Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ejor.2008.07.025
Title: Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization
Authors: Tan, K.C. 
Chiam, S.C.
Mamun, A.A. 
Goh, C.K.
Keywords: Dynamic adaptation
Genetic algorithms
Multi-objective optimization
Variation operator
Issue Date: 1-Sep-2009
Source: Tan, K.C., Chiam, S.C., Mamun, A.A., Goh, C.K. (2009-09-01). Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization. European Journal of Operational Research 197 (2) : 701-713. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ejor.2008.07.025
Abstract: Although recent studies have shown that evolutionary algorithms are effective tools for solving multi-objective optimization problems, their performances are often bottlenecked by the suitability of the evolutionary operators with respect to the optimization problem at hand and their corresponding parametric settings. To adapt the search dynamic of evolutionary operation in multi-objective optimization, this paper proposes an adaptive variation operator that exploits the chromosomal structure of binary representation and synergizes the function of crossover and mutation. The overall search ability is deterministically tuned online to maintain a balance between extensive exploration and local fine-tuning at different stages of the evolutionary search. Also, the coordination between the two variation operators is achieved by means of an adaptive control that ensures an efficient exchange of information between the different chromosomal sub-structures throughout the evolutionary search. Extensive comparative studies with several representative variation operators are performed on different benchmark problems and significant algorithmic performance improvements in terms of proximity, uniformity and diversity are obtained with the incorporation of the proposed adaptive variation operator into the evolutionary multi-objective optimization process. © 2008 Elsevier B.V. All rights reserved.
Source Title: European Journal of Operational Research
URI: http://scholarbank.nus.edu.sg/handle/10635/55183
ISSN: 03772217
DOI: 10.1016/j.ejor.2008.07.025
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