[1] ZHANG X M,SUN B Y,MEI T,et al.A survey of swarm intelligence algorithm[J].Advanced Science Letters,2012,11(1):842-845.
[2] KENNEDY J,EBERHART R C.Particle swarm optimization[C]//Proceedings of the 1995 International Conference on Neural Networks,Perth,1995:1942-1948.
[3] KRISHNANAND K N.Glowworm swarm optimization:a multimodal function optimization paradigm with applications to multiple signal source localization tasks[D].Indian Institute of Science,2007.
[4] HOLLAND J H.Adaptation in natural and artificial system[M].Michigan:University of Michigan Press,1975:971-113.
[5] MIRJALILI S,MIRJALILI S M,LEWIS A,et al.Grey wolf optimizer[J].Advances in Engineering Software,2014,69:46-61.
[6] GANDOMI A H,ALAVI A H.Krill herd:a new bio-inspired optimization algorithm[J].Communications in Nonlinear Science & Numerical Simulation,2012,17(12):4831-4845.
[7] XUE J K,SHEN B.A novel swarm intelligence optimization approach:sparrow search algorithm[J].Systems Science & Control Engineering,2020,8(1):22-34.
[8] 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.
FU H,LIU H.Improved sparrow search algorithm with multi-strategy integration and its application[J].Control and Decision,2022,37(1):87-96.
[9] 吴丁杰,周庆兴,温立书.基于Logistic混沌映射的改进麻雀算法[J].高师理科学刊,2021,41(6):10-15.
WU D J,ZHOU Q X,WEN L S.Improved sparrow algorithm based on Logistic chaos mapping[J].Journal of Science of Teachers’ College and University,2021,41(6):10-15.
[10] 吕鑫,慕晓冬,张钧,等.混沌麻雀搜索优化算法[J].北京航空航天大学学报,2021,47(8):1712-1720.
LV X,MU X D,ZHANG J,et al.Chaos sparrow search optimization algorithm[J].Journal of Beijing University of Aeronautics and Astronautics,2021,47(8):1712-1720.
[11] 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.
MAO Q H,ZHANG Q.Improved sparrow algorithm combining Cauchy mutation and opposition-based learning[J].Journal of Frontiers of Computer Science and Technology,2021,15(6):1155-1164.
[12] 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.
ZHANG W K,LIU S,REN C H.Mixed strategy to improved sparrow search algorithm[J].Computer Engineering and Applications,2021,57(24):74-82.
[13] 李爱莲,全凌翔,崔桂梅,等.融合正余弦和柯西变异的麻雀搜索算法[J].计算机工程与应用,2022,58(3):91-99.
LI A L,QUAN L X,CUI G M,et al.A sparrow search algorithm combining sine-cosine and Cauchy mutation[J].Computer Engineering and Applications,2022,58(3):91-99.
[14] 唐延强,李成海,宋亚飞,等.自适应变异麻雀搜索优化算法[J/OL].北京航空航天大学学报[2021-11-23].https://doi.org/10.13700/j.bh.1001-5965.2021.0282.
TANG Y Q,LI C H,SONG Y F,et al.Adaptive mutation sparrow search optimization algorithm[J/OL].Journal of Beijing University of Aeronautics and Astronautics[2021-11-23].https://doi.org/10.13700/j.bh.1001-5965.2021.0282.
[15] FENG J H,ZHANG J,ZHU X S,et al.A novel chaos optimization algorithm[J].Multimedia Tools and Applications,2017,76(16):17405-17436.
[16] 刘婷,张立毅,鲍韦韦,等.全局最优引导的差分演化二进制人工蜂群算法[J].计算机工程与应用,2013,49(6):43-47.
LIU T,ZHANG L Y,BAO W W,et al.Differential evolution binary artificial bee colony algorithm based on global best[J].Computer Engineering and Applications,2013,49(6):43-47.
[17] DUAN H B,QIAO P X.Pigeon-inspired optimization:a new swarm intelligence optimizer for air robot path planning[J].International Journal of Intelligent Computing and Cybernetics,2014,7:24-37.
[18] 吴润秀,孙辉,朱德刚,等.具有高斯扰动的最优粒子引导粒子群优化算法[J].小型微型计算机系统,2016,37(1):146-151.
WU R X,SUN H,ZHU D G,et al.Particle swarm optimization algorithm based on optimal particle guidance and Gauss perturbance[J].Journal of Chinese Computer Systems,2016,37(1):146-151.
[19] 董红斌,李冬锦,张小平.一种动态调整惯性权重的粒子群优化算法[J].计算机科学,2018,45(2):98-102.
DONG H B,LI D J,ZHANG X P.Particle swarm optimization algorithm with dynamically adjusting inertia weight[J].Computer Science,2018,45(2):98-102.
[20] 陈文平.多策略分层学习萤火虫算法研究及应用[D].南昌:南昌工程学院,2020.
CHEN W P.Research and application of firefly algorithm based on multi-strategy and level-based learning[D].Nanchang:Nanchang Institule of Technology,2020.
[21] 张津源,张军,季伟东,等.具备自纠正和逐维学习能力的粒子群算法[J].小型微型计算机系统,2021,42(5):919-926.
ZHANG J Y,ZHANG J,JI W D,et al.Particle swarm optimization algorithm with self-correcting and dimension by dimension learning capabilities[J].Journal of Chinese Computer Systems,2021,42(5):919-926.
[22] 张孟健,张浩,陈曦,等.基于Cubic映射的灰狼优化算法及应用[J].计算机工程与科学,2021,43(11):2035-2042.
ZHANG M J,ZHANG H,CHEN X,et al.A grey wolf optimization algorithm based on Cubic mapping and its application[J].Computer Engineering and Science,2021,43(11):2035-2042.
[23] 谈庆,黄樟灿.基于近邻套索算子的磷虾群算法[J].计算机工程与应用,2019,55(9):124-129.
TAN Q,HUANG Z C.Krill herd with nearest neighbor lasso operator[J].Computer Engineering and Applications,2019,55(9):124-129.
[24] SUN Y J,WANG X L,CHEN Y H,et al.A modified whale optimization algorithm for large-scale global optimization problems[J].Expert Systems with Applications,2018,114:563-577.
[25] DERRAC J,GARCIA S,MOLINA D,et al.A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms[J].Swarm and Evolutionary Computation,2011,1(1):3-18.
[26] 徐苗.基于人工鱼群算法的SVM参数优化[J].山西电子技术,2019(1):30-33.
XU M.SVM parameter optimization based on artificial fish swarm algorithm[J].Shanxi Electronic Technology,2019(1):30-33.
[27] SMITH W A,RANDALL R B.Rolling element bearing diagnostics using the case western reserve university data:a benchmark study[J].Mechanical Systems and Signal Processing,2015,64(21):100-131.