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具有组合扰动策略的哈里斯鹰优化算法及其应用

Harris Hawk optimization algorithm with combined perturbation strategy and its application.

作者信息

Wang Zihe, Wei Xiaohui

机构信息

College of Computer Science and Technology, Jilin University, Changchun, 130012, China.

出版信息

Sci Rep. 2025 Jul 2;15(1):23372. doi: 10.1038/s41598-025-04705-x.

Abstract

Solving complex engineering optimization problems can improve design quality, reduce costs, and enhance performance and reliability. However, these problems are often nonlinear, non-convex, and multimodal. The Harris Hawk Optimization (HHO) algorithm has limitations such as poor exploration-exploitation balance, tendency to fall into local optima, slow convergence, and low accuracy. To address these issues, this paper proposes an improved HHO algorithm with a combined perturbation strategy (HHO-CPS). First, an adaptive oscillatory escape energy parameter E formula is introduced to better improve the balance between exploration and exploitation in HHO by dynamically adjusting the energy value from large to small. Second, the improved position update formulas for exploration and exploitation phases in HHO-CPS address the limited offspring distribution range and underutilization of elite individual information. They fully utilize elite information and expand offspring distribution, increasing the likelihood of capturing prey and generating promising solutions. Additionally, the combined perturbation strategy not only enhances population diversity but also improves the algorithm's convergence speed. Finally, the effectiveness of HHO-CPS is verified by comparing it with eleven other algorithms from the literature using CEC 2017 (30-D, 50-D), CEC 2022 (10-D, 20-D), and four real-world engineering optimization problems. The test results, Friedman rank analysis, and Friedman test demonstrate that HHO-CPS significantly outperforms the other eleven algorithms in terms of both performance and robustness, with substantial differences in algorithm performance. The experimental results fully validate the effectiveness and feasibility of the HHO-CPS algorithm. In summary, HHO-CPS demonstrates great potential in solving complex engineering optimization problems and has a broad application prospect, which will contribute to the optimization and innovation of engineering design.

摘要

解决复杂的工程优化问题可以提高设计质量、降低成本,并增强性能和可靠性。然而,这些问题往往是非线性、非凸和多模态的。哈里斯鹰优化(HHO)算法存在探索-利用平衡不佳、易陷入局部最优、收敛速度慢和精度低等局限性。为了解决这些问题,本文提出了一种具有组合扰动策略的改进HHO算法(HHO-CPS)。首先,引入了自适应振荡逃逸能量参数E公式,通过动态地将能量值从大到小调整,以更好地改善HHO中探索与利用之间的平衡。其次,HHO-CPS中探索和利用阶段的改进位置更新公式解决了后代分布范围有限和精英个体信息利用不足的问题。它们充分利用精英信息并扩大后代分布,增加捕获猎物和生成有前景解决方案的可能性。此外,组合扰动策略不仅增强了种群多样性,还提高了算法的收敛速度。最后,通过使用CEC 2017(30维、50维)、CEC 2022(10维、20维)以及四个实际工程优化问题,将HHO-CPS与文献中的其他十一种算法进行比较,验证了HHO-CPS的有效性。测试结果、弗里德曼秩分析和弗里德曼检验表明,HHO-CPS在性能和鲁棒性方面均显著优于其他十一种算法,算法性能存在实质性差异。实验结果充分验证了HHO-CPS算法的有效性和可行性。总之,HHO-CPS在解决复杂工程优化问题方面具有巨大潜力,具有广阔的应用前景,这将有助于工程设计的优化与创新。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ca/12222552/788620f7c036/41598_2025_4705_Fig1_HTML.jpg

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