Evolutionary algorithms often struggle to find well converged (e.g small inverted generational distance on test problems) solutions to multi-objective optimization problems on a limited budget of function evaluations (here, a few hundred). The family of surrogate-assisted evolutionary algorithms (SAEAs) offers a potential solution to this shortcoming through the use of data driven models which augment evaluations of the objective functions. A surrogate model which has shown promise in single-objective optimization is to predict the "comparison relationship" between pairs of solutions (i.e. who's objective function is smaller). In this paper, we investigate the performance of this model on multi-objective optimization problems. First, we propose a new algorithm "CRSEA" which uses the comparison-relationship model. Numerical experiments are then performed with the DTLZ and WFG test suites plus a real-world problem from the field of accelerator physics. We find that CRSEA finds better converged solutions than the tested SAEAs on many of the medium-scale, biobjective problems chosen from the WFG suite suggesting the comparison-relationship surrogate as a promising tool for improving the efficiency of multi-objective optimization algorithms.
View on arXiv@article{pierce2025_2504.19411,
title={ A Comparison-Relationship-Surrogate Evolutionary Algorithm for Multi-Objective Optimization },
author={ Christopher M. Pierce and Young-Kee Kim and Ivan Bazarov },
journal={arXiv preprint arXiv:2504.19411},
year={ 2025 }
}