cite or link to this item: https://doi.org/10.1109/TEVC.2005.860762
Title: A distributed cooperative coevolutionary algorithm for multiobjective optimization
Authors: Tan, K.C. 
Yang, Y.J.
Goh, C.K.
Keywords: Coevolution
Distributed computing
Evolutionary algorithms
Multiobjective optimization
Issue Date: Oct-2006
Source: Tan, K.C., Yang, Y.J., Goh, C.K. (2006-10). A distributed cooperative coevolutionary algorithm for multiobjective optimization. IEEE Transactions on Evolutionary Computation 10 (5) : 527-549. ScholarBank@NUS Repository. https://doi.org/10.1109/TEVC.2005.860762
Abstract: Recent advances in evolutionary algorithms show that coevolutionary architectures are effective ways to broaden the use of traditional evolutionary algorithms. This paper presents a cooperative coevolutionary algorithm (CCEA) for multiobjective optimization, which applies the divide-and-conquer approach to decompose decision vectors into smaller components and evolves multiple solutions in the form of cooperative subpopulations. Incorporated with various features like archiving, dynamic sharing, and extending operator, the CCEA is capable of maintaining archive diversity in the evolution and distributing the solutions uniformly along the Pareto front. Exploiting the inherent parallelism of cooperative coevolution, the CCEA can be formulated into a distributed cooperative coevolutionary algorithm (DCCEA) suitable for concurrent processing that allows inter-communication of subpopulations residing in networked computers, and hence expedites the computational speed by sharing the workload among multiple computers. Simulation results show that the CCEA is competitive in finding the tradeoff solutions, and the DCCEA can effectively reduce the simulation runtime without sacrificing the performance of CCEA as the number of peers is increased. © 2006 IEEE.
Source Title: IEEE Transactions on Evolutionary Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/54081
ISSN: 1089778X
DOI: 10.1109/TEVC.2005.860762
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