Efficient three-stage surrogate-assisted differential evolution for expensive optimization problems
Article Type
Research Article
Publication Title
Swarm and Evolutionary Computation
Abstract
Surrogate-assisted evolutionary algorithms (SAEAs) have received significant acclaim for dealing with intricate and computationally demanding optimization problems. However, a prevalent challenge in many existing algorithms lies in their relatively sluggish convergence in the later stages of optimization. This study introduces an innovative three-stage surrogate-assisted differential evolution (DE) approach that adeptly addresses the demands of early exploration and subsequent exploitation by employing distinct mutation operators and surrogate models. In the initial stage, a combination of radial basis function and multi-dimensional Lipschitz function-assisted DE efficiently identifies a promising region within the complete decision space. After that, the subsequent stage employs a hybrid approach of local and global surrogate-assisted DE, renowned for its robust exploitation capabilities, to hasten the optimization process. This stage incorporates an on-the-fly update approach for both populations and surrogate models, utilizing a pre-set quantity of top-ranked individuals to facilitate updates. Additionally, a sampling technique based on a full-mutation operator is employed to incorporate the best genotypes in the population effectively. A surrogate-assisted local search operator is leveraged in the final stage to optimize the ultimate solution. This stage integrates a radial basis function-based local surrogate function and an interior-point method, enhancing sampling efficiency within the designated local region of interest. The efficacy of the three-stage framework and the proposed strategy is thoroughly validated through simulation experiments, empirical analyses, and ablation studies. Furthermore, we compare the proposed algorithm against other state-of-the-art surrogate-assisted evolutionary algorithms (SAEAs) on a diverse set of expensive benchmark functions and a real-world problem, demonstrating superior performance in terms of both robustness and effectiveness.
DOI
10.1016/j.swevo.2025.102093
Publication Date
10-1-2025
Recommended Citation
Kumar, Abhishek; Das, Swagatam; and Snášel, Václav, "Efficient three-stage surrogate-assisted differential evolution for expensive optimization problems" (2025). Journal Articles. 5332.
https://digitalcommons.isical.ac.in/journal-articles/5332