GSP-ANT: An Efficient Ant Colony Optimization Algorithm with Multiple Good Solutions for Pheromone Undate
Ant colony optimization (ACO) is a metaheuristic for various optimization problems, especially the hard combinatorial optimization problems. However, existing ACO algorithms suffer from search stagnation and exorbitantly long computation time. To alleviate these shortcomings, an improved ACO algorithm, called GSP-ANT, is presented in this paper. It maintains a good solution pool (GSP) and alternately uses the optimal solution and suboptimal solutions in the pool to update pheromone. This enables ants to transfer among different solution regions and accordingly explore larger solution space. On the other hand, once a solution in the GSP is selected, it is continuously used for pheromone update in a certain number of iterations with the aim of exploiting the neighborhood of this solution intensively. By this means, both the intensification and diversification of the search are considered. The performance of GSPANT is examined experimentally on typical traveling salesman problems. Computational results indicate that GSP-ANT is a promising approach.
Ant colony optimization search stagnation good solution pool pheromone update
Zhigang Ren Zuren Feng Zhaojun Zhang
Systems Engineering Institute,State Key Laboratory for Manufacturing Systems Engineering Xian Jiaotong University Xian,China
国际会议
上海
英文
589-592
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）