Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows

Pierre, D.M. and Zakaria, N. (2017) Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows. Applied Soft Computing Journal, 52. pp. 863-876.

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Abstract

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.

Item Type: Article
Impact Factor: cited By 1
Departments / MOR / COE: Division > Academic > Faculty of Science & Information Technology > Computer Information Sciences
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 20 Apr 2018 07:11
Last Modified: 20 Apr 2018 07:11
URI: http://scholars.utp.edu.my/id/eprint/19582

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