Localized genetic algorithm for vehicle routing problem with time windows

被引：97

作者：

Ursani, Ziauddin ^{[1
]}

Essam, Daryl ^{[1
]}

Cornforth, David ^{[2
]}

Stocker, Robert ^{[1
]}

机构：

[1] UNSW ADFA, Canberra, ACT 2600, Australia

[2] Commonwealth Sci & Ind Res Org Steel River Estate, Mayfield W, NSW 2304, Australia

来源：

APPLIED SOFT COMPUTING | 2011年 / 11卷 / 08期

关键词：

Vehicle Routing Problem with Time Windows (VRPTW); Localized Optimization Framework (LOF); Localized Genetic Algorithm (LGA); Benefit Maximization Genetic Algorithm (BMGA); Controlled De-Optimization Procedure (CDP); SEARCH;

D O I：

10.1016/j.asoc.2011.05.021

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper introduces the Localized Optimization Framework (LOF). This framework is an iterative procedure between two phases, Optimization and De-optimization. Optimization is done on the problem parts rather than the problem as a whole, while de-optimization is done on the whole problem. To test our hypothesis, we have chosen a genetic algorithm as an optimization methodology and Vehicle Routing Problem with Time Windows (VRPTW) as a domain space. We call this new scheme the Localized Genetic Algorithm (LGA). We demonstrate that the LGA is, on average, able to produce better solutions than most of the other heuristics on small scale problems of VRPTW. Furthermore the LGA has attained several new best solutions on popular datasets. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：5375 / 5390

页数：16

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