求解子集问题的鲶鱼效应蝙蝠蚁群优化

为求解子集问题,提出一种新的基于图的蚂蚁系统--鲶鱼效应蝙蝠蚁群优化(catfish bat algorithm ant colony optimization,CBA-ACO)。基于子集问题的构造图,利用路径概率转移公式进行路径搜索,采用等效路径信息素增强进行信息素更新;动态维护一定数量较好路径作为档案信息;使用混沌映射并结合鲶鱼效应对蝙蝠算法(bat algorithm,BA)进行改进,在全局最优解多次未更新时,利用档案信息初始化鲶鱼效应增强搜索,返回较好路径解;采用本轮迭代最优更新和增强搜索更新两种方式更新信息素,兼顾算法的收敛速度和搜索能力。对算法进行了描述并分析算法复杂度。结果表明，CBA-ACO具有更好的稳定性和获取较好解的能力。

Catfish bat algorithm-ant colony optimization for subset problems

In order to resolve subset problems, a new graph-based ant system called catfish bat algorithm-ant colony optimization (CBA-ACO) is proposed. Based on the subset problem’s structure graph, routes’ probability transition-equation is used to search for solutions, equivalent routes’ pheromones strengthening is adopted to update pheromone. Some solutions are maintained in archive dynamically. The chaotic map and catfish effect are adopted to improve the bat algorithm (BA) for the enhanced exploration which is initialized by archive information and used to find better solution in case the global optimal solution is not updated after several runs. After one cycle, the best route of this cycle updating and the enhanced exploration updating are two cases of updating pheromone. As a result the convergence speed and searching capability of the algorithm are improved. The algorithm is described, and its complexity is analyzed. Experiments show that the CBA-ACO algorithm has a better stability and capability for obtaining the optimal solution.