采用动态分割种群策略的改进MBO  

Improved monarch butterfly optimization by using strategy of dynamic-dividing population

作　　者：蒙丽萍 王勇 黄华娟 MENG Liping;WANG Yong;HUANG Huajuan(College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China;Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence, Nanning 530006, China)

机构地区：[1]广西民族大学信息科学与工程学院,南宁530006 [2]广西高校复杂系统与智能计算重点实验室,南宁530006

出　　处：《计算机工程与应用》2017年第18期149-156,共8页Computer Engineering and Applications

基　　金：广西自然科学基金(No.0832084);广西高等学校科研项目(No.KY2015YB078)

摘　　要：大红斑蝶优化算法(MBO)是最近提出的一种新的群智能优化算法。然而,该算法仍存在收敛速度较慢、易陷入局部最优的缺点。为克服MBO算法之不足,提出了一种改进的大红斑蝶优化算法(IMBO)。该算法采用将群体动态随机分割成两个子群体的策略,不同子群体中的大红斑蝶采用不同的搜索方法,以保持种群搜索的多样性。通过10个基准函数的仿真实验并与MBO算法以及标准PSO算法相比较,结果表明IMBO算法的全局搜索能力有了明显的提高,在函数优化中具有更好的收敛速度及稳定性。Monarch Butterfly Optimization(MBO)is a novel swarm intelligent optimization algorithm.Yet there are stillthe defects of slow convergence and easy being trapped into local optima in the MBO.In order to overcome the shortcomingsof the MBO,an Improved Monarch Butterfly Optimization(IMBO)is proposed in this paper.The IMBO uses the strategyof dynamic and random dividing the population into two sub-populations at every time-step,and the butterflies in differentsub-populations usually use different searching methods in order to keep the diversity of population search.Experimentsare done on a set of10benchmark functions,and the results show that the proposed algorithm has marked advantage ofglobal convergence property,can improve the convergence efficiency in function optimization,and is more stable whenbeing compared with MBO and PSO algorithms.

关 键 词：大红斑蝶优化算法 优化 智能计算 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]