{"title":"增强排列编码遗传算法的自组织迁移技术","authors":"K. Dinesh, Rajakumar R, R. Subramanian","doi":"10.1504/IJAMS.2021.10035828","DOIUrl":null,"url":null,"abstract":"Genetic algorithm (GA) is well-known optimisation algorithm for solving various kinds of the optimisation problems. GA is based on the evolutionary principles and effectively solves the large-scale problem. In addition, it incorporates the variety of hybrid techniques to achieve the best performance in complex problems. However, self-organisation is one of the popular model, which acquire global order from the local interaction among the individuals. The combined version of self-organisation and genetic algorithm are adopted to improve the performance in attaining the convergence. This paper proposes a bi-directional self-organisation migration technique for improving the genetic algorithm which achieves the convergence and well-balanced diversity in the population. The experimentation is conducted on the standard test-bed of travelling salesman problem and instances are obtained from TSPLIB. Thus, the proposed algorithm has shown its dominance with the existing classical GA in terms of various parameter metrics.","PeriodicalId":38716,"journal":{"name":"International Journal of Applied Management Science","volume":null,"pages":null},"PeriodicalIF":0.3000,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Self-organisation migration technique for enhancing the permutation coded genetic algorithm\",\"authors\":\"K. Dinesh, Rajakumar R, R. Subramanian\",\"doi\":\"10.1504/IJAMS.2021.10035828\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Genetic algorithm (GA) is well-known optimisation algorithm for solving various kinds of the optimisation problems. GA is based on the evolutionary principles and effectively solves the large-scale problem. In addition, it incorporates the variety of hybrid techniques to achieve the best performance in complex problems. However, self-organisation is one of the popular model, which acquire global order from the local interaction among the individuals. The combined version of self-organisation and genetic algorithm are adopted to improve the performance in attaining the convergence. This paper proposes a bi-directional self-organisation migration technique for improving the genetic algorithm which achieves the convergence and well-balanced diversity in the population. The experimentation is conducted on the standard test-bed of travelling salesman problem and instances are obtained from TSPLIB. Thus, the proposed algorithm has shown its dominance with the existing classical GA in terms of various parameter metrics.\",\"PeriodicalId\":38716,\"journal\":{\"name\":\"International Journal of Applied Management Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2021-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Applied Management Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/IJAMS.2021.10035828\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Applied Management Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJAMS.2021.10035828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MANAGEMENT","Score":null,"Total":0}
Self-organisation migration technique for enhancing the permutation coded genetic algorithm
Genetic algorithm (GA) is well-known optimisation algorithm for solving various kinds of the optimisation problems. GA is based on the evolutionary principles and effectively solves the large-scale problem. In addition, it incorporates the variety of hybrid techniques to achieve the best performance in complex problems. However, self-organisation is one of the popular model, which acquire global order from the local interaction among the individuals. The combined version of self-organisation and genetic algorithm are adopted to improve the performance in attaining the convergence. This paper proposes a bi-directional self-organisation migration technique for improving the genetic algorithm which achieves the convergence and well-balanced diversity in the population. The experimentation is conducted on the standard test-bed of travelling salesman problem and instances are obtained from TSPLIB. Thus, the proposed algorithm has shown its dominance with the existing classical GA in terms of various parameter metrics.