一个大规模的多目标进化算法,由缩量函数的低维替代品指导
Haoran Gu1, Handing Wang2, Cheng He3
1School of Artificial Intelligence, Xidian University, Xi'an 710071, China xdu_guhaoran@163.com.
Evolutionary computation
|June 18, 2024
概括
本研究引入了一个新的算法,用于大型昂贵的多目标优化问题. 它有效地处理使用低维替代模型的多达1000个决策变量的问题.
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:
- 代理辅助的多目标进化算法有效地解决计算密集的问题.
- 现有的算法由于不可靠的替代模型,与200多个决策变量作斗争.
- 大规模昂贵的多目标优化仍然是一个重大挑战.
研究的目的:
- 开发一种新的大型多目标进化算法.
- 为了应对现有方法的性能恶化,增加决策变量.
- 为了使高达1000个决策变量问题的高效优化.
主要方法:
- 提出了一个大规模的多目标进化算法 (LDS-AF).
- 利用了缩量函数的低维替代模型.
- 通过主要组件分析减少了决策空间的维度.
- 采用了两阶段建模和趋同控制策略.
主要成果:
- 在大规模的多目标优化问题上,LDS-AF表现出卓越的性能.
- 该算法成功处理了多达1000个决策变量的问题.
- 仅在500个实际功能评估中取得了有前途的结果.
- 保持了融合与多样性之间的平衡,避免过早的局部优化.
结论:
- 对于大规模问题,LDS-AF克服了现有算法的局限性.
- 拟议的方法为计算上昂贵的多目标优化提供了一种有效的方法.
- LDS-AF显示了解决具有高维度的复杂优化任务的巨大潜力.
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