摘要
为解决基本蝙蝠算法中存在的易陷入局部最优且求解精度不足的问题,提出一种改进的混合蝙蝠算法,引入了分组迭代模式和多种速度迭代公式加强了全局搜索能力,更新了种群领域搜索公式的基础上引用了t分布作为种群最优解的领域搜索方式,补足了蝙蝠算法的局部搜索能力,避免了算法陷入局部最优解。通过多个标准测试函数的实验验证改进的混合蝙蝠算法能有效解决基本蝙蝠算法中出现的问题。
In order to solve the problem that the basic bat algorithm is easy to fall into the local optimal and the precision of solving is insufficient,an improved hybrid bat algorithm is proposed,the packet iteration mode and various velocity iteration formulas are introduced to strengthen the global search ability,and the domain search method of t distribution as the optimal solution of the population is referenced on the basis,the local search ability of BAT algorithm is supplemented to avoid the algorithm falling into the local optimal solution.Through the experiment of several standard test functions,it is proved that the improved hybrid bat algorithm can effectively solve the problems in the basic bat algorithm.
作者
郜振华
吴昊
GAO Zhenhua;WU Hao(Institute of Management Science and Engineering,Anhui University of Technology,Maanshan,Anhui 243000,China)
出处
《南华大学学报（自然科学版）》
2019年第1期62-66,共5页
Journal of University of South China：Science and Technology
关键词
蝙蝠算法
混合算法
分组迭代
bat algorithm
hybrid algorithm
group iteration
