邻域搜索和改进莱维因子的人工蜂鸟优化算法
Neighborhood Search and Artificial Hummingbird Optimization Algorithms with Improved Lévy Factors
DOI: 10.12677/MOS.2024.132095, PDF,  被引量    科研立项经费支持
作者: 何永康, 李旭芳：上海工程技术大学管理学院，上海
关键词: 邻域搜索；莱维飞行；人工蜂鸟算法；Neighborhood Search； Lévy Flight； Artificial Hummingbird Algorithm
摘要: 针对人工蜂鸟算法(AHA)寻优精度较低、易陷入局部最优等问题，提出新的基于自适应距离围猎和改进莱维因子的人工蜂鸟优化算法(ALAHA)。首先，在AHA引导觅食、领地觅食阶段引入改进的莱维飞行作为自适应权重因子调节搜索步长，提高种群全局搜索能力；然后，根据种群收敛情况，在蜂鸟个体周围进行自适应距离围猎搜索，提高算法搜索精度。本文选取了23个基准测试函数对算法进行了实验，并与其他算法进行了比较，以不同角度对于算法的性能进行测试，并使用Wilcoxon秩和检验来证明算法的性能，结果表明了ALAHA算法改进在寻优能力、稳定性和鲁棒性等方面有所提升。
Abstract: Aiming at the problems of low search accuracy and easy to fall into local optimization of artificial hummingbird algorithm (AHA), a new artificial hummingbird optimization algorithm (ALAHA) based on adaptive distance rounding and improved Lévy factor is proposed. First, the improved Lé-vy flight is introduced as an adaptive weighting factor to regulate the search step in the AHA guided foraging and territorial foraging phases, which improves the global search ability of the population; then, according to the convergence of the population, adaptive distance hunting search is per-formed around the individual hummingbird to improve the algorithm’s search accuracy. In this paper, 23 benchmark test functions were selected to experiment the algorithm and compared with other algorithms to test the performance of the algorithm from different perspectives, and the Wil-coxon rank-sum test was used to prove the performance of the algorithm, and the results showed that the ALAHA algorithm improved in terms of optimization ability, stability and robustness.
文章引用：何永康, 李旭芳. 邻域搜索和改进莱维因子的人工蜂鸟优化算法[J]. 建模与仿真, 2024, 13(2): 987-1003. https://doi.org/10.12677/MOS.2024.132095

参考文献

[1] Shefaei, A., Vahid-Pakdel, M.J. and Mohammadi-Ivatloo, B. (2018) Application of a Hybrid Evolutionary Algorithm on Reac-tive Power Compensation Problem of Distribution Network. Computers & Electrical Engineering, 72, 125- 136. [Google Scholar] [CrossRef] 
[2] Kennedy, J. and Eberhart, R. (1995) Particle Swarms Optimiza-tion. Proceedings of ICNN’95—International Conference on Neural Networks, Perth, 27 November -1 December 1995, 1942-1948.
[3] Dorigo, M. and Di Caro, G. (1999) Ant Colony Optimization: A New Meta-Heuristic. Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), Washington DC, 6-9 July 1999, 1470-1477.
[4] Weiguo, Z., Liying, W. and Seyedali, M. (2022) Artificial Hummingbird Algorithm: A New Bio-Inspired Op-timizer with Its Engineering Applications. Computer Methods in Applied Mechanics and Engineering, 388, Article ID: 114194.
[5] Mirjalili, S. and Lewis, A. (2016) The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67. [Google Scholar] [CrossRef] 
[6] Assien, E., Masadeh, R. and Alzaqebah, A. (2017) Grey Wolf Op-timization Applied to the 0/1 Knapsack Problem. International Journal of Computer Applications, 169, 11-15. [Google Scholar] [CrossRef] 
[7] Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., et al. (2017) Salp Swarm Algo-rithm: A Bio-Inspired Optimizer for Engineering Design Problems. Advances in Engineering Software, 114, 163-191. [Google Scholar] [CrossRef] 
[8] Eidari, A.A., Mirjalili, S., Faris, H., et al. (2019) Harris Hawks Optimization: Algorithm and Applications. Future generation Computer Systems, 97, 849-872. [Google Scholar] [CrossRef] 
[9] Rao, R.V., Savsani, V.J. and Vakharia, D.P. (2012) Teach-ing—Learning-Based Optimization: An Optimization Method for Continuous Non-Linear Large Scale Problems. Information Sciences, 183, 1-15. [Google Scholar] [CrossRef] 
[10] Li, S., Chen, H., Wang, M., Heidari, A.A. and Mirjalili, S. (2020) Slime Mould Algorithm: A New Method for Stochastic Optimization. Future Generation Computer Systems, 111, 300-323. [Google Scholar] [CrossRef] 
[11] Zhao, W., Zhang, Z. and Wang, L. (2020) Manta Ray Foraging Optimi-zation: An Effective Bio-Inspired Optimizer for Engineering Applications. Engineering Applications of Artificial Intelligence, 87, Article ID: 103300. [Google Scholar] [CrossRef] 
[12] Faramarzi, A., Heidarinejad, M., Stephens, B. and Mirjalili, S. (2020) Equilibrium Optimizer: A Novel Optimization Algorithm. Knowledge-Based Systems, 191, Article ID: 105190. [Google Scholar] [CrossRef] 
[13] Zhao, W.G., Wang, L.Y. and Mirjalili, S. (2022) Artificial Humming-bird Algorithm: A New Bio-Inspired Optimizer with Its Engineering Applications. Computer Methods in Applied Mechanics and Engineering, 388, Article ID: 114194. [Google Scholar] [CrossRef] 
[14] Mohamed, A.H., Ragab, A.E., Ahmed, R.G., Ehab, E., Abdullah, M.S., et al. (2022) Parameter Identification and State of Charge Estimation of Li-Ion Batteries Used in Electric Vehicles Using Artificial Hummingbird Optimizer. Journal of Energy Storage, 51, Article ID: 104535. [Google Scholar] [CrossRef] 
[15] Wang, M.X., Jiang, W., Ye, X.L., Wang, Z.W., Li, J. and Liu, Z.G. (2022) Optimal UAVs Placement for Localization Based on Artificial Hummingbird Algorithm. 2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Xi’an, 25-27 October 2022, 1-6.
[16] Abdelhady, R., Sa-lah, K., Mohamed, H.H., Emad, M.A. and Hany, M.H. (2022) Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm. Electronics, 11, Article 318. [Google Scholar] [CrossRef] 
[17] Mohamed, A.E., Abdelghani, D., Shaker, E., Alhassan, M. and Mo-hamed, M.G. (2022) AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification. Applied Sciences, 12, Article 9710. [Google Scholar] [CrossRef] 
[18] Yildiz, B.S., Mehta, P., Sait, S.M., et al. (2022) A New Hybrid Artificial Hummingbird-Simulated Annealing Algorithm to Solve Con-strained Mechanical Engineering Problems. Materials Testing, 64, 1043-1050. [Google Scholar] [CrossRef] 
[19] Zhao, W., Zhang, Z., Mirjalili, S., et al. (2022) An Effective Multi-Objective Artificial Hummingbird Algorithm with Dynamic Elimination-Based Crowding Distance for Solving Engineering Design Prob-lems. Computer Methods in Applied Mechanics and Engineering, 398, Article ID: 115223. [Google Scholar] [CrossRef] 
[20] Ali, M.A.S., Fathimathul Rajeena, P.P. and Salama Abd Elminaam, D. (2022) A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem. Mathematics, 10, Article 2675. [Google Scholar] [CrossRef] 
[21] Yang, Q., Chen, W.N., Li, Y., et al. (2017) Multimodal Estimation of Distri-bution Algorithms. IEEE Transactions on Cybernetics, 47, 636-650. [Google Scholar] [CrossRef] 
[22] 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.

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