Comparison of Recombination Operators in Panmictic and Cellular GAs to Solve a Vehicle Routing Problem

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence Pub Date : 2010-03-22 DOI:10.4114/IA.V14I46.1520

Carlos Bermúdez, P. Graglia, Natalia Stark, C. Salto, Hugo Alfonso

{"title":"Comparison of Recombination Operators in Panmictic and Cellular GAs to Solve a Vehicle Routing Problem","authors":"Carlos Bermúdez, P. Graglia, Natalia Stark, C. Salto, Hugo Alfonso","doi":"10.4114/IA.V14I46.1520","DOIUrl":null,"url":null,"abstract":"The Vehicle Routing Problem (VRP) deals with the assignment of a set of transportation orders to a fleet of vehicles and the sequencing of stops for each vehicle to minimize transportation costs. This paper presents two different genetic algorithm models (panmitic and cellular models) for providing solutions for the Capacitated VRP (CVRP), which is mainly characterized by using vehicles of the same capacity. We propose a new problem dependent recombination operator, called Best Route Better Adjustment recombination (BRBAX), which incorporates problem specific knowledge such as information about the routes constitution. A comparison of its performance is carried out with respect to classical recombination operators for permutations. A complete study of the influence of the recombination operators on the genetic search is presented. The results show that the use of our specialized BRBAX operator outperforms the others more generic operators for all problem instances under all metrics. The deviation between our best solution and the best-known one is very low, under 0.91%.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2010-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4114/IA.V14I46.1520","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 10

Abstract

The Vehicle Routing Problem (VRP) deals with the assignment of a set of transportation orders to a fleet of vehicles and the sequencing of stops for each vehicle to minimize transportation costs. This paper presents two different genetic algorithm models (panmitic and cellular models) for providing solutions for the Capacitated VRP (CVRP), which is mainly characterized by using vehicles of the same capacity. We propose a new problem dependent recombination operator, called Best Route Better Adjustment recombination (BRBAX), which incorporates problem specific knowledge such as information about the routes constitution. A comparison of its performance is carried out with respect to classical recombination operators for permutations. A complete study of the influence of the recombination operators on the genetic search is presented. The results show that the use of our specialized BRBAX operator outperforms the others more generic operators for all problem instances under all metrics. The deviation between our best solution and the best-known one is very low, under 0.91%.

查看原文本刊更多论文

泛群和蜂窝气体组合算子解决车辆路径问题的比较

车辆路线问题(Vehicle Routing Problem, VRP)处理的是将一组运输订单分配给车队，以及每辆车停靠的顺序，以使运输成本最小化。本文提出了两种不同的遗传算法模型(泛型模型和细胞模型)来求解有容VRP (Capacitated VRP, CVRP)， CVRP的主要特点是使用相同容量的车辆。我们提出了一种新的问题相关重组算子，称为最佳路线更好调整重组算子(BRBAX)，该算子结合了特定问题的知识，如路线构成信息。并将其与经典置换复合算子的性能进行了比较。对重组算子对遗传搜索的影响进行了全面的研究。结果表明，对于所有指标下的所有问题实例，使用我们专门的BRBAX操作符的性能优于其他更通用的操作符。我们的最佳解与最知名解之间的偏差非常低，小于0.91%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

8 weeks

期刊介绍： Inteligencia Artificial is a quarterly journal promoted and sponsored by the Spanish Association for Artificial Intelligence. The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. Particularly, the Journal welcomes: New approaches, techniques or methods to solve AI problems, which should include demonstrations of effectiveness oor improvement over existing methods. These demonstrations must be reproducible. Integration of different technologies or approaches to solve wide problems or belonging different areas. AI applications, which should describe in detail the problem or the scenario and the proposed solution, emphasizing its novelty and present a evaluation of the AI techniques that are applied. In addition to rapid publication and dissemination of unsolicited contributions, the journal is also committed to producing monographs, surveys or special issues on topics, methods or techniques of special relevance to the AI community. Inteligencia Artificial welcomes submissions written in English, Spaninsh or Portuguese. But at least, a title, summary and keywords in english should be included in each contribution.