将MOEA/D调整为CMA-ES,以应对无条件的多目标问题
Chengyu Lu1, Zhenhua Li2, Qingfu Zhang3
1Department of Computer Science, City University of Hong Kong, Hong Kong, China chengyulu3-c@my.cityu.edu.hk.
Evolutionary computation
|February 19, 2026
概括
一个新的算法,MOES/D,解决了进化多目标优化中的不良条件问题. 它有效地解决了不可分离和不良条件的问题,超过了现有的方法.
科学领域:
- 进化计算是一种进化计算.
- 多目标优化 多目标优化
- 算法设计 算法设计
背景情况:
- 不良条件问题在单一目标优化中带来了重大挑战.
- 这些挑战在进化型多目标优化 (EMO) 中基本上没有得到解决.
- 现有的EMO方法在整合它们时可能会损害核心进化战略特征.
研究的目的:
- 引入一种新的基于分解的多目标进化战略 (MOES/D).
- 解决不可分离且条件不良的多目标优化问题.
- 为协调进化算法制定量身定制的策略.
主要方法:
- 开发了MOES/D,一种基于分解的多目标进化策略.
- 实施了一种重要的混合算法,以获得无偏见的样本效率.
- 采用协作升级方法,同时优化子问题.
- 应用预期最大化原则性资源配置以优先考虑模型.
主要成果:
- MOES/D在中等和不良条件的多目标问题上表现出卓越的表现.
- 该算法显著超过了大多数最先进的算法.
- 实验是在一个新的基准套件的不可分离和不良条件的问题进行的.
结论:
- MOES/D有效地解决了具有挑战性的不可分割和不良条件的多目标问题.
- 拟议的定制策略提高了EMO.进化算法的效率和能力.
- 这项工作通过解决条件不良的实例,弥合了EMO研究中的关键差距.
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