Random Pairwise Competition Based Ant Selection for Pheromone Updating in Ant Colony Optimization
Hao Cao, Qiang Yang, Xudong Gao, Peilan Xu, Zhenyu Lu, Jun Zhang
Abstract
Ant Colony Optimization (ACO) has shown very promising performance in solving Traveling Salesman Problem (TSP). However, most existing ACO algorithms utilize either the absolutely best ants or all ants to update the pheromone matrix. This leads to either serious diversity loss or slow convergence. To alleviate these predicaments, this paper designs a random pairwise competition based ant selection for pheromone updating. Specifically, a number of ants are randomly selected from the ant colony and then are randomly paired together. Subsequently the better one in each pair is selected to update the pheromone matrix. In this way, a good balance between search diversity and search convergence is potentially maintained. Integrating this selection strategy along with a local search scheme into the ACO framework, a new ACO algorithm called random pairwise competition based ACO (RPCACO) is developed. Experiments conducted on 8 TSP instances from the TSPLIB benchmark set demonstrate that RPCACO is more effective and efficient than the five classical ACO algorithms in solving TSP.