A multi-objective memetic algorithm based on locality-sensitive hashing for one-to-many-to-one dynamic pickup-and-delivery problem
A multi-objective memetic algorithm based on locality-sensitive hashing for one-to-many-to-one dynamic pickup-and-delivery problem
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DOI：
10.1016/j.ins.2015.09.006
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发表时间：
2016-02
期刊：
影响因子：
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通讯作者：
Zexuan Zhu;Jun Xiao;Shan He;Zhen Ji;Yiwen Sun
中科院分区：
文献类型：
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作者：
Zexuan Zhu;Jun Xiao;Shan He;Zhen Ji;Yiwen Sun
提出了一种多目标模因算法LSH-MOMA，该算法结合了多目标进化算法和基于局部搜索的局部搜索算法，解决了一对多对一动态收发问题(DPDP)。在进化框架中同时优化了三个目标，即路径长度、响应时间和工作量。在每一代LSH-MOMA中，采用基于LSH的纠偏和局部搜索来修复和改进个体解。LSH-MOMA在四个基准DPPS上进行了评估，实验结果表明，LSH-MOMA能够有效地获得三个目标的最优解。
This paper presents an early attempt to solve one-to-many-to-one dynamic pickup-and-delivery problem (DPDP) by proposing a multi-objective memetic algorithm called LSH-MOMA, which is a synergy of multi-objective evolutionary algorithm and locality-sensitive hashing (LSH) based local search. Three objectives namely route length, response time, and workload are optimized simultaneously in an evolutionary framework. In each generation of LSH-MOMA, LSH-based rectification and local search are imposed to repair and improve the individual solutions. LSH-MOMA is evaluated on four benchmark DPDPs and the experimental results show that LSH-MOMA is efficient in obtaining optimal tradeoff solutions of the three objectives.