基于迭代局部搜索的路径规划蚁群算法

Path Planning Ant Colony Algorithm Based on Iterated Local Search

针对蚁群算法易早熟及局部搜索能力欠佳的缺陷,将迭代局部搜索策略引入蚁群算法。新算法的基本思想是:从初始解出发,用蚁群算法进行局部搜索,如陷入局部最优,则产生一个摄动解作为新的初始解再进行局部搜索,根据接受规则决定进入下一步迭代的局部最优解。将改进算法应用于二维路径规划,数值实验表明,改进算法相比基本蚁群算法有更佳的局部收敛性,可获得比基本蚁群算法结果更优路径。

Ant colony algorithm is easy to premature and the ability of local search is poor.The iterative local search strategy is introduced into ant colony algorithm.The basic idea of the new algorithm is that the new algorithm starts from the initial solution and use ant colony algorithm for local search.A perturbation solution is generated as a new initial solution,and then the local search is carried out if it falls into the local optimum,and the local optimal solution of the next iteration is determined according to the acceptance rule.The improved algorithm is applied to two-dimensional path planning,and the numerical experiments show that the improved algorithm has better local convergence than the basic ant colony algorithm,and it can obtain better path than that of the basic ant colony algorithm.