基于多个改进策略的增强麻雀搜索算法  

Enhanced sparrow search algorithm based on multiple improvement strategies

作　　者：李大海 詹美欣 王振东 LI Dahai;ZHAN Meixin;WANG Zhendong(School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou Jiangxi 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：《计算机应用》2023年第9期2845-2854,共10页journal of Computer Applications

基　　金：国家自然科学基金资助项目(620620237);江西理工大学校级基金资助项目(205200100013)。

摘　　要：针对麻雀搜索算法(SSA)存在寻优精度不高且易陷入局部最优的问题,提出一种基于多个改进策略的增强麻雀搜索算法(EMISSA)。首先,为平衡算法的全局和局部搜索能力,引入模糊逻辑来动态调整麻雀发现者的规模;其次,对麻雀跟随者进行混合差分变异操作以产生变异子群,从而增强EMISSA跳出局部最优的能力;最后,通过拓扑对立学习(TOBL)产生当前麻雀发现者个体的拓扑对立解,以充分挖掘搜索空间内的优质位置信息。通过2013年进化计算大会(CEC2013)中的12个测试函数评估EMISSA、标准SSA以及混沌麻雀搜索优化算法(CSSOA)等改进麻雀算法的性能。实验结果表明,EMISSA在30维情况下,在12个测试函数上获得了11个第一;在80维情况下,在所有的测试函数上都获得了第一。而在Friedman检验中,EMISSA的排名均获得了第一。将EMISSA应用于障碍物环境下的无线传感器网络(WSN)节点部署,实验结果表明,相较于其他算法,EMISSA获得了最高的无线节点覆盖率,节点分布更均匀,覆盖冗余更少。Aiming at the drawbacks that Sparrow Search Algorithm(SSA)has relatively low search accuracy and is easy to fall into the local optimum,an Enhanced Sparrow Search Algorithm based on Multiple Improvement strategies(EMISSA)was proposed.Firstly,in order to balance the global search and local search abilities of the algorithm,fuzzy logic was introduced to adjust the scale of sparrow finders dynamically.Secondly,the mixed differential mutation operation was performed on sparrow followers to generate mutation subgroups,thereby enhancing the ability of EMISSA to jump out of the local optimum.Finally,Topological Opposition-Based Learning(TOBL)was used to obtain topological opposition solutions of sparrow finders,thereby fully mining high-quality position information in the search space.EMISSA,standard SSA and Chaotic Sparrow Search Optimization Algorithm(CSSOA)were evaluated by 12 test functions in 2013 Congress on Evolutionary Computation(CEC2013).Experimental results show that EMISSA achieves 11 first places on 12 test functions in the 30-dimensional case;in the 80-dimensional case,the proposed algorithm has the optimal results on all the test functions.In the Friedman test,EMISSA ranks first on all the test functions.Experimental results of applying EMISSA to the Wireless Sensor Network(WSN)node deployment in obstacle environment show that compared with other algorithms,EMISSA achieves the highest wireless node coverage with more uniform node distribution and less coverage redundancy.

关 键 词：麻雀搜索算法 模糊逻辑 混合差分变异操作 拓扑对立学习 无线传感器网络 节点部署 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]