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<div><p>Large-scale many-objective optimization problems (LSMaOPs) are a current research hotspot. However, since LSMaOPs involves a large number of variables and objectives, state-of-the-art methods face a huge search space, which is difficult to be explored comprehensively. This paper...

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Main Author:	Xiaozhi Du (20890101) (author)
Other Authors:	Kai Chen (129938) (author), Hongyuan Du (12470842) (author), Zongbin Qiao (20890104) (author)
Published:	2025
Subjects:	Ecology Inorganic Chemistry Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified objective optimization problems huge search space current research hotspot art methods face xlink "> large since lsmaops involves population search strategy 2000 decision variables art moeas solving lsmaops significant performance second stage results show result shows real case paper proposes mainly manages mainly manage large number improved two first stage explored comprehensively experimentally compared efficiency advantages called two
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