全局优化的多策略蜜獾算法。

IF 3.9 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Delong Guo, Huajuan Huang
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引用次数: 0

摘要

蜜獾算法(Honey Badger Algorithm, HBA)是受蜜獾觅食行为启发而提出的一种元启发式优化算法。该算法的搜索机制分为挖掘阶段和寻蜜阶段,有效地模拟了搜索空间内的探索和开发过程。尽管采用了创新的方法,但Honey Badger Algorithm (HBA)仍面临着一些挑战,如收敛速度慢、勘探和开采之间的权衡不平衡,以及容易陷入局部最优状态。为了解决这些问题,我们提出了蜜獾算法(HBA)的增强版本,即多策略蜜獾算法(MSHBA),它包含了用于种群初始化的三次混沌映射机制。这种整合旨在增强初始种群分布的均匀性和多样性。在采蜜和寻蜜阶段,蜜獾的位置根据种群内的最佳适应度值进行更新。由于种群聚集在最适合的个体周围,这种策略可能导致过早收敛。为了消除这种倾向并增强算法的全局优化能力,我们引入了随机搜索策略。此外,在三次迭代后,采用精英切向搜索和差分突变策略,而不会在种群中检测到新的最优值,从而提高了算法的有效性。在一套已建立的基准功能中进行的综合性能评估显示,MSHBA在29个IEEE CEC 2017基准中的26个中表现出色。随后的统计分析证实了MSHBA的优越性能。此外,MSHBA已成功应用于四个工程设计问题,突出了其解决受限工程设计挑战的能力,并且优于该领域的其他优化算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Strategy Honey Badger Algorithm for Global Optimization.

The Honey Badger Algorithm (HBA) is a recently proposed metaheuristic optimization algorithm inspired by the foraging behavior of honey badgers. The search mechanism of this algorithm is divided into two phases: a mining phase and a honey-seeking phase, effectively emulating the processes of exploration and exploitation within the search space. Despite its innovative approach, the Honey Badger Algorithm (HBA) faces challenges such as slow convergence rates, an imbalanced trade-off between exploration and exploitation, and a tendency to become trapped in local optima. To address these issues, we propose an enhanced version of the Honey Badger Algorithm (HBA), namely the Multi-Strategy Honey Badger Algorithm (MSHBA), which incorporates a Cubic Chaotic Mapping mechanism for population initialization. This integration aims to enhance the uniformity and diversity of the initial population distribution. In the mining and honey-seeking stages, the position of the honey badger is updated based on the best fitness value within the population. This strategy may lead to premature convergence due to population aggregation around the fittest individual. To counteract this tendency and enhance the algorithm's global optimization capability, we introduce a random search strategy. Furthermore, an elite tangential search and a differential mutation strategy are employed after three iterations without detecting a new best value in the population, thereby enhancing the algorithm's efficacy. A comprehensive performance evaluation, conducted across a suite of established benchmark functions, reveals that the MSHBA excels in 26 out of 29 IEEE CEC 2017 benchmarks. Subsequent statistical analysis corroborates the superior performance of the MSHBA. Moreover, the MSHBA has been successfully applied to four engineering design problems, highlighting its capability for addressing constrained engineering design challenges and outperforming other optimization algorithms in this domain.

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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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