| 51 | 0 | 13 |
| 下载次数 | 被引频次 | 阅读次数 |
麻雀搜索算法(SSA)作为一种新型群智能优化算法，凭借其结构简单、参数少、收敛速度快等优势，近年来在学术界引起了广泛关注。围绕麻雀搜索算法的基本原理、改进策略及应用等方面对其近年来的研究进行综述。首先，阐述了标准SSA的基本原理与实现步骤，并对比分析了SSA与其他智能优化算法在典型测试函数上的性能表现。其次，分析了SSA在种群初始化、发现者位置更新、跟随者位置更新、警戒者位置更新以及扰动策略等关键环节的改进方法。再次，梳理了SSA在电力系统优化与预测、故障诊断、路径优化以及参数优化等工程领域中的典型应用。最后，基于SSA的研究现状和应用特点，对其未来的研究方向进行了探讨。
Abstract：Sparrow search algorithm(SSA) is a new optimization algorithm with the advantages of simlicity, less parameters and fast convergence speed, which has received great attention of researchers. Achievements of sparrow algorithm in recent years are reviewed around the basic principle, improvement strategy and application of sparrow search algorithm. Firstly, the principle and steps of the sparrow search algorithm are introduced, and the performance of SSA and other algorithms in solving related functions is compared and analyzed. Secondly, the improvement method of initial population, the producers updating, the scroungers updating, the vigilances updating and disturbance strategy in SSA are analyzed and classed. Thirdly, the application of SSA is classified and discussed in power operation optimization and prediction, fault diagnosis, path optimization, shop scheduling, and parameter optimization. Finally, according to the characteristics of the sparrow search algorithm and the current research status, the future research direction of SSA is prospected.
[1] KARABOGA D,GORKEMLI B,OZTURK C,et al.A comprehensive survey:Artificial bee colony (ABC) algorithm and applications[J].Artificial Intelligence Review,2014,42(1):21-57.
[2] LIU C G,YAN X H,LIU C Y,et al.The wolf colony algorithm and its application[J].Chinese Journal of Electronics,2011,20(2):212-216.
[3] SHEHAB M,ABU-HASHEM M A,SHAMBOUR M K Y,et al.A comprehensive review of bat inspired algorithm:Variants,applications,and hybridization[J].Archives of Computational Methods in Engineering,2023,30(2):765-797.
[4] FISTER I,FISTER I Jr,YANG X-S,et al.A comprehensive review of firefly algorithms[J].Swarm and Evolutionary Computation,2013,13:34-46.
[5] DORIGO M,MANIEZZO V,COLORNI A.Ant system:Optimization by a colony of cooperating agents[J].IEEE Transactions on Systems,Man,and Cybernetics Part B,Cybernetics,1996,26(1):29-41.
[6] XUE J K,SHEN B.A novel swarm intelligence optimization approach:Sparrow search algorithm[J].Systems Science & Control Engineering,2020,8(1):22-34.
[7] LIU T T,YUAN Z,WU L,et al.An optimal brain tumor detection by convolutional neural network and Enhanced Sparrow Search Algorithm[J].Proceedings of the Institution of Mechanical Engineers Part H,Journal of Engineering in Medicine,2021,235(4):459-469.
[8] 吕鑫，慕晓冬，张钧.基于改进麻雀搜索算法的多阈值图像分割[J].系统工程与电子技术，2021,43(2):318-327.
[9] YUAN J H,ZHAO Z W,LIU Y P,et al.DMPPT control of photovoltaic microgrid based on improved sparrow search algorithm[J].IEEE Access,2021,9:16623-16629.
[10] FATHY A,ALANAZI T M,REZK H,et al.Optimal energy management of micro-grid using sparrow search algorithm[J].Energy Reports,2022,8:758-773.
[11] ZHU Y L,YOUSEFI N.Optimal parameter identification of PEMFC stacks using Adaptive Sparrow Search Algorithm[J].International Journal of Hydrogen Energy,2021,46(14):9541-9552.
[12] DONG J,DOU Z H,SI S Q,et al.Optimization of capacity configuration of wind-solar-diesel-storage using improved sparrow search algorithm[J].Journal of Electrical Engineering & Technology,2022,17(1):1-14.
[13] WANG P,ZHANG Y,YANG H W.Research on economic optimization of microgrid cluster based on chaos sparrow search algorithm[J].Computational Intelligence and Neuroscience,2021,2021(1):5556780.
[14] GAI J B,ZHONG K Y,DU X J,et al.Detection of gear fault severity based on parameter-optimized deep belief network using sparrow search algorithm[J].Measurement,2021,185:110079.
[15] TUERXUN W,XU C,GUO H Y,et al.Fault diagnosis of wind turbines based on a support vector machine optimized by the sparrow search algorithm[J].IEEE Access,2021,9:69307-69315.
[16] WU C Y,FU X S,PEI J K,et al.A novel sparrow search algorithm for the traveling salesman problem[J].IEEE Access,2021,9:153456-153471.
[17] LI P C,DONG B T,LI S X,et al.A repair method for missing traffic data based on FCM,optimized by the twice grid optimization and sparrow search algorithms[J].Sensors,2022,22(11):4304.
[18] ZHANG Z,HE R,YANG K.A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm[J].Advances in Manufacturing,2022,10(1):114-130.
[19] LIU G Y,SHU C,LIANG Z W,et al.A modified sparrow search algorithm with application in 3d route planning for UAV[J].Sensors,2021,21(4):1224.
[20] ZHANG G J,ZHANG E H.An improved sparrow search based intelligent navigational algorithm for local path planning of mobile robot[J].Journal of Ambient Intelligence and Humanized Computing,2023,14(10):14111-14123.
[21] YAN S Q,YANG P,ZHU D L,et al.Improved sparrow search algorithm based on iterative local search[J].Computational Intelligence and Neuroscience,2021,2021(1):6860503.
[22] 汤安迪，韩统，徐登武，等.基于混沌麻雀搜索算法的无人机航迹规划方法[J].计算机应用，2021,41(7):2128-2136.
[23] 李雅丽，王淑琴，陈倩茹，等.若干新型群智能优化算法的对比研究[J].计算机工程与应用，2020,56(22):1-12.
[24] 薛建凯.一种新型的群智能优化技术的研究与应用——麻雀搜索算法[D].上海：东华大学，2020.
[25] 欧阳城添，朱东林.融合K-means的多策略改进麻雀搜索算法研究[J].电光与控制，2021,28(12):11-16.
[26] ZHANG C L,DING S F.A stochastic configuration network based on chaotic sparrow search algorithm[J].Knowledge-Based Systems,2021,220:106924.
[27] 张玉杰，王帆.基于改进麻雀搜索算法的照明控制优化[J].计算机应用，2023,43(3):835-841.
[28] 马卫，朱娴.基于莱维飞行扰动策略的麻雀搜索算法[J].应用科学学报，2022,40(1):116-130.
[29] 毛清华，张强.融合柯西变异和反向学习的改进麻雀算法[J].计算机科学与探索，2021,15(6):1155-1164.
[30] YANG X X,LIU J,LIU Y,et al.A novel adaptive sparrow search algorithm based on chaotic mapping and T-distribution mutation[J].Applied Sciences,2021,11(23):11192.
[31] WANG Z K,HUANG X Y,ZHU D L.A multistrategy-integrated learning sparrow search algorithm and optimization of engineering problems[J].Computational Intelligence and Neuroscience,2022,2022(1):2475460.
[32] 回立川，陈雪莲，孟嗣博.多策略混合的改进麻雀搜索算法[J].计算机工程与应用，2022,58(16):71-83.
[33] 李鹏，丁倩雯.基于麻雀算法优化的OSTU分割算法[J].电子测量技术，2021,44(19):148-154.
[34] 王海瑞，鲜于建川.改进麻雀搜索算法在分布式电源配置中的应用[J].计算机工程与应用，2021,57(20):245-252.
[35] GAO B W,SHEN W,GUAN H,et al.Research on multistrategy improved evolutionary sparrow search algorithm and its application[J].IEEE Access,2022,10:62520-62534.
[36] MA J,HAO Z Y,SUN W J.Enhancing sparrow search algorithm via multi-strategies for continuous optimization problems[J].Information Processing & Management,2022,59(2):102854.
[37] LI Z F,ZHAO C C,ZHANG G H,et al.Multi-strategy improved sparrow search algorithm for job shop scheduling problem[J].Cluster Computing,2024,27(4):4605-4619.
[38] 高晨峰，陈家清，石默涵.融合黄金正弦和曲线自适应的多策略麻雀搜索算法[J].计算机应用研究，2022,39(2):491-499.
[39] 宋立钦，陈文杰，陈伟海，等.基于混合策略的麻雀搜索算法改进及应用[J].北京航空航天大学学报，2023,49(8):2187-2199.
[40] 张伟康，刘升，任春慧.混合策略改进的麻雀搜索算法[J].计算机工程与应用，2021,57(24):74-82.
[41] LIU J H,WANG Z H.A hybrid sparrow search algorithm based on constructing similarity[J].IEEE Access,2021,9:117581-117595.
[42] 刘睿，莫愿斌.增强型麻雀搜索算法及其工程优化应用[J].小型微型计算机系统，2023,44(3):497-505.
[43] 陈功，曾国辉，黄勃，等.螺旋探索与自适应混合变异的麻雀搜索算法[J].小型微型计算机系统，2023,44(4):779-786.
[44] 王子恺，黄学雨，朱东林，等.融合边界处理机制的学习型麻雀搜索算法[J].北京航空航天大学学报，2024,50(1):286-298.
[45] 贺航，马小晶，王宏伟，等.基于改进麻雀搜索算法的森林火灾图像多阈值分割[J].科学技术与工程，2021,21(26):11263-11270.
[46] 段玉先，刘昌云.基于Sobol序列和纵横交叉策略的麻雀搜索算法[J].计算机应用，2022,42(1):36-43.
[47] 胡林静，郭朝泽，王景帅.基于ISSA-LSSVM模型的短期电力负荷预测[J].科学技术与工程，2021,21(23):9916-9922.
[48] 闫少强，杨萍，朱东林，等.基于佳点集的改进麻雀搜索算法[J].北京航空航天大学学报，2023,49(10):2790-2798.
[49] 于权伟，李光，谢楚政，等.改进混沌麻雀搜索算法及其在冗余机械臂逆运动学求解中的应用[J].机械科学与技术，2023,42(5):702-708.
[50] 刘成汉，何庆.改进搜索机制的单纯形法引导麻雀搜索算法[J].计算机工程与科学，2022,44(12):2238-2245.
[51] 温泽宇，谢珺，谢刚，等.基于新型拥挤度距离的多目标麻雀搜索算法[J].计算机工程与应用，2021,57(22):102-109.
[52] ZHANG J N,XIA K W,HE Z P,et al.Semi-supervised ensemble classifier with improved sparrow search algorithm and its application in pulmonary nodule detection[J].Mathematical Problems in Engineering,2021,2021(1):6622935.
[53] ZHOU S H,XIE H,ZHANG C C,et al.Wavefront-shaping focusing based on a modified sparrow search algorithm[J].Optik,2021,244:167516.
[54] 欧阳城添，朱东林，王丰奇，等.基于折射麻雀搜索算法的无人机路径规划[J].电光与控制，2022,29(6):25.
[55] FAN Y Y,ZHANG Y,GUO B S,et al.A hybrid sparrow search algorithm of the hyperparameter optimization in deep learning[J].Mathematics,2022,10(16):3019.
[56] 付华，刘昊.多策略融合的改进麻雀搜索算法及其应用[J].控制与决策，2022,37(1):87-96.
[57] 汤安迪，韩统，徐登武，等.基于等级制度和布朗运动的混沌麻雀搜索算法[J].空军工程大学学报(自然科学版),2021,22(3):96-103.
[58] 许亮，张紫叶，陈曦，等.基于改进麻雀搜索算法优化BP神经网络的气动光学成像偏移预测[J].光电子·激光，2021,32(6):653-658.
[59] 刘丽娜，南新元，石跃飞.改进麻雀搜索算法求解作业车间调度问题[J].计算机应用研究，2021,38(12):3634-3639.
[60] 张然，潘芷涵，尹毅峰，等.基于SAA-SSA-BPNN的网络安全态势评估模型[J].计算机工程与应用，2022,58(11):117-124.
[61] 王金玉，金宏哲，王海生，等.ISSA优化Attention双向LSTM的短期电力负荷预测[J].电力系统及其自动化学报，2022,34(5):111-117.
[62] 杨邓，杨俊杰，胡晨阳，等.基于改进LSSVM的短期电力负荷预测[J].电子测量技术，2021,44(18):47-53.
[63] 刘栋，魏霞，王维庆，等.基于SSA-ELM的短期风电功率预测[J].智慧电力，2021,49(6):53-59,123.
[64] 张守京，慎明俊，杨静雯，等.采用参数自适应最大相关峭度解卷积的滚动轴承故障特征提取[J].西安交通大学学报，2022,56(3):75-83.
[65] 李怡，李焕锋，刘自然.基于CEEMDAN多尺度熵和SSA-SVM的滚动轴承故障诊断研究[J].机电工程，2021,38(5):599-604.
[66] 单亚峰，段金凤，付华，等.基于SSA-AdaBoost-SVM的变压器故障诊断[J].控制工程，2022,29(2):280-286.
[67] 石明江，陈瑞，冯林.基于磁记忆的金属管道缺陷检测方法[J].电子测量与仪器学报，2022,36(1):44-53.
[68] 王红君，李万丰，赵辉，等.基于改进VMD-SSA的直流微电网故障检测技术研究[J].电工电能新技术，2022,41(2):53-62.
[69] 杨玮，杨白月，王晓雅，等.低碳环境下冷链物流企业库存—配送优化[J].包装工程，2021,42(11):45-52.
[70] 熊国文，张敏，许文鑫.基于众包模式的两级开闭混合车辆路径优化[J].浙江大学学报(工学版),2021,55(12):2397-2408.
[71] 宋立业，胡朋举.改进SSA在三维路径规划中的应用[J].传感器与微系统，2022,41(3):158-160.
[72] YAN P C,SHANG S H,ZHANG C Y,et al.Research on the processing of coal mine water source data by optimizing BP neural network algorithm with sparrow search algorithm[J].IEEE Access,2021,9:108718-108730.
[73] 贾丙宏，祝文硕，王瑞富，等.基于SSA-BP神经网络模型的风暴潮灾害损失评估[J].海洋预报，2022,39(2):50-58.
[74] 石颉，杜国庆.改进麻雀搜索算法优化SVM的方法及应用[J].计算机工程与设计，2023,44(3):954-961.
[75] 尹航，吕佳威，陈耀聪，等.基于LightGBM-SSA-ELM的新疆羊舍CO2浓度预测[J].农业机械学报，2022,53(1):261-270.
[76] 周勇，张杰，钟祾充，等.铁路集装箱中心站多轨道吊柔性协同调度优化[J].交通运输系统工程与信息，2022,22(1):133-141.
基本信息:
DOI：
中图分类号:TP18
引用信息:
[1]李峥峰,叶文枭,梅亚航.麻雀搜索算法及其应用研究综述[J].中原工学院学报,2025,36(04):1-10+45.
基金信息:
国家自然科学基金项目(U1904167); 河南省高等学校重点科研项目(19A460034); 河南省重点研发专项(231111221200); 教育部人文社科项目(23YJAZH193)