基于混合行为的蚁群算法

针对基本蚁群算法易出现停滞、收敛速度慢的问题,在最大最小蚁群算法的基础上提出了一种基于混合行为的蚁群(HBAC)算法,通过引入停止蚂蚁来构造局部路线方式和增加全局调优策略,提高了算法的搜索能力和收敛速度,同时将蚂蚁所寻找的各条路径的信息素限定在一个可动态调整的范围之内,避免了算法过早陷于局部最优解.通过HBAC算法同其他蚁群算法在求解旅行商问题上的实验比较,发现该算法拥有较快的收敛速度,提高了全局最优解搜索能力,在性能上有了较大的提高.

Ant colony algorithm based on hybrid behavior

Aiming at the problem of stagnation and slow convergence about the basic ant colony algorithm,an improved ant colony algorithm Hybrid Behavior Ant Colony HBAC algorithm based on hybrid behavior is proposed,which is developed from the Max-Min ant colony algorithm.By applying stopping ant to construct partial solutions and adding global optimization strategies,both the search capability and convergence rate of the algorithm is increased,meantime,the pheromone of the path is limited in a dynamic range which avoids algorithm falling in local optimal easily.The HBAC is compared with the other algorithms on traveling salesman problems,experimental results show that the HBAC algorithm has a better speed of convergence,enhances the ability of searching the whole best solution and promises a better performance.