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基于性别差异的多重学习能力的火算法
Wenning Zhang1, Chongyang Jiao2, Qinglei Zhou3
1Zhongyuan University of Technology, Zhengzhou, 450000, China. zwn@zut.edu.cn.
Scientific reports
|August 4, 2025
概括
基于性别差异 (MLFA-GD) 的多重学习能力的新火算法提高了优化精度. 这种改进的算法平衡了探索和利用,以获得更好的搜索功能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 标准的火算法 (FA) 面临着诸如搜索振荡和有限的收精度等挑战.
- 现有的FA变种往往难以有效地平衡复杂的优化任务的勘探和开发.
研究的目的:
- 引入一种基于性别差异 (MLFA-GD) 的多重学习能力的新火算法.
- 为了解决传统的FA的局限性,特别是搜索振荡和低收精度.
主要方法:
- MLFA-GD算法将人口分为男性和女性子组.
- 它采用不同的学习策略:雄性使用带有逃生机制的部分吸引模式,而雌性则由雄性中位素和全球最佳指导.
- 整合了一个随机步行策略,以改进优化准确度.
主要成果:
- 对23个数值函数和30个CEC 2017基准函数的实验表明了卓越的性能.
- 与6个FA变体和10个元启发算法的比较显示了增强的搜索能力和更高的优化精度.
- MLFA-GD有效地平衡了勘探和开采.
结论:
- 拟议的MLFA-GD显著改善了现有的火算法变体.
- 基于性别的学习策略和随机步行增强了探索和开发能力.
- 对于复杂的问题,MLFA-GD提供了更精确,更有效的优化解决方案.
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