广西师范大学学报（自然科学版） ›› 2024, Vol. 42 ›› Issue (4): 153-164.doi: 10.16088/j.issn.1001-6600.2023100902

• 研究论文 • 上一篇    下一篇

多策略改进的猎人猎物优化算法及其应用

唐天兵*, 李继发, 严毅*   

  1. 广西大学 计算机与电子信息学院, 广西 南宁 530004
  • 收稿日期:2023-10-09 修回日期:2023-12-29 出版日期:2024-07-25 发布日期:2024-09-05
  • 通讯作者: 唐天兵(1972—), 男, 四川成都人, 广西大学副教授。E-mail:tbtangtbtang@126.com;严毅(1971—), 男, 广西桂平人, 广西大学高级工程师。E-mail:ccie@gxu.edu.cn
  • 基金资助:
    国家自然科学基金(62266004)

Multi-strategy Improved of Hunter-Prey Optimization Algorithm and Its Application

TANG Tianbing*, LI Jifa, YAN Yi*   

  1. School of Computer, Electronics and Information, Guangxi University, Nanning Guangxi 530004, China
  • Received:2023-10-09 Revised:2023-12-29 Online:2024-07-25 Published:2024-09-05

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摘要： 针对猎人猎物优化算法易陷入局部最优和收敛精度不足的问题,本文提出多策略改进的猎人猎物优化算法。该算法基于动态搜索思想,通过自适应机制从全局搜索转向局部开发;通过利用种群的历史信息来实施差分进化,从而增强种群的多样性;采用精英池策略和非线性步长相结合的方法,以防止算法陷入局部最优,并提升其收敛精度。在10个大规模(10 000维)测试函数上对改进后的算法和其他6种经典或最新的优化算法进行性能评估,结果显示,该算法在全局优化能力、寻优精度和稳定性方面均表现出色,能有效解决高维优化问题。最后,将多策略改进猎人猎物优化算法应用于三维无人机路径规划问题,仿真实验结果表明,该算法能求解到最优的无人机三维规划路径。

关键词: 猎人猎物优化算法, 差分进化, 高维优化, 多策略, 路径规划

Abstract: Aiming at the shortcomings of the hunter-prey optimization algorithm, which is easy to fall into local optimum and low convergence precision, a multi-strategy improved hunter-prey optimization algorithm is proposed. The proposed algorithm is based on the idea of dynamic search, and the adaptive adjustment algorithm is transformed from global search to local search; the historical information of the population is used for differential evolution to improve the diversity of the population; combined with the elite pool strategy and nonlinear step size, the algorithm is prevented from falling into local optimum, to improve the convergence accuracy of the algorithm. The improved algorithm and six other classical or state-of-the-art optimization algorithms are evaluated for their performance on 10 large-scale (10 000-dimensional) test functions. The experimental results show that the proposed algorithm has better global optimization capabilities, optimization accuracy and stability, and can effectively solve high-dimensional optimization problems. Finally, the multi-strategy improved hunter-prey optimization algorithm is applied to the path planning problem of 3D UAV. The simulation experiment results show that the algorithm can also solve the optimal result in the 3D path planning optimization problem of UAVs.

Key words: hunter-prey optimization algorithm(HPO), differential evolution, high dimensional optimization, multi-strategy, path planning

中图分类号:  TP301.6

[1] HUANG C W, LI Y X, YAO X. A survey of automatic parameter tuning methods for metaheuristics[J]. IEEE Transactions on Evolutionary Computation, 2020, 24(2): 201-216. DOI: 10.1109/TEVC.2019.2921598.
[2] 王鹤静, 王丽娜. 机器人路径规划算法综述[J]. 桂林理工大学学报, 2023, 43(1): 137-147. DOI: 10.3969/j.issn.1674-9057.2023.01.017.
[3] 汪涛, 林川, 郭生伟. 基于混合策略改进鲸鱼优化算法的模糊时间序列模型[J]. 电子设计工程, 2023, 31(15): 98-106. DOI: 10.14022/j.issn1674-6236.2023.15.021.
[4] 符强, 孔健明, 纪元法, 等. 基于改进粒子群优化PID的双补偿时钟同步算法[J]. 桂林电子科技大学学报, 2023, 43(1): 27-34. DOI: 10.16725/j.cnki.cn45-1351/tn.2023.01.011.
[5] WOLPERT D H, MACREADY W G. No free lunch theorems for optimization[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67-82. DOI: 10.1109/4235.585893.
[6] 李洋州, 顾磊. 基于曲线自适应和模拟退火的蝗虫优化算法[J]. 计算机应用研究, 2019, 36(12): 3637-3643. DOI: 10.19734/j.issn.1001-3695.2018.07.0580.
[7] 李大海, 詹美欣, 王振东. 求解多峰目标函数的改进阴阳对算法[J]. 计算机应用研究, 2022, 39(5): 1402-1409. DOI: 10.19734/j.issn.1001-3695.2021.11.0465.
[8] 逯苗, 何登旭, 曲良东. 非线性参数的精英学习灰狼优化算法[J]. 广西师范大学学报(自然科学版), 2021, 39(4): 55-67. DOI: 10.16088/j.issn.1001-6600.2020093002.
[9] 王钦甜, 沈艳军. 多阶段的郊狼优化算法[J]. 广西师范大学学报(自然科学版), 2023, 41(3): 105-117. DOI: 10.16088/j.issn.1001-6600.2022110604.
[10] 王喜敏, 袁杰, 寇巧媛. 一种基于多策略的改进黏菌算法[J]. 广西师范大学学报(自然科学版), 2022, 40(6): 98-108. DOI: 10.16088/j.issn.1001-6600.2021122104.
[11] PERAZA-VÁZQUEZ H, PEÑA-DELGADO A F, ECHAVARRÍA-CASTILLO G, et al. A bio-inspired method for engineering design optimization inspired by dingoes hunting strategies[J]. Mathematical Problems in Engineering, 2021, 2021: 9107547. DOI: 10.1155/2021/9107547.
[12] NARUEI I, KEYNIA F. Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems[J]. Engineering with Computers, 2022, 38(Suppl 4): 3025-3056. DOI: 10.1007/s00366-021-01438-z.
[13] ALSATTAR H A, ZAIDAN A A, ZAIDAN B B. Novel meta-heuristic bald eagle search optimisation algorithm[J]. Artificial Intelligence Review, 2020, 53(3): 2237-2264. DOI: 10.1007/s10462-019-09732-5.
[14] NARUEI I, KEYNIA F, SABBAGH MOLAHOSSEINI A. Hunter-prey optimization: algorithm and applications[J]. Soft Computing, 2022, 26(3): 1279-1314. DOI: 10.1007/s00500-021-06401-0.
[15] ELSHAHED M, EL-RIFAIE A M, TOLBA M A, et al. An innovative hunter-prey-based optimization for electrically based single-, double-, and triple-diode models of solar photovoltaic systems[J]. Mathematics, 2022, 10(23): 4625. DOI: 10.3390/math10234625.
[16] RAMADAN H A, KHAN B, DIAB A A Z. Accurate parameters estimation of three diode model of photovoltaic modules using hunter-prey and wild horse optimizers[J]. IEEE Access, 2022, 10: 87435-87453. DOI: 10.1109/ACCESS.2022.3199001.
[17] INKOLLU S R, ANJANEYULU G V P, KOTAIAH N C, et al. An application of hunter-prey optimization for maximizing photovoltaic hosting capacity along with multi-objective optimization in radial distribution network[J]. International Journal of Intelligent Engineering & Systems, 2022, 15(4): 575-584. DOI: 10.22266/ijies2022.0831.52.
[18] SHAHEEN A M, EL-SEHIEMY R A, GINIDI A, et al. Optimal allocation of PV-STATCOM devices in distribution systems for energy losses minimization and voltage profile improvement via hunter-prey-based algorithm[J]. Energies, 2023, 16(6): 2790. DOI: 10.3390/en16062790.
[19] ALSHAHRANI H J, HASSAN A Q A, TARMISSI K, et al. Hunter prey optimization with hybrid deep learning for fake news detection on Arabic corpus[J]. Computers, Materials & Continua, 2023, 75(2): 4255-4272. DOI: 10.32604/cmc.2023.034821.
[20] XIANG C Y, GU J F, LUO J, et al. Structural damage identification based on convolutional neural networks and improved hunter-prey optimization algorithm[J]. Buildings, 2022, 12(9): 1324. DOI: 10.3390/buildings12091324.
[21] MA J, LIU F M. Bearing fault diagnosis with variable speed based on fractional hierarchical range entropy and hunter-prey optimization algorithm-optimized random forest[J]. Machines, 2022, 10(9): 763. DOI: 10.3390/machines10090763.
[22] 王芬, 杨媛. 基于猎人猎物优化算法求解TSP问题[J]. 宁夏师范学院学报, 2022, 43(7): 59-63, 71. DOI: 10.3969/j.issn.1674-1331.2022.07.008.
[23] FU M X, LIU Q. An improved hunter-prey optimization algorithm and its application[C] //2022 IEEE International Conference on Networking, Sensing and Control (ICNSC). Piscataway, NJ: IEEE, 2022: 1-7. DOI: 10.1109/ICNSC55942.2022.10004114.
[24] ABDELATY A M, YOUSRI D, CHELLOUG S, et al. Fractional order adaptive hunter-prey optimizer for feature selection[J]. Alexandria Engineering Journal, 2023, 75: 531-547. DOI: 10.1016/j.aej.2023.05.092.
[25] KHADIDOS A O, ALKUBAISY Z M, KHADIDOS A O, et al. Binary hunter-prey optimization with machine learning: based cybersecurity solution on internet of things environment[J]. Sensors, 2023, 23(16): 7207. DOI: 10.3390/s23167207.
[26] HASSAN M H, DAQAQ F, KAMEL S, et al. An enhanced hunter-prey optimization for optimal power flow with FACTS devices and wind power integration[J]. IET Generation, Transmission & Distribution, 2023, 17(14): 3115-3139. DOI: 10.1049/gtd2.12879.
[27] 常耀华, 韦根原. 基于领导者竞争策略的改进猎人猎物优化算法[J]. 计算机应用研究, 2024, 41(1): 142-149. DOI: 10.19734/j.issn.1001-3695.2023.05.0222.
[28] 鲁英达,张菁.基于改进猎人猎物算法的VMD-KELM短期负荷预测[J]. 电气工程学报, 2023, 18(4): 228-238. DOI: 10.11985/2023.04.025.
[29] 华罗庚, 王元. 数论在近代分析中的应用[M]. 北京: 科学出版社, 1978: 1-99.
[30] HARIFI S, MOHAMMADZADEH J, KHALILIAN M, et al. Giza pyramids construction: an ancient-inspired metaheuristic algorithm for optimization[J]. Evolutionary Itelligence, 2021, 14(4): 1743-1761. DOI: 10.1007/s12065-020-00451-3.
[31] FARAMARZI A, HEIDARINEJAD M, STEPHENS B, et al. Equilibrium optimizer: a novel optimization algorithm[J]. Knowledge-Based Systems, 2020, 191: 105190. DOI: 10.1016/j.knosys.2019.105190.
[32] 李克文, 梁永琪, 李绍辉. 基于混合策略改进的花朵授粉算法[J]. 计算机应用研究, 2022, 39(2): 361-366. DOI: 10.19734/j.issn.1001-3695.2021.08.0311.
[33] STORN R, PRICE K.Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of global optimization, 1997,11(4): 341-359. DOI: 10.1023/A:1008202821328.
[34] SAREMI S, MIRJALILI S, LEWIS A. Grasshopper optimisation algorithm: theory and application[J]. Advances in Engineering Software, 2017, 105: 30-47. DOI: 10.1016/j.advengsoft.2017.01.004.
[35] 李煜, 尚志勇, 刘景森. 求解函数优化问题的改进布谷鸟搜索算法[J]. 计算机科学, 2020, 47(1): 219-230. DOI: 10.11896/jsjkx.181102165.
[36] YAO X, LIU Y, LIN G M. Evolutionary programming made faster[J]. IEEE Transactions on Evolutionary Computation, 1999, 3(2): 82-102. DOI: 10.1109/4235.771163.
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