多目标粒子群集优化与反向多领导者
Fei Chen1, Yanmin Liu2, Jie Yang2
1School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China.
Mathematical biosciences and engineering : MBE
|July 28, 2023
概括
一个新的算法,反向多领导者多目标粒子群集优化 (RMMOPSO),增强了融合和多样性. 它使用新的策略来引导粒子,比现有方法更好地实现性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 多目标粒子群优化 (MOPSO) 算法在平衡解决方案的融合和多样性方面面临着挑战.
- 现有的MOPSO方法需要进一步改进以获得最佳性能.
研究的目的:
- 提出一种新的MOPSO算法,RMMOPSO,可以改善融合和多样性之间的平衡.
- 增强学习过程和引导粒子向真正的帕雷托前线.
主要方法:
- 开发了一个反向的多领导者多目标粒子群集优化 (RMMOPSO) 算法.
- 引入了使用全球排名的融合策略和基于平均角距离的多样性策略.
- 实施了选择两个全球领导人的反向选择方法和更新个人最佳的信息融合策略.
- 提出了一种新的粒子速度更新方法,以实现更好的融合-多样性平衡.
主要成果:
- 与最先进的MOPSO和多目标进化算法 (MOEA) 相比,RMMOPSO的综合性表现优越.
- 对22个基准问题的模拟验证了拟议策略的有效性.
结论:
- 在多目标优化中,RMMOPSO有效地平衡了融合和多样性.
- 拟议的反向多领导者方法和相关策略显著提高了算法性能.
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