Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows
This paper presents a stochastic partially optimized cyclic shift crossover operator for the optimization of the multi-objective vehicle routing problem with time windows using genetic algorithms. The aim of the paper is to show how the combination of simple stochastic rules and sequential appendage...
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| Main Authors: | , |
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| Format: | Article |
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Elsevier Ltd
2017
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992126241&doi=10.1016%2fj.asoc.2016.09.039&partnerID=40&md5=6c6a5901c8044018dedbf0df784b2b2d http://eprints.utp.edu.my/19582/ |
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| Summary: | This paper presents a stochastic partially optimized cyclic shift crossover operator for the optimization of the multi-objective vehicle routing problem with time windows using genetic algorithms. The aim of the paper is to show how the combination of simple stochastic rules and sequential appendage policies addresses a common limitation of the traditional genetic algorithm when optimizing complex combinatorial problems. The limitation, in question, is the inability of the traditional genetic algorithm to perform local optimization. A series of tests based on the Solomon benchmark instances show the level of competitiveness of the newly introduced crossover operator. © 2016 Elsevier B.V. |
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