Addressing Heterogeneous Evaluation Times in Constrained Multi-Objective Optimization using a Mixed-Fidelity Evaluation Technique: Proof-of-Concept Results
Abstract
Most practical optimization problems involve expensive evaluation procedures for computing objective and constraint functions. To obtain reasonable and accurate solutions close to true Pareto-optimal solutions, evolutionary multi-objective optimization (EMO) algorithms create surrogate models from already-evaluated high-fidelity solutions and use them during optimization to save computational time. However, most surrogate-assisted EMO algorithms are designed to evaluate all objectives and constraints of a solution, if found worthy of a high-fidelity evaluation. Such algorithms are inefficient if objectives and constraints involve heterogeneity with orders of magnitude of difference in evaluation times. Clearly, functions with relatively small evaluation time can be high-fidelity evaluated more often to obtain an overall idea of the potential importance of the solution before deciding to spend more time on evaluating expensive functions. In this paper, we propose an EMO approach that carefully determines which constraints and objectives should be high-fidelity evaluated for every population member and suggests a mixed-fidelity survival selection procedure capable of working with low- and high-fidelity evaluated population members. Results on a number of test and engineering problems indicate the viability of such a constrained multi- and many-objective optimization algorithm and encourage further attention.
Authors: Balija Santoshkumar, Kalyanmoy Deb
Published in: Genetic and Evolutionary Computation Conference (GECCO) (2025)