A Comparison-Relationship-Surrogate Evolutionary Algorithm for Multi-Objective Optimization
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| Format: | Preprint |
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2025
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| _version_ | 1866913811995295744 |
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| author | Pierce, Christopher M. Kim, Young-Kee Bazarov, Ivan |
| author_facet | Pierce, Christopher M. Kim, Young-Kee Bazarov, Ivan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_19411 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comparison-Relationship-Surrogate Evolutionary Algorithm for Multi-Objective Optimization Pierce, Christopher M. Kim, Young-Kee Bazarov, Ivan Neural and Evolutionary Computing 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. |
| title | A Comparison-Relationship-Surrogate Evolutionary Algorithm for Multi-Objective Optimization |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2504.19411 |