基于混合行为的自适应蚁群算法

针对传统蚁群算法求解能力的不足,提出了一种基于混合行为的自适应蚁群算法(HBACA).通过引入具有多行为的混合蚂蚁来扩大解搜索空间,避免早熟和停滞现象;另外在每次迭代过程中具有不同行为的蚂蚁数目可以视具体情况而动态地进行调整,以便在加速收敛和防止早熟、停滞现象之间取得一个较好的平衡.实验表明,相比ACS、MMAS算法,改进算法求解TSP问题的性能得到了加强.

A Self- Adaptive Ant Colony Algorithm Based on Hybrid Behavior

In order to overcome the drawbacks of the conventional ant colony algorithm such as insufficient solu-tion ability, a hybrid behavior based ant colony algorithm (HBACA) is proposed. The ant colony based on hybrid behavior was introduced to extend the area of feasible solutions and overcome the defect of precocity and stagnation. In addition, the number of different ants can be adaptively adjusted to keep good balance between accelerating con-vergence speed and avoiding precocity and stagnation. Experimental results of traveling salesman problem show that the proposed algorithm has a better global searching ability, higher efficiency than Ant Colony System (ACS) and MAX-MIN Ant System (MMAS).