Surrogate model-driven differential evolution with multi-strategy techniques
Anshan Chen, Hecheng Li, Hua Ran · 2022
For large-scale complex optimization problems, some traditional evolutionary algorithms (EAs) using a single evolutionary strategy often face the challenges of easily falling into the local optima and are computationally expensive. In order to deal with these shortcomings, a multi-strategy differential evolution algorithm based on surrogate model is proposed in this manuscript. First, to reduce the computational cost, we employ cheap surrogate models to reduce the number of real function evaluations and construct global and local surrogate models to guide population evolution. Secondly, a multi-strategy differential evolution operator is developed to generate promising individuals and enhance population diversity. Finally, seven benchmark functions with up 200 decision variables are calculated to illustrate the efficiency of the proposed method. The experimental results show that the proposed algorithm is highly competitive when compared with other state-of-the-art algorithms for large-scale complex optimization problems.