基于竞争机制的多目标正弦共弦算法及其在工程设计问题中的应用
Nengxian Liu1, Jeng-Shyang Pan2, Genggeng Liu1
1College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China.
Biomimetics (Basel, Switzerland)
|February 23, 2024
概括
一个新的竞争机制多目标正弦共弦算法 (CMOSCA) 改善了多目标优化问题 (MOP) 的融合和多样性. 这种算法增强了进化策略,以获得更好的现实世界工程设计解决方案.
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:
- 多目标优化问题 (MOP) 在现实世界的场景中很普遍.
- 现有的多目标进化算法 (MOEA) 在平衡非主导解决方案的融合和多样性方面面临挑战.
研究的目的:
- 提出一个具有竞争机制的高效多目标正弦共弦算法 (CMOSCA),以解决MOP中的融合-多样性权衡.
主要方法:
- 在CMOSCA中,使用非主导分类和拥挤距离来对代理进行排名和选择.
- 一个新的位置更新运营商是使用基于基于轮班密度估计的竞争机制开发的.
- 优秀的代理商指导了演变过程,竞争中获胜者被整合到位置更新计划中.
主要成果:
- 在DTLZ,WFG和ZDT基准套件上,CMOSCA表现出卓越的表现,实现了更好的融合和多样性.
- 统计结果证实了CMOSCA在工程设计问题上应用时的效率和有效性.
结论:
- 拟议的CMOSCA有效地平衡了融合和多样性,以实现多目标优化.
- CMOSCA为解决复杂的工程设计挑战提供了一种有希望的方法.
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