Adaptive Switching Strategy for Metamodeling Based Multi-objective Optimization : Part I , Generative Frameworks

Evaluating computationally expensive objective and constraint functions is one of the main challenges faced when solving real-world optimization problems. For handling such problems, it is common to use a metamodeling approach. Metamodels for objectives and constraints are initially formed using a few high-fidelity solution evaluations. Then, the metamodels are optimized to find a set of in-fill solutions. New metamodels are then formed by including high-fidelity evaluations of in-fill solutions to the initial set. This procedure is continued in a progressive manner until a predetermined budget of solutions are evaluated. A recent study has provided a taxonomy of 10 different frameworks for forming metamodels of objective and constraint combinations. In this paper, we propose a novel adaptive method for switching among five different generative metamodeling frameworks and one simultaneous framework in multiple epochs. Statistical tests on multi-objective convergence and diversity-preservation metrics are made at the start of each epoch to determine one of the six frameworks which is most suitable at that instant. In the second part of this extensive research, we perform a similar study using the remaining five simultaneous frameworks, followed up by another study including all 10 frameworks. Results of this study clearly show the efficacy and efficiency of the proposed adaptive switching approach compared to past framework-wise studies and to three recentlyproposed other metamodeling algorithms on challenging multiobjective optimization problems using a limited budget of highfidelity evaluations. Keywords—Surrogate model, Metamodel, Evolutionary multiobjective optimization, Kriging, Taxonomy.

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