High Dimensional Many Objective Optimisation through Diverse Creation and Categorisation of Reference Vectors
PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2023 COMPANION(2023)
摘要
Predefined reference vectors (RVs) are utilised in decomposition-based algorithms to solve many-objective optimisation problems (MaOPs). Two commonly used creation methods are those proposed by Das and Dennis and Deb and Jain. The former method generates many RVs at lower-order boundaries to create sufficient RVs in the intermediate area. The latter solves this challenge by adopting a layered creation of RVs but often fails in creating RVs on higher-order boundaries. To address these challenges, RVs in this study proposed to be created and categorised in clusters in the intermediate area of the m-dimensional space and the boundaries of various kinds independently. Diverse combinations of RV groups, with a manageable number of vectors each, can be utilised in optimisation. Pareto front in various subregions can be investigated with the desired number of approximations. A separate analysis in each subregion can be performed if needed. The approximations can be combined to form the final PF. The method is readily implemented into existing decomposition-based MaOP algorithms.
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关键词
Subregional Pareto-optimal solution,subdomains,reference points,reference vectors,weight vectors,Many Objective Optimisation (MaOP)
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