A competitive variable-fidelity surrogate-assisted CMA-ES algorithm using data mining techniques

Abstract

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm is one of the most successful continuous black-box optimization algorithms widely used in many fields. However, the algorithm would be computationally unaffordable due to the sharp increase in dimension and complexity of problems. To improve the computational efficiency and global optimizing ability, a competitive variable-fidelity surrogate-assisted CMA-ES (CVFS-CMA-ES) algorithm using data mining techniques is proposed in this paper. In the first data mining of CVFS-CMA-ES, the establishment method of the competitive variable-fidelity model (VFM) based on the fuzzy clustering algorithm is developed, aiming at making full use of high-fidelity information and focusing more on potential areas of design space. In the second data mining of CVFS-CMA-ES, the Lower Confidence Bound (LCB) method is modified by introducing the step size of CMA-ES to control the uncertain term adaptively, and the sample points are screened in each generation of CMA-ES based on the modified LCB method, which can make a balance between exploration and exploitation in the optimization process. The performance of CVFS-CMA-ES is compared with four known black-box optimization algorithms. Firstly, ten numerical examples of 10-dimensional and 30-dimensional benchmark functions are carried out, respectively. Moreover, a 20-dimensional engineering example of the aerospace variable-stiffness composite shell under combined loadings is studied in detail. Results of benchmark functions and engineering examples verify the high efficiency, robustness and high global optimizing ability of the proposed CVFS-CMA-ES in comparison to other algorithms.