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基于规模压缩的混合蚁群算法
引用本文:严建峰,李伟华,杜北.基于规模压缩的混合蚁群算法[J].控制与决策,2007,22(9):1061-1064.
作者姓名:严建峰  李伟华  杜北
作者单位:西北工业大学,计算机学院,西安,710072
基金项目:国家部委基金项目(9140A17050206HK03).
摘    要:为了提高蚁群算法处理大规模问题的性能,提出一种基于规模压缩的混合蚁群算法.根据TSP问题的最优解与次优解共享部分路径片断的原理,设计城市压缩算法,减少了TSP问题的城市处理量.在求解过程中,引入最优解的区域特征的概念,采用优化状态转移规则,压缩了解空间.仿真实验结果证明,采用所提出算法得到解的质量和收敛速度都有显著提高.

关 键 词:蚁群算法  规模压缩  路径片断  区域特征
文章编号:1001-0920(2007)09-1060-04
收稿时间:2006/6/9 0:00:00
修稿时间:2006-06-09

Hybrid ant colony algorithm based on scale compression
YAN Jian-feng,LI Wei-hua,DU Bei.Hybrid ant colony algorithm based on scale compression[J].Control and Decision,2007,22(9):1061-1064.
Authors:YAN Jian-feng  LI Wei-hua  DU Bei
Abstract:To improve the performance of ant colony algorithm in solving large-scale TSP problem, a hybrid ant colony algorithm based on scale compression is proposed. Genetic algorithm is used to generate a suboptimal solution set and calculate their intersection. By eliminating all cities mapped by the elements among the intersection in the primal TSP problem, the original problem is converted into a new one with smalier scale. In addition, an optimal state transition rule is designed based on regional characteristics of optimal solutions to accelerate convergence speed. Simulation results show the approach possesses high searching ability and excellent convergence performance.
Keywords:Ant colony algorithm  Scale compression  Segment  Regional character
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