A framework for expensive many-objective optimization with Pareto-based bi-indicator infill sampling criterion

摘要

Surrogate-assisted many-objective optimization is to locate Pareto optimal solutions using a limited number of function evaluations. Most existing surrogate-assisted evolutionary algorithms are designed to embed in a specific many-objective evolutionary algorithm. The Pareto-based bi-indicator infill sampling criterion has been proven to be effective in saving expensive evaluations in surrogate-assisted multi-objective evolutionary algorithms. It introduces two indicators measuring the convergence and diversity as two optimization objectives. In this paper, we extend our previous work to propose a generic framework with a Pareto-based bi-indicator infill sampling criterion for expensive many-objective optimization. The proposed framework gives a general method for traditional MOEAs to solve the expensive optimization task. We incorporate the proposed framework into the reference vectors guided evolutionary algorithm and compare it with another surrogate-assisted reference vectors guided evolutionary algorithm. Empirical studies on DTLZ problems with more than three objectives demonstrate the effectiveness and superiority of the proposed framework and the incorporated algorithm.

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