The Improved Ant Colony Algorithm Based on Immunity System Genetic Algorithm and Application
In this paper, aims at the weakness of ant colony algorithm that leads to converge rashly to the non-overall superior solution and its calculating time is long, when deals with resolving large optimization problem, a improved ant colony algorithm is presented. The algorithm combines the overall hunting ability with expansibility of the genetic algorithm and the character of immunity system in guiding partial hunting for particular problem. It is applied to the process of searching for the optimization in TSP, compares with the result of GA and ACA, the result of the new algorithm closes to superior solution much more, the validity of the algorithm is verified.
Ant Colony Algorithm (ACA) Genetic Algorithm (GA) Immunity System (IS) TSP.
Caiqing Zhang Yanchao Lu
Dept. of Economic Management, North China Electric Power University Baoding, 071003, Hebei, China
国际会议
Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)
北京
英文
726-731
2006-07-17（万方平台首次上网日期，不代表论文的发表时间）