Premium-Penalty Ant Colony Optimization

摘要：

The ant colony optimization is a new meta-heuristic,it is a population based algorithm and is a good method for combination optimizations.Due to the random probabilistic search strategy,the slow convergence is the main problem of the ACO.In order to improve the convergence of the algorithm,the Premium-Penalty Ant Colony Optimization(PPACO)is proposed.In this new algorithm,the good solutions found by the ants are awarded while the ordinary ones are punished.In order to counteract the polarization of pheromone values on all roads,the pheromone trails are limited to an intervalτmin,τmax and the evaporation rate is set to a higher value.The results of the simulation experiments are presented,in which the solutions gained by PPACO is compared with the best known results on several traveling salesman problems.It proves that the PPACO is ranked among the best ACO for tackling the TSP problems.

作者: Gao Wei Zhang Fei-jun

作者单位: Wuhan Polytechnic University,Hubei,Wuhan 430023,P.R.China

会议类型: 国际会议

会议名称: 第一届智能网络与智能系统国际会议(ICINIS 2008)(The First International Conference on Intelligent Networks and Intelligent Systems)

会议地点: 武汉

会议语种:英文

在线出版日期: 2008-11-01（万方平台首次上网日期，不代表论文的发表时间）