一个基于双关联的进化算法,用于多目标优化
Junhua Liu1, Wei Zhang2, Mengnan Tian1
1The Shaanxi Key Laboratory of Clothing Intelligence, School of Computer Science, Xi'an Polytechinic University, Xi'an 710048, China.
Mathematical biosciences and engineering : MBE
|November 3, 2023
概括
一个新的基于双关联的进化算法 (DAEA) 有效地解决了多目标优化问题. 它通过考虑空子空间和完善多样性测量来提高探索和解决方案质量.
科学领域:
- 计算智能是一种计算智能.
- 优化算法优化算法
- 进化计算的演变
背景情况:
- 多目标优化问题 (MaOPs) 提出了重要的计算挑战.
- 现有的进化算法在高维客观空间中难以保持多样性和融合.
研究的目的:
- 提出一种基于双关联的新型进化算法 (DAEA) 来解决MaOPs.
- 在多目标优化中增强勘探能力和解决方案质量评估.
主要方法:
- 引入双重关联战略,将解决方案与子空间 (包括空的子空间) 联系起来,以促进探索.
- 制定精细的质量评估方案,衡量融合,全球多样性和当地多样性.
- 实施动态惩罚系数以平衡融合和多样性.
主要成果:
- 在对比20个目标的MaOPs上,DAEA表现出高竞争力,与5个最先进的算法对比.
- 拟议的双重关联战略改善了未知的地区的探索.
- 改进后的质量评估方案提供了更细致的解决方案质量的评估.
结论:
- DAEA提供了一种有前途的方法,可以有效地解决多目标优化问题.
- 这些新的策略增强了算法找到多样化和良好融合的解决方案的能力.
- DAEA代表了MaOPs进化计算领域的重大进步.
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