一个新的多目标优化算法,用于解决优化问题
Divya Adalja1, Pinank Patel2, Nikunj Mashru3
1Department of Mathematics, Marwadi University, Rajkot, 360003, India. pateldivya91@gmail.com.
Scientific reports
|April 24, 2025
概括
一个新的多目标猪优化 (MOCPO) 算法增强了复杂的问题解决能力. 它改善了多目标优化任务的融合和解决方案多样性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 进化计算是一种进化计算.
背景情况:
- 多目标优化问题 (MOP) 涉及平衡相互矛盾的目标.
- 现有的算法在维持解决方案多样性和融合方面面临着挑战.
- 猪的行为激发了新的优化方法.
研究的目的:
- 介绍多目标尾猪优化 (MOCPO) 算法.
- 在多目标优化中加强融合和多样性控制.
- 为复杂的工程设计和基准问题提供有效的工具.
主要方法:
- 基于Crested Porcupines算法开发了MOCPO,结合了精英,非主导分类和拥挤距离机制.
- 整合了一个新的信息反机制 (IFM) 和一个增强的解决方案更新策略.
- 在ZDT,DTLZ和RWMOP基准套件上评估MOCPO,与最先进的算法进行比较.
主要成果:
- MOCPO在融合速度和解决方案多样性方面取得了显著改善.
- 定性和定量分析证实了算法的有效性在不同类型的问题上.
- MOCPO的性能超过了MOGBO,Pre-DEMO,MOEDO,Pi-MOEA和ClGrMOEA等已建立的算法.
结论:
- MOCPO是一个强大而有效的算法,用于解决复杂的多目标优化问题.
- 算法的设计在融合和多样性方面都促进了卓越的性能.
- MOCPO为现实世界的工程设计挑战提供了可行和改进的替代方案.
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