一种带有摄动的导向性蚁群算法  

Perturbation Guided Ant Colony Optimization

作　　者：段汐[1] 杨群[1] 陈兵[1] 李媛祯 

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京210016

出　　处：《计算机科学》2014年第12期151-154,共4页Computer Science

摘　　要：针对加入导向性局部搜索(Guided Local Search,GLS)的蚁群算法(Ant Colony Optimization,ACO)容易过早收敛的问题,提出一种带有摄动的导向性蚁群算法(Perturbation Guided Ant Colony Optimization,PGACO),该算法在当前解表现出过早收敛的趋势时,采用摄动(Perturbation)方式干扰解构建过程,使当前解移动到其邻域空间,从而产生一个新的可行解来避免算法过早收敛,提高算法求解的精度。实验结果表明,PGACO能有效地改善过早收敛问题,获得更优的可行解和执行速度,同时具有更强的全局搜索能力,能进一步提高算法的性能。The hybridizations of Ant Colony Optimization（ACO） with Guided Local Search（GLS） can be used to solve the problem that ACO is easily trapped in local optima.However,there is a problem that the algorithmic optima prematurely converges to suboptimal solutions.This paper presented a Perturbation Guided Ant Colony Optimization（PGACO） algorithm to avoid the problem.A proposed perturbation method is used to move current solution to a neighbor solution space to build new global optimal solutions when the algorithm is prone to premature convergence.The experimental results show that PGACO can effectively avoid a premature convergence of the algorithm to suboptimal solutions.PGACO can generate a better solution,simultaneously has a better global search capability.

关 键 词：蚁群算法 导向性局部搜索 摄动 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]