基于多策略改进的麻雀搜索算法  

An Improved Sparrow Search Algorithm Based on Multi-strategy

作　　者：卢磊 贺智明[1] 黄志成 LU Lei;HE Zhi-ming;HUANG Zhi-cheng(College of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China)

机构地区：[1]江西理工大学信息工程学院,江西赣州341000

出　　处：《计算机与现代化》2023年第10期23-31,共9页Computer and Modernization

摘　　要：针对麻雀搜索算法(SSA)迭代末期种群多样性衰减、易陷入局部最优等缺陷,提出一种基于多策略改进的麻雀搜索算法(MUSSA)。MUSSA首先采用混沌透镜反向策略增强种群多样性,并根据遗忘递减策略,逐步减少使用反向迭代策略的种群数,降低无用搜索损耗,加快算法收敛速度;然后引入自适应权重螺旋搜索策略和参考系机制对发现者更新公式进行修改,进一步扩大个体的搜索范围,增强算法的全局搜索能力;最后,在追随者更新策略中引入方向因子和非静态选择策略,增强局部挖掘能力。利用13个基准测试函数进行模拟仿真测试,实验结果表明MUSSA相较于SSA、HHO、WOA和AO具有更好的寻优性能。To address the problems that the population diversity of the sparrow search algorithm(SSA)decreases in the late it⁃eration and easily falls into local optimum,a multi-strategy based improved sparrow search algorithm(MUSSA)is proposed.Firstly,MUSSA uses opposition-based learning and iterative strategy to enhance population diversity.According to the forgotten decline strategy,the number of populations using the reverse iteration strategy is gradually reduced,the loss of useless search is reduced,and the convergence speed of the algorithm is accelerated.Then,the adaptive weight spiral search strategy and refer⁃ence frame mechanism are introduced to modify the discoverer update formula,further expand the search range of individuals and enhance the global search capability of the algorithm.Finally,direction factor and non-static selection strategy are intro⁃duced into follower renewal strategy to enhance local mining excavation.The simulation results of 13 benchmark test functions show that MUSSA has better optimization performance than SSA,HHO,WOA and AO.

关 键 词：麻雀搜索算法 反向学习 Iterative映射 遗忘曲线 螺旋策略 自适应权重 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]