工程优化问题的改进多策略Aquila优化器。

IF 3.9 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Honglin Kan, Yaping Xiao, Zhiliang Gao, Xuan Zhang
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引用次数: 0

摘要

Aquila Optimizer (AO)是一种新颖高效的优化算法,其灵感来自于Aquila的狩猎和搜索行为。然而,在处理高维复杂的优化问题时,由于搜索能力不足和倾向于过早收敛到局部最优,使得算法的整体性能受到限制。为了应对这些挑战,本研究通过集成多种增强技术,提出了多策略Aquila优化器(MSAO)。首先,引入随机子维更新机制,显著增强了MSAO在高维空间的探测能力;其次,结合梦优化算法(DOA)中的记忆策略和梦共享策略,实现了全局探索和局部开发的平衡;此外,MSAO采用自适应参数和动态对立学习来进一步完善AO的原始更新规则,使其更适合多策略协作框架。在实验中,MSAO优于8种最先进的算法,包括CEC-winning和增强的AO变体,分别在55%、69%、69%和72%的基准函数上获得最佳优化结果,证明了其出色的性能。此外,烧蚀实验验证了每种策略的独立贡献,MSAO在五个工程问题中的应用证实了其强大的实用价值和广泛采用的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Multi-Strategy Aquila Optimizer for Engineering Optimization Problems.

The Aquila Optimizer (AO) is a novel and efficient optimization algorithm inspired by the hunting and searching behavior of Aquila. However, the AO faces limitations when tackling high-dimensional and complex optimization problems due to insufficient search capabilities and a tendency to prematurely converge to local optima, which restricts its overall performance. To address these challenges, this study proposes the Multi-Strategy Aquila Optimizer (MSAO) by integrating multiple enhancement techniques. Firstly, the MSAO introduces a random sub-dimension update mechanism, significantly enhancing its exploration capacity in high-dimensional spaces. Secondly, it incorporates memory strategy and dream-sharing strategy from the Dream Optimization Algorithm (DOA), thereby achieving a balance between global exploration and local exploitation. Additionally, the MSAO employs adaptive parameter and dynamic opposition-based learning to further refine the AO's original update rules, making them more suitable for a multi-strategy collaborative framework. In the experiment, the MSAO outperform eight state-of-the-art algorithms, including CEC-winning and enhanced AO variants, achieving the best optimization results on 55%, 69%, 69%, and 72% of the benchmark functions, respectively, which demonstrates its outstanding performance. Furthermore, ablation experiments validate the independent contributions of each proposed strategy, and the application of MSAO to five engineering problems confirms its strong practical value and potential for broader adoption.

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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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