An Improved Cuckoo Search Algorithm With Stud Crossover for Chinese TSP Problem

IF 0.6 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-10-01 DOI:10.4018/ijcini.20211001.oa17

Anbang Wang, Lihong Guo, Yuan Chen, Junjie Wang, Luo Liu, Yuanzhang Song

{"title":"An Improved Cuckoo Search Algorithm With Stud Crossover for Chinese TSP Problem","authors":"Anbang Wang, Lihong Guo, Yuan Chen, Junjie Wang, Luo Liu, Yuanzhang Song","doi":"10.4018/ijcini.20211001.oa17","DOIUrl":null,"url":null,"abstract":"The travelling salesman problem (TSP) is an NP-hard problem in combinatorial optimization. It has assumed significance in operations research and theoretical computer science. The problem was first formulated in 1930 and since then, has been one of the most extensively studied problems in optimization. In fact, it is used as a benchmark for many optimization methods. This paper represents a new method to addressing TSP using an improved version of cuckoo search (CS) with Stud (SCS) crossover operator. In SCS method, similar to genetic operators used in various metaheuristic algorithms, a Stud crossover operator that is originated from classical Stud genetic algorithm, is introduced into the CS with the aim of improving its effectiveness and reliability while dealing with TSP. Various test functions had been used to test this approach, and used subsequently to find the shortest path for Chinese TSP (CTSP). Experimental results presented clearly demonstrates SCS as a viable and attractive addition to the portfolio of swarm intelligence techniques.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"28 1","pages":"1-26"},"PeriodicalIF":0.6000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Cognitive Informatics and Natural Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijcini.20211001.oa17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

The travelling salesman problem (TSP) is an NP-hard problem in combinatorial optimization. It has assumed significance in operations research and theoretical computer science. The problem was first formulated in 1930 and since then, has been one of the most extensively studied problems in optimization. In fact, it is used as a benchmark for many optimization methods. This paper represents a new method to addressing TSP using an improved version of cuckoo search (CS) with Stud (SCS) crossover operator. In SCS method, similar to genetic operators used in various metaheuristic algorithms, a Stud crossover operator that is originated from classical Stud genetic algorithm, is introduced into the CS with the aim of improving its effectiveness and reliability while dealing with TSP. Various test functions had been used to test this approach, and used subsequently to find the shortest path for Chinese TSP (CTSP). Experimental results presented clearly demonstrates SCS as a viable and attractive addition to the portfolio of swarm intelligence techniques.

查看原文本刊更多论文

中文TSP问题的一种改进的带Stud交叉的布谷鸟搜索算法

旅行商问题(TSP)是组合优化中的np困难问题。它在运筹学和理论计算机科学中具有重要意义。这个问题最早是在1930年提出的，从那时起，它就成为最优化领域研究最广泛的问题之一。事实上，它被用作许多优化方法的基准。本文提出了一种利用改进的布谷鸟搜索(CS)和Stud (SCS)交叉算子来寻址TSP的新方法。在SCS方法中，与各种元启发式算法中使用的遗传算子类似，在CS中引入了源自经典Stud遗传算法的Stud交叉算子，以提高其在处理TSP时的有效性和可靠性。利用各种测试函数对该方法进行了测试，并随后用于寻找中文TSP的最短路径(CTSP)。实验结果清楚地表明，SCS是一种可行且有吸引力的群体智能技术组合的补充。

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

International Journal of Cognitive Informatics and Natural Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

2.00

自引率

11.10%

发文量

期刊介绍： The International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) encourages submissions that transcends disciplinary boundaries, and is devoted to rapid publication of high quality papers. The themes of IJCINI are natural intelligence, autonomic computing, and neuroinformatics. IJCINI is expected to provide the first forum and platform in the world for researchers, practitioners, and graduate students to investigate cognitive mechanisms and processes of human information processing, and to stimulate the transdisciplinary effort on cognitive informatics and natural intelligent research and engineering applications.