摘要
蚁群算法在寻优过程中很容易出现早熟现象而陷入局部最优,同时蚁群算法在构造问题的可行解时,计算复杂度较大.为解决以上问题,将免疫算法和蚁群算法相结合,构成了一种结合免疫机制的蚁群优化算法,并将其用于解决WTA(武器目标分配)问题.通过仿真及与其它多种优化算法对比发现:基于免疫的蚁群优化算法在搜索效率上要高于其它优化算法.
Ant colony algorithm easily encounters premature problem and leads to local optimum in the optimization process.The computation is too complex when using ant colony algorithm to get the feasible solution of a prolem.Immune algorithm was coupled with ant colony algorithm to solve WTA（weapon target assignment） problem in this paper.Simulation results show that the immune-based ant colony optimization algorithm has higher searching efficiency than the other optimization algorithms.
出处
《山东理工大学学报（自然科学版）》
CAS
2009年第4期100-102,共3页
Journal of Shandong University of Technology:Natural Science Edition
