应用捕食搜索策略的改进多态蚁群算法  

Improved Polymorphic Ant Colony Algorithm with Predatory Search Strategy

作　　者：赵亚文 熊瑞平[1] 乔治 梁齐齐 罗勇 ZHAO Yawen;XIONG Ruiping;QIAO Zhi;LIANG Qiqi;LUO Yong(School of Manufacturing Science and Engineering,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学制造科学与工程学院

出　　处：《计算机工程与应用》2019年第14期115-121,共7页Computer Engineering and Applications

摘　　要：结合捕食搜索策略对多态蚁群算法进行改良。该算法引入以下机制:在人工蚁选择路径阶段,设置侦查素路径为优先,为非侦查素路径设置惩罚因子;利用权值在侦查素和非侦查素路径都施加信息素,通过该机制避免多态蚁群算法陷入停滞;在每轮人工蚁最优结果的邻域应用捕食搜索策略,并通过竞争机制选择最优解更新信息素。通过TSP的仿真实验结果表明,提出的融合算法可以有目的地指导信息素分布,加快算法向最优解的收敛速度及提高最优解质量,克服传统多态蚁群算法的缺陷。Aiming at the problem of iterative stagnation in solving the traveling salesman problem with polymorphic ant colony algorithm, the improved ant colony algorithm is proposed. Combining with the predatory search algorithm, a new fusion ant colony algorithm is proposed. This fusion algorithm introduces the following mechanisms. In the artificial ants path-selecting stage, the snoop path is set to be a priority, a penalty factor is set for a non-snoop path, the pheromone is applied to both the snoop and non-snoop paths, and the mechanism is used to avoid the polymorphic ant colony algorithm’s stagnation. The predator search strategy is applied in the neighborhood of the optimal result of each round of artificial ant, and the optimal solution is selected by the competitive mechanism to update the pheromone. The research shows that the above mechanism can overcome the shortcomings of the polymorphic ant colony algorithm. The predatory search strategy can be used to guide the distribution of pheromone effectively and speed up the convergence of the algorithm to the opti- mal solution and improve the quality of the optimal solution. Simulation results for the TSP problem prove the effective- ness of the proposed algorithm.

关 键 词：多态蚁群算法 捕食搜索 旅行商问题(TSP) 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]