一个改进的差异演化算法,用于多模式多目标优化
Dan Qu1,2, Hualin Xiao1, Huafei Chen2
1College of Mathematics Education, China West Normal University, Nanchong, China.
PeerJ. Computer science
|April 25, 2024
概括
一个新的多模多目标差异进化算法与亲和传播集群 (MMODE_AP) 有效地识别了多个帕雷托最佳集. 这种方法增强了复杂的优化问题的解决方案分布和融合.
科学领域:
- 优化算法 优化算法
- 计算智能是一种计算智能.
- 多目标优化 多目标优化
背景情况:
- 多模式多目标问题 (MMOPs) 具有多个帕雷托最佳集 (PSs),对融合和多样性构成挑战.
- 现有的多模式多目标差异演化 (MMODE) 算法在有效地识别和维护所有PS的多样化解决方案方面扎.
研究的目的:
- 引入一种新的MMODE算法,包括亲和传播集群 (APC),以提高MMOP的性能.
- 增强MMODE_AP算法在全球和本地帕雷托前线的融合能力,同时确保分布良好的解决方案.
主要方法:
- 通过集成APC来开发MMODE_AP,以定义决策和目标空间中的拥挤度.
- 采用适应性突变策略来平衡探索和开发,改善进化过程的多样性.
- 采用了修改后的非主导分类方案,并使用拥挤距离来进行种群截断和溶液分配.
主要成果:
- 与现有的MMODE算法相比,MMODE_AP在CEC'2020基准函数上表现出优异的表现.
- 在帕雷托集近距离 (rPSP) 和逆代距离 (IGD) 的反向值方面获得了大约20%的更好的结果.
- 通过分布良好的解决方案,展示了真正的本地和全球帕雷托前线的高效融合.
结论:
- 拟议的MMODE_AP算法有效地解决了多模式多目标优化的挑战.
- 集成APC显著改善了在多个帕雷托最佳集中的解决方案的识别和分布.
- 在复杂的优化场景中,MMODE_AP提供了一种强大的方法来实现融合和多样性.
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