关键词: 微粒群优化, 蚁群优化, 自适应选取, 优质组合, 货郎问题

Abstract:

The parameter values of the ant colony optimization (ACO) algorithm was optimized based on particle swarm optimization(PSO) thought . Through the high-quality combination of the particles search and adaptive selection of parameter values, the ACO algorithm parameter values could be selected without relying on human experience or trial and error of artificial selection. The parameter combination obtained from the algorithm could significantly improve the performance of the ACO algorithm and give parameter values in continuity, randomness and accuracy. By using the highquality combination of parameter values feedback to the ACO algorithm, this algorithm can work well in solving traveling salesman problem (TSP)  with excellent results.

Key words: particle swarm optimization, ant colony optimization, adaptive selection, high-quality combination, traveling salesman problem