期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 47, 期 9, 页码 2664-2677出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2710978
关键词
Active learning; expensive problems; model management; particle swarm optimization (PSO); surrogate
类别
资金
- EPSRC [EP/M017869/1]
- National Natural Science Foundation of China [61590922]
- Joint Research Fund for Overseas Chinese, Hong Kong, and Macao Scholars of the National Natural Science Foundation of China [61428302]
- Engineering and Physical Sciences Research Council [EP/M017869/1] Funding Source: researchfish
- EPSRC [EP/M017869/1] Funding Source: UKRI
Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted EAs (SAEAs) either still require thousands of expensive FEs to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogate-assisted particle swarm optimization (PSO) inspired from committee-based active learning (CAL) is proposed. In the proposed algorithm, a global model management strategy inspired from CAL is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a PSO 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 PSO 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 SAEAs 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 FEs.
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