[1] 王凌. 智能优化算法及其应用[M]. 北京: 清华大学出版社, 2001.
WANG L. Intelligent optimization algorithm and its appli-cation[M]. Beijing: Tsinghua University Press, 2001.
[2] 张松灿, 普杰信, 司彦娜, 等. 基于种群相似度的自适应改进蚁群算法及应用[J]. 计算机工程与应用, 2021, 57(8): 70-77.
ZHANG S C, PU J X, SI Y N, et al. Adaptive improved ant colony algorithm based on population similarity and its app-lication[J]. Computer Engineering and Applications, 2021, 57(8): 70-77.
[3] 卜冠南, 刘建华, 姜磊, 等. 一种自适应分组的蚁群算法[J]. 计算机工程与应用, 2021, 57(6): 67-73.
BU G N, LIU J H, JIANG L, et al. Ant colony algorithm with adaptive grouping[J]. Computer Engineering and App-lications, 2021, 57(6): 67-73.
[4] 王贵程, 吴国新, 左云波, 等. 基于改进蚁群算法包装机器人轨迹规划研究[J]. 电子测量与仪器学报, 2019, 33(8):100-106.
WANG G C, WU G X, ZUO Y B, et al. Research on trajec-tory planning of packaging robot based on improved ant colony algorithm[J]. Journal of Electronic Measurement and Instrumentation, 2019, 33(8): 100-106.
[5] MAHESHWARI P, AJAY K S, VERMA K. Energy efficient cluster based routing protocol for WSN using butterfly opti-mization algorithm and ant colony optimization[J]. Ad Hoc Networks, 2020, 110: 102317.
[6] 薛建凯, 沈波. 一种新型群智能优化算法:麻雀搜索算法[J]. 系统科学与控制工程, 2020, 8(1): 22-34.
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] 何国松, 董泽, 孙明. 基于混合量子麻雀算法的过热汽温模型参数辨识[J]. 华北电力大学学报(自然科学版), 2023(1): 92-100.
HE G S, DONG Z, SUN M. Parameter identification of superheated steam temperature model based on hybrid quantum sparrow algorithm[J]. Journal of North China Electric Power University (Natural Science Edition), 2023(1): 92-100.
[8] 高兵, 郑雅, 秦静, 等. 基于麻雀搜索算法和改进粒子群优化算法的网络入侵检测算法[J]. 计算机应用, 2022, 42(4): 1201-1206.
GAO B, ZHENG Y, QIN J, et al. Network intrusion detection algorithm based on sparrow search algorithm and improved particle swarm optimization algorithm[J]. Journal of Computer Applications, 2022, 42(4): 1201-1206.
[9] 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 Deci-sion, 2022, 37(1): 87-96.
[10] ABUALIGAH L, DIANAT A, MIRJALILI S, et al. The arithmetic optimization algorithm[J]. Computer Methods in Applied Mechanics and Engineering, 2021, 376: 113609.
[11] 王海英, 黄强, 李传涛, 等. 图论算法及其MATLAB实现[M]. 北京: 北京航空航天大学出版社, 2010.
WANG H Y, HUANG Q, LI C T, et al. Graph theory algorithm and its MATLAB implementation[M]. Beijing: Beihang University Press, 2010.
[12] SOLIS F J, WETS J B. Minimization by random search techniques[J]. Mathematics of Operations Research, 1981, 6(1): 19-30.
[13] 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 com-bining Cauchy mutation and opposition-based learning[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(6): 1155-1164.
[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]. 北京航空航天大学学报, 2023, 49(3): 681-692.
TANG Y Q, LI C H, SONG Y F, et al. Adaptive mutation sparrow search optimization algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49(3): 681-692.
[15] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.
[16] 严逍亚, 王振雷, 王昕. 动态调整成长方式的郊狼优化算法及其应用[J]. 计算机工程, 2022, 48(7): 73-81.
YAN X Y, WANG Z L, WANG X. Coyote optimization algorithm with dynamically adjusting growth mode and its application[J]. Computer Engineering, 2022, 48(7): 73-81.
[17] HE Q, WANG L. An effective co-evolutionary particle swarm optimization for constrained engineering design problems[J]. Engineering Applications of Artificial Intelligence, 2007, 20(1): 89-99. |