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Improved Ant Colony Optimization Algorithm Based on Non-uniform Mutation Operator

GONG Yue, WU Hang, ZHAO Fei   

  1. (School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China)
  • Received:2012-08-13 Online:2013-10-15 Published:2013-10-14

基于非均匀变异算子的改进蚁群优化算法

龚 跃,吴 航,赵 飞   

  1. (长春理工大学计算机科学技术学院,长春 130022)
  • 作者简介:龚 跃(1960-),男,教授,主研方向:网络通信;吴 航、赵 飞,硕士研究生
  • 基金资助:
    国家“863”计划基金资助项目(2006AA701306);国家科技型中小企业技术创新基金资助项目(05C26212200378)

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

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