多目标狮子优化器 (MaOALO):一个新的多目标优化器及其工程应用程序
Kanak Kalita1,2, Sundaram B Pandya3, Robert Čep4
1Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, 600 062, India.
Heliyon
|July 18, 2024
概括
一个新的多目标狮优化器 (MaOALO) 在复杂的工程问题中平衡了融合和多样性. 这种新的算法在广泛的基准测试中表现出比现有方法更好的性能.
科学领域:
- 工程优化工程优化
- 计算智能是一种计算智能.
背景情况:
- 多目标优化 (MaO) 在平衡融合和多样性方面提出了挑战.
- 现有的多目标优化算法 (MaOAs) 随着目标数量的增加而面临越来越大的复杂性.
研究的目的:
- 介绍一个新的多目标狮优化器 (MaOALO).
- 在多目标优化问题中增强融合和多样性.
主要方法:
- 在MaOALO集成的狮优化器与一个参考点,利基保护,和信息反机制 (IFM).
- 根据现实问题 (RWMaOP1-RWMaOP5) 和标准测试套件 (MaF1-MaF15,DTLZ1-DTLZ7) 进行评估.
主要成果:
- 与ARMOEA,NSGA-III,MaOTLBO,RVEA,MaOABC-TA,DSAE,RL-RVEA和MaOEA-IH相比,MaOALO显示出更优异的性能.与ARMOEA,NSGA-III,MaOTLBO,RVEA,MaOABC-TA,DSAE,RL-RVEA和MaOEA-IH相比,MaOALO显示出更优异的性能.与ARMOEA,NSGA-III,MaOTLBO,RVEA和MAOEA-TA相比,MaOALO显示出更优异的性能.
- 在包括GD,IGD,SP,SD,HV和RT在内的所有指标中表现出改善.
结论:
- 拟议的MaOALO有效地解决了多目标优化中的融合-多样性权衡.
- 对于解决复杂,多目标的工程挑战,MaOALO提供了一个有前途的进步.
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