解决基于 PF 形状分类和矢量角度选择的多目标优化问题
Y T Wu1, F Z Ge2,3, D B Chen2,4
1Computer Science and Technology, Huaibei Normal University, Huaibei, 340604, China 2679512854@qq.com.
Evolutionary computation
|March 17, 2025
概括
一个新的多目标优化算法 (MaOEA) 通过分类帕雷托前形和使用矢量角度进行选择来提高性能. 这增强了高维度问题的融合和多样性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 多目标优化 多目标优化
背景情况:
- 多目标优化算法 (MaOEAs) 在高维空间中与融合和多样性作斗争.
- 现有的MaOEA通常依赖于预先假定的帕雷托前端 (PF) 形状,导致性能不佳.
- 选择压力不足是多目标优化中的一个关键挑战.
研究的目的:
- 提出一个新的多目标优化算法,MaOEA-PV,解决现有方法的局限性.
- 在高维的客观空间中增强融合与多样性之间的平衡.
- 为了提高优化算法中的选择压力.
主要方法:
- 开发了一种新的巴雷托前 (PF) 形状分类方法.
- 引入了一个新的健身功能,整合了融合和多样性指标,以改善父母选择.
- 实施了一项策略,以选择高度融合的个体来加强人口质量.
- 采用最大-最小向量角度策略,以平衡解决方案选择中的收和多样性.
主要成果:
- 与五个最先进的MaOEA相比,MaOEA-PV表现出了竞争力和有效的表现.
- 根据41个测试问题和5个现实问题进行评估,最多有15个目标.
- 拟议的算法成功地克服了环境选择中缺乏选择压力的缺点.
结论:
- 拟议的MaOEA-PV通过PF形状分类和矢量角度选择有效平衡融合和多样性.
- 该算法显示了多目标优化问题的性能显著改善.
- 马奥EA-PV为解决复杂的高维度优化挑战提供了一种有前途的方法.
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