| ［1］ |
POLI R， KENNEDY J， BLACKWELL T. Particle swarm optimization: An overview［J］. Swarm Intelligence， 2007，1（1）:33-57.
|
| ［2］ |
MIRJALILI S， MIRJALILI S M， LEWIS A. Grey wolf optimizer［J］. Advances in Engineering Software， 2014，69:46-61.
|
| ［3］ |
TAN Y， ZHU Y C. Fireworks algorithm for optimization［C］// Proceedings of the 2010 International Conference in Swarm Intelligence. 2010:355-364.
|
| ［4］ |
刘立群，顾任远. 核中心驱动混合蛙跳算法及其应用［J］. 计算机科学与探索， 2022，16（5）:1169-1181.
|
| ［5］ |
宋阿妮，包贤哲，权轶. 基于混沌自适应萤火虫算法的UAVs分配策略［J］. 计算机应用与软件， 2022，39（2）:300-306.
|
| ［6］ |
白晓波，邵景峰，王铁山，等. 分段搜索的果蝇算法及其对纺织企业资源配置［J/OL］. 计算机科学与探索:2022，16（10）：2330-2344.
|
| ［7］ |
WOLPERT D H， MACREADY W G. No free lunch theorems for optimization［J］. IEEE Transactions on Evolutionary Computation， 1997，1（1）:67-82.
|
| ［8］ |
HEIDARI A A， MIRJALILI S， FARIS H， et al. Harris hawks optimization: Algorithm and applications［J］. Future Generation Computer Systems， 2019，97:849-872.
|
| ［9］ |
贾鹤鸣，刘庆鑫，刘宇翔，等. 融合动态反向学习的阿奎拉鹰与哈里斯鹰混合优化算法［J/OL］. 智能系统学报，2023，18（01）：104-116.
|
| ［10］ |
ABUALIGAH L， YOUSRI D， ABD ELAZIZ M， et al. Aquila optimizer: A novel meta-heuristic optimization algorithm［J］. Computer & Industrial Engineering， 2021，157. DOI: 10.1016/j.cie.2021.107250.
|
| ［11］ |
汤安迪，韩统，徐登武，等. 混沌精英哈里斯鹰优化算法［J］. 计算机应用， 2021，41（8）:2265-2272.
|
| ［12］ |
高建瓴，喻明毫. 混合差分进化与二次插值改进哈里斯鹰算法［J/OL］. 小型微型计算机系统，2023，44（7）：1352-1359.
|
| ［13］ |
朱诚，潘旭华，张勇. 基于趋化校正的哈里斯鹰优化算法［J］. 计算机应用， 2022，42（4）:1186-1193.
|
| ［14］ |
刘小龙，梁彤缨. 基于方形邻域和随机数组的哈里斯鹰优化算法［J/OL］. 控制与决策:1-11［2022-07-31］. DOI: 10.13195/j.kzyjc.2021.0478.
|
| ［15］ |
WANG Y H， ZHOU Y Q， LUO Q F. Parameter optimization of shared electric vehicle dispatching model using discrete Harris hawks optimization［J］. Mathematical Biosciences and Engineering， 2022，19（7）:7284-7313.
|
| ［16］ |
SONG M J， JIA H M， ABUALIGAH L， et al. Modified Harris hawks optimization algorithm with exploration factor and random walk strategy［J］. Computational Intelligence and Neuroscience， 2022，2022. DOI: 10.1155/2022/4673665.
|
| ［17］ |
ELGAMAL Z M， YASIN N B M， TUBISHAT M， et al. An improved Harris hawks optimization algorithm with simulated annealing for feature selection in the medical field［J］. IEEE Access， 2020，8:186638-186652.
|
| ［18］ |
NANDI A， KAMBOJ V K. A Canis lupus inspired upgraded Harris hawks optimizer for nonlinear， constrained， continuous， and discrete engineering design problem［J］. International Journal for Numerical Methods in Engineering， 2021，122（4）:1051-1088.
|
| ［19］ |
HUSSIEN A G， AMIN M. A self-adaptive Harris hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection［J］. International Journal of Machine Learning and Cybernetics， 2022，13（2）:309-336.
|
| ［20］ |
KAMBOJ V K， NANDI A， BHADORIA A， et al. An intensify Harris hawks optimizer for numerical and engineering optimization problems［J］. Applied Soft Computing， 2020，89. DOI: 10.1016/j.asoc.2019.106018.
|
| ［21］ |
KAVEH A， RAHMANI P， ESLAMLOU A D. An efficient hybrid approach based on Harris hawks optimization and imperialist competitive algorithm for structural optimization［J］. Engineering with Computers， 2022，38（S2）:1555-1583.
|
| ［22］ |
ATASHPAZ-GARGARI E， LUCAS C. Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition［C］// Proceedings of the 2007 IEEE Congress on Evolutionary Computation. 2007:4661-4667.
|
| ［23］ |
GUPTA S， DEEP K， HEIDARI A A， et al. Opposition-based learning Harris hawks optimization with advanced transition rules: Principles and analysis［J］. Expert Systems with Applications， 2020，158. DOI: 10.1016/j.eswa.2020.
|
|
113510.
|
| ［24］ |
HUANG W N， PENG Y， GE Y， et al. A new Kmeans clustering model and its generalization achieved by joint spectral embedding and rotation［J］. PeerJ Computer Science， 2021，7. DOI: 10.7717/peerj-cs.450.
|
| ［25］ |
陈贵敏，贾建援，韩琪. 粒子群优化算法的惯性权值递减策略研究［J］. 西安交通大学学报， 2006，40（1）:53-56.
|
| ［26］ |
TAHERI A， RAHIMIZADEH K， RAO R V. An efficient balanced teaching-learning-based optimization algorithm with individual restarting strategy for solving global optimization problems［J］. Information Sciences， 2021，576:68-104.
|
