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Stepwise sampling coevolutionary framework for expensive constrained multiobjective optimization problems
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DOI：10.1016/j.asoc.2025.114408.png)
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Abstract
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• This paper proposes a stepwise sampling coevolutionary framework (SSCF), integrating a fuzzy clustering-based Kriging modeling (FuzzyCM) method with the coevolutionary framework to form an optimizer to solve expensive constraint multiobjective optimization problems (CMOPs). The FuzzyCM method can fully use all samples in the training set to construct Kriging models without increasing the computational costs. • A new stepwise sampling strategy is adopted in SSCF. This strategy comprehensively takes into account the requirements of convergence and feasibility. Besides selecting solutions that perform well in the objective space, it also considers selecting samples near the constraint boundaries. • This work conducts a parametric study on SSCF to analyze the influence of a specific parameter on its performance and provides appropriate recommendations for setting parameters.
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