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|Title:||A novel diversity maintenance scheme for evolutionary multi-objective optimization||Authors:||Gee, S.B.
multi-objective evolutionary algorithm
|Issue Date:||2013||Citation:||Gee, S.B.,Qiu, X.,Tan, K.C. (2013). A novel diversity maintenance scheme for evolutionary multi-objective optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8206 LNCS : 270-277. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-41278-3_33||Abstract:||Recently, decomposition-based multi-objective evolutionary algorithm (MOEA/D) has received increasing attentions due to its simplicity and decent optimization performance. In the presence of the deceptive optimum, the weight vector approach used in MOEA/D may not be able to prevent the population traps into local optimum. In this paper, we propose a new algorithm, namely Diversity Preservation Multi-objective Evolutionary Algorithm based on Decomposition (DivPre-MOEA/D), which uses novel diversity maintenance scheme to enhance the performance of MOEA/D. The proposed algorithm relaxes the dependency of the weight vector approach on approximated ideal vector to maintain diversity of the population. The proposed algorithm is evaluated on CEC-09 test suite and compared the optimization performance with MOEA/D. The experiment results show that DivPre-MOEA/D can provide better solutions spread along the Pareto front. © 2013 Springer-Verlag.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/83395||ISBN:||9783642412776||ISSN:||03029743||DOI:||10.1007/978-3-642-41278-3_33|
|Appears in Collections:||Staff Publications|
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