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

Zakaria, M.N. (2017) Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows. . .

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Official URL: https://www.sciencedirect.com/science/article/pii/...

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.

Item Type:Article
Subjects:T Technology > T Technology (General)
Academic Subject One:Academic Department - Information Communication Technology
Departments / MOR / COE:Departments > Computer Information Sciences
ID Code:12368
Deposited By: Ahmad Suhairi
Deposited On:24 Jan 2018 00:28
Last Modified:24 Jan 2018 00:28

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