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.
Full text not available from this repository.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 |
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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 |