Kei Nishihara
Kei Nishihara
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Scalarization Function
Evolutionary multiobjective optimization assisted by scalarization function approximation for high-dimensional expensive problems
This paper presents a novel surrogate-assisted evolutionary algorithm based on scalarization function approximation, which is designed to strengthen its robustness against this deterioration. The proposed algorithm, called SFA/DE, constructs an approximation model for each scalarization function defined in a decomposition-based framework. Each decomposed problem is then solved using multiple independent models trained for its neighbor problems.
Yuma Horaguchi
,
Kei Nishihara
,
Masaya Nakata
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