一种基于代价算子的蚁群算法及其应用

An Ant Colony Algorithm Based on Induced Cost Operator and Its Application in TSP

采用改进的蚁群算法求解较为复杂环境下的旅行商问题.针对出现蚁群算法运算时间过长、求解的结果精度低等不足问题,给出一种以动态搜索诱导代价算子为主的蚁群算法,制定动态搜索模型公式,加大进化初期的阈值,利用衰减模型的动态性能,最终加快收敛速度.TSP实例表明,改进的算法加快了收敛速度,提高了优化解.

An improved ant colony algorithm is used to solve the traveling salesman problem in complex environments.Aiming at the problems of long operation time and low precision of the results,an ant colony algorithm based on the dynamic search induced cost operator(DSCO-ACA)is proposed to formulate a dynamic search model,increase the threshold at the initial stage of evolution,and finally accelerate the convergence speed by utilizing the dynamic performance of the attenuation model.An TSP example shows that the improved algorithm speeds up the convergence speed and improves the optimal solution.