徐江乐, 肖志涛, 赵京华. 基于遗传算法的改进智能优化蚁群算法[J]. 微电子学与计算机, 2011, 28(8): 47-50.
引用本文: 徐江乐, 肖志涛, 赵京华. 基于遗传算法的改进智能优化蚁群算法[J]. 微电子学与计算机, 2011, 28(8): 47-50.
XU Jiang-le, XIAO Zhi-tao, ZHAO Jing-hua. An Improved Intelligent Ant Colony Optimization Based on Genetic Algorithm[J]. Microelectronics & Computer, 2011, 28(8): 47-50.
Citation: XU Jiang-le, XIAO Zhi-tao, ZHAO Jing-hua. An Improved Intelligent Ant Colony Optimization Based on Genetic Algorithm[J]. Microelectronics & Computer, 2011, 28(8): 47-50.

基于遗传算法的改进智能优化蚁群算法

An Improved Intelligent Ant Colony Optimization Based on Genetic Algorithm

  • 摘要: 针对蚁群算法加速收敛和早熟停滞现象的矛盾,根据遗传算法的交叉算子、变异算子和粒子群算法的粒子极值,采用一种优化蚁群算法,以在加速收敛和防止早熟停滞现象之间取得更好的平衡.在利用该算法解决TSP问题中,当前解与个体极值和全局极值分别进行交叉操作,产生的解为新的位置信息.通过对50个城市问题进行实验,结果表明,该方法比一般蚁群算法具有更好的收敛速度和稳定性,适合于求解大规模的问题.

     

    Abstract: For the conflict of ant colony algorithm for accelerating convergence and premature stagnation phenomenon,according to genetic algorithm crossover operator,mutation operator and particle extreme value of particle swarm algorithm,we studied and improved the ant colony algorithm,to achieve good balance between accelerating convergence and preventing premature stagnation phenomenon.Based on this algorithm,for TSP problem,the current solution is made crossover operation with individual extreme and global extreme respectively.The solution is the new position information produced.The experiment results of 50 urban problems show that it is better than general ant colony algorithm in convergence speed and stability,and suitable for solving large-scale problems.

     

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