一个改进的FOX优化算法,使用适应式探索和开发来实现全球优化
Mahmood A Jumaah1, Yossra H Ali1, Tarik A Rashid2
1College of Computer Science, University of Technology, Baghdad, Iraq.
PloS one
|September 18, 2025
概括
改进的FOX (IFOX) 算法通过自适应地平衡勘探和开发来增强优化,在基准和现实世界问题上表现优于原始FOX算法和其他领先的方法.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超听证学是一种超听证学.
背景情况:
- 优化算法面临的挑战包括局部最小值和平衡勘探/开采.
- 现有的方法通常具有众多的超参数,使调整复杂化.
- 有效的优化对于各种现实应用程序至关重要.
研究的目的:
- 引入一个改进的优化算法,改进的FOX (IFOX).
- 通过完善其核心机制来提高FOX算法的性能.
- 解决当前优化技术的局限性.
主要方法:
- 开发了IFOX,具有一个新的适应性步骤大小参数,用于动态勘探/开采平衡.
- 通过删除四个参数 (C1,C2,a,Mint) 来减少超参数.
- 在20个经典,61个CEC基准函数和10个现实问题上测试了IFOX.
主要成果:
- 与原来的FOX算法相比,IFOX的整体性能提高了40%.
- 与其他16个优化算法 (880次胜利,228次平局,348次失利) 相比,实现了优越的性能.
- 统计测试证实了IFOX的竞争力,使用LSHADE和NRO等最先进的算法.
结论:
- IFOX是一个强大而有效的优化算法.
- 适应式步骤大小机制显著提高了性能.
- IFOX显示出解决复杂优化任务的巨大潜力.
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