一个基于分解的进化算法,邻近地区占主导地位
Hongfeng Ma1, Jiaxu Ning1, Jie Zheng1
1School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China.
Biomimetics (Basel, Switzerland)
|January 24, 2025
概括
基于分解的多目标进化算法 (MOEA/D) 得到了MOEA/D-NRD的改进. 这种新方法通过使用邻近区域主导来提高解决方案多样性和计算效率,以实现更快的融合.
科学领域:
- 计算智能是一种计算智能.
- 多目标优化多目标优化
- 进化算法是一种进化算法.
背景情况:
- 基于分解的多目标进化算法 (MOEA/D) 使用基于邻近的优化来解决子问题.
- 有限的多样性和不良的收性质源于MOEA/D.的仅邻近的比较.
- MOEA/D需要大量的人口代来实现质量解决方案,从而降低计算效率.
研究的目的:
- 提高基于分解的多目标优化算法的融合速度和计算效率.
- 引入一种新的方法,即MOEA/D-NRD,解决传统MOEA/D的局限性.
- 为了提高解决方案的多样性和质量,设置多目标进化算法.
主要方法:
- 建议MOEA/D-NRD,在MOEA/D框架内的增强算法.
- 实施邻近地区的统治,以确定解决方案的统治关系.
- 将后代的解决方案与邻里理想和最差的选择点进行比较.
主要成果:
- 与标准的MOEA/D-NRD相比,MOEA/D-NRD显示出加速的人口趋同.
- 该算法显示,由于更快的融合,计算效率提高了.
- 改进的选择策略导致解决方案集更有效地接近理想点.
结论:
- 能源部/DNRD有效地解决了能源部/DNRD的收和效率限制.
- 邻近地区的统治是改进多目标进化算法的可行策略.
- 提出的方法提供了一种更有效的方法来获得高质量的解决方案集在复杂的优化问题.
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