一个基于双选择策略的多目标进化算法
Cheng Peng1, Cai Dai1, Xingsi Xue2
1School of Computer Science, Shaanxi Normal University, Xi'an 710119, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种具有双重选择策略 (MaOEA/DS) 的多目标进化算法,以改善高维优化的融合和多样性. 而MAOEA/DS有效地平衡了这些因素,超过了现有的算法.
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:
- 在高维空间中的多目标优化问题 (MaOPs) 挑战了现有的算法.
- 由于目标增加,平衡融合和多样性是很困难的.
- 区分非主导解决方案和评估多样性变得有问题.
研究的目的:
- 提出一个新的多目标进化算法,MaOEA/DS,以解决融合-多样性平衡.
- 增强区分优质解决方案和改善人口多样性的能力.
- 在多目标优化中减少选择压力.
主要方法:
- 开发了一个新的距离函数,用于有效的距离度量计算.
- 引入了一个点拥挤度 (PC) 策略,用于优质解决方案的识别.
- 实施了一种双重选择策略,首先关注融合,然后关注多样性.
主要成果:
- 与最先进的算法相比,MaOEA/DS表现出优越的整体性能.
- 实验结果验证了拟议的双重选择策略的有效性.
- 该PC策略有效地提高了优质解决方案的区分.
结论:
- 拟议的MAOEA/DS有效地平衡了多目标优化中的融合和多样性.
- 新的距离功能和PC策略有助于提高性能.
- MAOEA/DS提供了一种有希望的方法来应对高维度优化挑战.
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