{"title":"Analysis of the number of ants in ant colony system algorithm","authors":"M. M. Alobaedy, A. A. Khalaf, I. D. Muraina","doi":"10.1109/ICOICT.2017.8074653","DOIUrl":null,"url":null,"abstract":"This study presents an analysis of the number of ants in ant colony system algorithm. The study focuses on the effect of changing the number of ants in the algorithm behavior rather than find the optimum number. The factors investigated in this study are algorithm execution time, best solution, pheromones accumulative, pheromone dispersion, and the number of new solutions found by the ants. The experiment was conducted using travelling salesman problem to investigate those factors. The results show that the number of ants changes the algorithm behavior dramatically. Therefore, tuning the parameter number of ants in ant colony system could be easier by applying the min and max number of ants recommended in this study.","PeriodicalId":244500,"journal":{"name":"2017 5th International Conference on Information and Communication Technology (ICoIC7)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 5th International Conference on Information and Communication Technology (ICoIC7)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOICT.2017.8074653","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
This study presents an analysis of the number of ants in ant colony system algorithm. The study focuses on the effect of changing the number of ants in the algorithm behavior rather than find the optimum number. The factors investigated in this study are algorithm execution time, best solution, pheromones accumulative, pheromone dispersion, and the number of new solutions found by the ants. The experiment was conducted using travelling salesman problem to investigate those factors. The results show that the number of ants changes the algorithm behavior dramatically. Therefore, tuning the parameter number of ants in ant colony system could be easier by applying the min and max number of ants recommended in this study.
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