分工协同蝙蝠算法  

Division of Labor and Coordinated Bat Algorithm

作　　者：李辉[1] LI Hui(Fujian College of Water Conservancy and Electric Power,Yong’an 366000,china)

机构地区：[1]福建水利电力职业技术学院,福建永安366000

出　　处：《电脑与信息技术》2020年第3期33-37,共5页Computer and Information Technology

摘　　要：针对蝙蝠算法收敛速度不高,且后期易陷入局部最优的缺点,提出分工协同蝙蝠算法,该算法将蝙蝠群体按适应度从小到大分为精英族群、平凡族群和淘汰族群,对不同族群采用不同的进化策略,并注重个体信息共享。实验表明:该算法的搜索性能比基本蝙蝠算法和相关文献中的算法都有显著提高,且该算法鲁棒性强,便于应用,具有较强的实用价值。As the convergence speed of the bat algorithm is not high,and the latter is easy to fall into the local optimum,a division of labor and bat algorithm is proposed.This algorithm divides the bat group into elite groups,ordinary groups and eliminated groups according to their fitness.We use different evolution strategies for different ethnic groups,and focus on individual Informa-tion sharing.Experiments show that the search performance of the algorithm is significantly improved compared with the basic bat algorithm and related algorithms in the literature.The algorithm is robust and easy to apply,and has strong practical value.

关 键 词：分工协同 蝙蝠算法 族群 搜索性能 

分 类 号：O224[理学—运筹学与控制论] TP301[理学—数学]