{"title":"An improved hybrid ant colony algorithm and its application in solving TSP","authors":"He Min, Pang Dazhi, Yang Song","doi":"10.1109/ITAIC.2014.7065084","DOIUrl":null,"url":null,"abstract":"Ant colony algorithm is a simulated evolutionary algorithm with the characteristics of positive feedback and distributed computation. It simulate the process of ants foraging to search the optimal solution. But the algorithm fall into local optimum easily and the convergence speed is very slow. After analyzing the disadvantages of ant colony algorithm, we put forward an improved hybrid ant colony algorithm. For each generation of ant colony perform crossover and mutation operations, and accept new individuals with a specified probability according to the Metropolis criterion of simulation annealing algorithm. Through series of simulation experiments' results, it can be found that the proposed algorithm is good at stability and optimization capacity.","PeriodicalId":111584,"journal":{"name":"2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITAIC.2014.7065084","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Ant colony algorithm is a simulated evolutionary algorithm with the characteristics of positive feedback and distributed computation. It simulate the process of ants foraging to search the optimal solution. But the algorithm fall into local optimum easily and the convergence speed is very slow. After analyzing the disadvantages of ant colony algorithm, we put forward an improved hybrid ant colony algorithm. For each generation of ant colony perform crossover and mutation operations, and accept new individuals with a specified probability according to the Metropolis criterion of simulation annealing algorithm. Through series of simulation experiments' results, it can be found that the proposed algorithm is good at stability and optimization capacity.