Hybrid Ant Colony Algorithm and Its Application on Function Optimization

Authors:
Bo Liu;Huiguang Li;Tihua Wu;Qingbin Zhang
Affiliations:
Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China 066004;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China 066004;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China 066004;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China 066004
Venue:
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Year:
2008

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Abstract

A new hybrid ant colony algorithm was proposed. Firstly, weight factor was introduced to the binary ant colony algorithm, and then we obtained a new probability by combining probability model of Population based incremental learning (PBIL) with transfer probability of ants pheromone . The new population are produced by probability model of PBIL, transfer probability of ants pheromone and the probability of proposed algorithm so that population polymorphism is ensured and the optimal convergence rate and the ability of breaking away from the local minima are improved. Optimization simulation results based on the benchmark test functions show that the hybrid algorithm has higher convergence rate and stability than binary ant colony algorithm (BACA) and Population based incremental learning (PBIL).