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

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.

Automated summary

This paper proposes a generic framework for expensive many-objective optimization using a Pareto-based bi-indicator infill sampling criterion. Empirical studies demonstrate the effectiveness and superiority of this framework and the incorporated algorithm in handling DTLZ problems with more than three objectives.