适应性捕食者猎物算法用于许多目标优化
Nikunj Mashru1, Kanak Kalita2,3, Lenka Čepová4
1Department of Mechanical Engineering, Marwadi University, Rajkot, 360003, India.
Scientific reports
|April 12, 2025
概括
多目标海洋捕食者算法 (MaOMPA) 有效地平衡了复杂的工程优化问题的解决方案多样性和融合. 这种新的方法增强了传统方法,在高维,多目标场景中提供了卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法优化算法
- 工程设计工程设计工程设计
背景情况:
- 多目标优化在平衡解决方案多样性和融合方面提出了挑战,特别是在高维空间.
- 现有的元启发方法经常在现实世界工程问题中与复杂的,相互矛盾的目标作斗争.
研究的目的:
- 引入多目标海洋捕食者算法 (MaOMPA),这是海洋捕食者算法 (MPA) 的增强版本.
- 在多目标优化任务中解决传统算法的局限性.
主要方法:
- 马奥姆帕集成了精英主义,非主导分类和拥挤距离机制.
- 它使用捕食者-猎物交互模型来平衡探索和剥削.
- 该算法在基准测试函数和现实世界的工程问题上进行了评估.
主要成果:
- 与最先进的算法相比,MaOMPA表现出更高的性能.
- 评估使用了代际距离,超量和覆盖范围的指标.
- 该算法在勘探和开采之间实现了稳健的平衡.
结论:
- 对于工程师和研究人员来说,MaOMPA是一个有效可靠的工具,可以解决复杂的优化问题.
- 拟议的算法在处理多目标优化场景方面取得了显著的改进.
- 源代码是公开可用的,用于进一步的研究和应用.
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