A Q-learning driven competitive surrogate assisted evolutionary optimizer with multiple oriented mutation operators for expensive problems

Abstract

Surrogate-assisted evolutionary algorithms (SAEAs) prevail in the solution of computationally expensive optimization problems. However, with the growth of problem scale and complexity, the high-dimensional problem space greatly restricts their effectiveness and applicability. This paper proposes a Q-learning driven competitive surrogate-assisted evolutionary optimizer with multiple oriented mutation operators (CSEO-MOMO) to improve the prediction effectiveness and accuracy of SAEAs in different scenarios of optimization problems. CSEO-MOMO constructs isomorphic models with different complexities during iteration. The Q-learning method is employed to pick the proper model for prediction based on the state feedback of the iterative population. To explore the search space more comprehensively and increase the probability of finding a better solution, two competitive sub-populations coevolve with personalized mutation operators, of which different oriented optimal tractive base vectors are nominated to guide the mutation direction. In addition, a tailored infilling strategy concerning both the performance of the iterative individuals and their spatial distribution is used to winnow out the individuals with great potential for real evaluation. A suit of high-dimensional CEC benchmarks of different properties and an openly shared topology optimization problem are used for the effectiveness and efficiency verification of CSEO-MOMO. The experimental results reveal that CSEO-MOMO trumps seven excellent SAEAs in solving high-dimensional complex problems and possesses better accuracy and robustness under various fitness landscape scenarios. (The relevant MATLAB code of CSEO-MOMO is publicly available in the first author’s GitHub repository: https://github.com/qinna-zhu/CSEO-MOMO.)