Abstract
To solve engineering problems with evolutionary algorithms, many expensive objective function evaluations (FEs) are required. To alleviate this difficulty, the surrogate-assisted evolutionary algorithm (SAEA) has attracted increasingly more attention in both academia and industry. The existing SAEAs depend on the quantity and quality of the original samples, and it is difficult for them to yield satisfactory solutions within the limited number of FEs. Moreover, these methods easily fall into local optima as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization (ASAPSO) algorithm. In the proposed algorithm, an adaptive surrogate selection method that depends on the comparison between the best existing solution and the latest obtained solution is suggested to ensure the effectiveness of the optimization operations and improve the computational efficiency. Additionally, a model output criterion based on the standard deviation is suggested to improve the robustness and stability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within the limited number of FEs. The performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process.
An adaptive surrogate-assisted particle swarm optimization for expensive problems
