Abstract
Function evaluations of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms to solve these problems. To address this challenge, the research on surrogate-assisted evolutionary algorithms has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted evolutionary algorithms either still require thousands of expensive function evaluations to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogateassisted particle swarm optimization inspired from committeebased active learning is proposed. In the proposed algorithm, a global model management strategy inspired from committeebased active learning is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a particle swarm optimization algorithm and evaluates these solutions using the expensive objective function. In addition, a local surrogate model is built around the best solution obtained so far. Then a particle swarm optimization algorithm searches on the local surrogate to find its optimum and evaluates it. The evolutionary search using the global model management strategy switches to the local search once no further improvement can be observed, and vice versa. This iterative search process continues until the computational budget is exhausted. Experimental results comparing the proposed algorithm with a few state-of-the-art surrogate-assisted evolutionary algorithms on both benchmark problems up to 30 decision variables as well as an airfoil design problem demonstrate that the proposed algorithm is able to achieve better or competitive solutions with a limited budget of hundreds of exact function evaluations.