基于启发式变异的蚁群算法

Ant Colony Algorithm Based on Heuristic Mutation

提出一种基于启发式变异的蚁群算法,结合传统蚁群算法和遗传变异算法的优点,利用蚁群算法找到一条全局近优解,采用启发式变异进行路径优化,并将优化信息以信息素的方式传递给下一代,从而快速得到全局最优解。以旅行商问题为例进行仿真实验,结果表明该算法比其他同类算法具有更好的性能。

This paper proposes an Ant Colony Algorithm based on Heuristic Mutation（ACAHM）,combining with the advantage of the traditional Ant Colony Algorithm（ACA）and the genetic mutation algorithm.ACA is used to find a globally near optimal solution,and then it is optimized by the heuristic mutation.The optimized route information is passed to the next generation with the pheromone to quickly get a globally optimal solution.Simulation results for solving the Traveling Salesman Problem（TSP）show that ACAHM is superior to other congeneric algorithms in terms of solution quality and computation speed.