Abstract
Artificial bee colony (ABC) is a popular swarm intelligence algorithm, which has shown excellent performance on many optimization problems. However, it is rarely used to solve computationally expensive problems. In this paper, a multi-surrogate-assisted ABC (called MSABC) algorithm is proposed to solve computationally expensive problems. Multiple surrogates cannot only improve the prediction performance and estimate the degree of prediction uncertainty, but also capture both global and local features of the fitness landscape. In the employed bee phase, Radial Basis Function (RBF) network is used as a global surrogate model to assist ABC to quickly find the region where the global optimum might be located. In the onlooker bee phase, Kriging model is employed as a local surrogate built around some top best data points. To speed up the convergence, multiple dimensions for each solution are updated simultaneously. Experimental studies on CEC 2014 expensive optimization benchmark set show that the proposed approach can effectively solve those expensive problems under a limited computational budget.
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Acknowledgment
This work was supported by National Natural Science Foundation of China (No. 62166027), and Jiangxi Provincial Natural Science Foundation (Nos. 20212ACB212004, 20212BAB202023, and 20212BAB202022).
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Zeng, T., Wang, H., Ye, T., Wang, W., Zhang, H. (2022). A Multi-Surrogate-Assisted Artificial Bee Colony Algorithm for Computationally Expensive Problems. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_30
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