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• W2891428731 abstract "Classical Pareto-dominance based multi-objective evolutionary algorithms underperform when applied to optimization problems with more than three objectives. A class of multi-objective evolutionary algorithms introduced in the literature, utilizing pre-determined reference points acting as target vectors to maintain diversity in the objective space, has shown promising results. Inspired by this approach, we propose a scalarization-based dominance evolutionary algorithm (SDEA) that utilizes a reference point-based method and combine it with a novel sorting strategy that employs fitness values determined via scalarization. SDEA reduces computation complexity by eliminating the need for a Pareto-dominance approach to obtain non-dominated solutions. By means of a set of common benchmark optimization problems with 3- to 15-objectives, we compare the performance of SDEA with state-of-the-art many-objective evolutionary algorithms. The results indicate that SDEA outperforms existing algorithms in undertaking complex optimization problems with a high number of objectives, and has comparable outcomes over low-dimensional objective space benchmark problems." @default.
• W2891428731 created "2018-09-27" @default.
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• W2891428731 date "2019-02-01" @default.
• W2891428731 modified "2023-10-12" @default.
• W2891428731 title "A scalarization-based dominance evolutionary algorithm for many-objective optimization" @default.
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• W2891428731 doi "https://doi.org/10.1016/j.ins.2018.09.031" @default.
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