Journal
APPLIED SOFT COMPUTING
Volume 61, Issue -, Pages 844-859Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2017.08.036
Keywords
Evolutionary multi-objective optimization; Decomposition method; Utopian point; Complicated Pareto fronts
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Funding
- National Natural Science Foundation of China [61303119, 61472302, 61272280]
- Fundamental Research Funds for the Central Universities [JB170304]
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Multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been considered as a promising method for solving multi-objective optimization problems (MOPs). It devotes most of its effort on convergence by optimizing a set of scalar optimization subproblems in a collaborative manner, while maintaining the diversity by using a set of uniformly distributed weight vectors. However, more recent studies illustrated that MOEA/D faces difficulties on MOPs with complicated Pareto fronts, mainly because the uniformity of weight vectors no longer lead to an evenly scattered approximation of the Pareto fronts in these cases. To remedy this, we suggest replacing the ideal point in the reciprocal Tchebycheff decomposition method with a more optimistic utopian point, with the aim of alleviating the sensitivity of MOEA/D to the Pareto front shape of MOPs. Experimental studies on benchmark and real-world problems have shown that such simple modification can significantly improve the performances of MOEA/D with reciprocal Tchebycheff decomposition on MOPs with complicated Pareto fronts. (C) 2017 Elsevier B.V. All rights reserved.
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