Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCB.2006.883270
Title: A multiobjective memetic algorithm based on particle swarm optimization
Authors: Liu, D.
Tan, K.C. 
Goh, C.K.
Ho, W.K. 
Keywords: Memetic algorithm (MA)
Multiobjective (MO) optimization
Particle swarm optimization (PSO)
Issue Date: Feb-2007
Citation: Liu, D., Tan, K.C., Goh, C.K., Ho, W.K. (2007-02). A multiobjective memetic algorithm based on particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 37 (1) : 42-50. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCB.2006.883270
Abstract: In this paper, a new memetic algorithm (MA) for multiobjective (MO) optimization is proposed, which combines the global search ability of particle swarm optimization with a synchronous local search heuristic for directed local fine-tuning. A new particle updating strategy is proposed based upon the concept of fuzzy global-best to deal with the problem of premature convergence and diversity maintenance within the swarm. The proposed features are examined to show their individual and combined effects in MO optimization. The comparative study shows the effectiveness of the proposed MA, which produces solution sets that are highly competitive in terms of convergence, diversity, and distribution. © 2007 IEEE.
Source Title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/54458
ISSN: 10834419
DOI: 10.1109/TSMCB.2006.883270
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