Abstract: This essay puts forward an improved ant colony algorithm based on the introduction of the variability of non-uniform mutation operator. It makes the path optimization, which uses optimized ant colony algorithm to complete the task of ant colony after the completion of one iteration and non-uniform mutation operator mutation. It can speed up the convergence of the algorithm. The satisfactory results are reached after N evolutions. Simulation results demonstrate that the optimal solution and stability of the improved algorithm are better than the basic ant algorithm and Ant Colony Optimization(ACO) algorithm.

Key words: Travelling Salesman Problem(TSP), Ant Colony Optimization(ACO) algorithm, mutation operator, Genetic Algorithm(GA), non-uniform mutation

摘要： 为解决对称旅行商问题，在改进蚁群优化算法的基础上，提出一种引入非均匀变异算子的改进算法。在路径寻优时采用改进的蚁群优化算法，且在完成一次循环迭代后，运用非均匀变异算子对已完成该次任务的蚁群进行变异处理，从而加快算法收敛速度，经过N次进化直至达到满意结果。仿真结果表明，在寻最优解的能力和算法稳定性方面，该算法比基本蚁群算法和蚁群优化算法更强。

关键词: 旅行商问题, 蚁群优化算法, 变异算子, 遗传算法, 非均匀变异

CLC Number: 

• TP301.6