求解TSP 问题的离散狼群算法

通过定义反转算子, 对人工狼位置和智能行为重新进行整数编码设计, 并结合概率近邻初始化方法, 提出一种求解旅行商问题的离散狼群算法. 该算法保留了狼群算法基于职责分工的协作式搜索特性, 并较好地平衡了算法的广度开拓和深度开采能力. 采用C-TSP 问题和TSPLIB 数据库中的多组TSP 问题作为实验用算例, 并将所提出算法与其他5 种智能优化算法进行对比, 仿真结果表明, 所提出算法在求解准确率、稳定性和所需迭代次数等方面具有相对优势.

Discrete wolf pack algorithm for traveling salesman problem

Based on designing of reverse operator integer coding, the artificial wolves’ position and intelligent behaviors are redesigned. Then combined with the probabilistic nearest neighbor method, a discrete wolf pack algorithm(DWPA) is proposed to solve traveling salesman problem(TSP). The DWPA preserves the cooperative searching feature based on job distribution of the wolf pack, and makes a good balance between the searching ability in breadth and depth. Algorithm efficiency of TSP problems against five existing algorithms is examined for C-TSP and some TSPLIB instances of various sizes. Simulation results show that the DWPA has more advantages on accurate rate, computational robustness and iteration number.