期刊
COMPUTERS & OPERATIONS RESEARCH
卷 62, 期 -, 页码 61-77出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cor.2015.04.009
关键词
Multi-objective optimization; Memetic algorithm; Decomposition; Vehicle routing problem with time windows
类别
资金
- National Natural Science Foundation of China [61303119, 61373043]
- Fundamental Research Funds for the Central Universities [JB140304, JB140317]
Multi-objective evolutionary algorithm based on decomposition (MOEA/D) provides an excellent algorithmic framework for solving multi-objective optimization problems. It decomposes a target problem into a set of scalar sub-problems and optimizes them simultaneously. Due to its simplicity and outstanding performance, MOEA/D has been widely studied and applied. However, for solving the multi-objective vehicle routing problem with time windows (MO-VRPTW), MOEA/D faces a difficulty that many sub-problems have duplicated best solutions. It is well-known that MO-VRPTW is a challenging problem and has very few Pareto optimal solutions. To address this problem, a novel selection operator is designed in this work to enhance the original MOEA/D for dealing with MO-VRPTW. Moreover, three local search methods are introduced into the enhanced algorithm. Experimental results indicate that the proposed algorithm can obtain highly competitive results on Solomon's benchmark problems. Especially for instances with long time windows, the proposed algorithm can obtain more diverse set of non-dominated solutions than the other algorithms. The effectiveness of the proposed selection operator is also demonstrated by further analysis. (C) 2015 Elsevier Ltd. All rights reserved.
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