An improved hybrid ant colony algorithm and its application in solving TSP

被引：0
|
作者：
He Min [1 ]
Pan Dazhi [1 ]
Yang Song [1 ]
机构：
[1] China West Normal Univ, Coll Math & Informat, Nanchong 637009, Peoples R China
来源：
2014 IEEE 7TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC) | 2014年
关键词：
Ant colony; algorithm; Crossover Operation; Mutation; Metropolis criterion; TSP;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
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页码：423 / 427
页数：5
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