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nsga-iii进化多目标优化算法采用基于参考点的非支配排序方法

于 2022-10-09 发布 文件大小:42.58 kB
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开发的多目标优化算法采用进化优化方法和证明自己的优势在各种涉及实际问题主要是两个和三个目标,现在有一个不断增长的需求发展进化多目标优化(EMO)用于处理许多目标的算法(具有4或更多个目标)优化问题。在本文中,我们认识到一个最近几年的努力,并讨论了许多的可行方向发展潜力EMO算法求解多目标优化问题。此后,我们建议的参考点基于许多目标NSGA-II(我们称之为NSGA-III),强调人口会员,非主导且靠近一组提供的参考点。建议NSGA-III是施加到若干许多目标测试问题具有两个到15的目标,并与两个版本的一个最近比较建议EMO算法(MOEA/ D)。而每两个MOEA/ D的方法效果很好对不同类别的问题,所提出的NSGA-III被发现,以产生令人满意的结果在这项研究中考虑了所有的问题。本文介绍结果在无约束的问题,续集本文认为限制,在处理其他专科许多目标优化问题。

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