Solving Traveling Salesman Problem by Ant Colony Optimization -Particle Swarm Optimization Algorithm
Using the properties of ant colony algorithm and panicle swarm optimization, a hybrid algorithm is proposed to solve the traveling salesman problems. First, it adopts statistics method to get several initial better solutions and in accordance with them, gives information pheromone to distribute. Second, it makes use of the ant colony algorithm to get several solutions through information pheromone accumulation and renewal. Finally, using across and mutation operation of particle swarm optimization, the effective solutions are obtained. Compare with the simulated annealing algorithm, the standard genetic algorithm and the standard ant colony algorithm, all the 16 hybrid algorithms are proved effective. Especially the hybrid algorithm with across strategy B and mutation strategy B is a simple and effective better algorithm than others.
Ant Colony Algorithm Particle Swarm Optimization Traveling Salesman Problem Simulated Annealing Algorithm Genetic Algorithm
Shang Gao Ling-fang Sun Xin-zi JIANG Ke-zong TANG
School of electronics and information, Jiangsu University of Science and Technology, Zhenjiang 21200 School of Economic and management, Jiangsu University of Science and Technology, Zhenjiang 212003,Ch School of electronics and information, Jiangsu University of Science and Technology, Zhenjiang 21200
国际会议
杭州
英文
426-429
2006-10-12（万方平台首次上网日期，不代表论文的发表时间）