An Offline Weighted-Bagging Data-Driven Evolutionary Algorithm with Data Generation Based on Clustering

Zongliang Guo, Sikai Lin, Runze Suo and Xinming Zhang ()
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Zongliang Guo: School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Sikai Lin: School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Runze Suo: School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Xinming Zhang: School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China

Mathematics, 2023, vol. 11, issue 2, 1-24

Abstract: In recent years, a variety of data-driven evolutionary algorithms (DDEAs) have been proposed to solve time-consuming and computationally intensive optimization problems. DDEAs are usually divided into offline DDEAs and online DDEAs, with offline DDEAs being the most widely studied and proven to display excellent performance. However, most offline DDEAs suffer from three disadvantages. First, they require many surrogates to build a relatively accurate model, which is a process that is redundant and time-consuming. Second, when the available fitness evaluations are insufficient, their performance tends to be not entirely satisfactory. Finally, to cope with the second problem, many algorithms use data generation methods, which significantly increases the algorithm runtime. To overcome these problems, we propose a brand-new DDEA with radial basis function networks as its surrogates. First, we invented a fast data generation algorithm based on clustering to enlarge the dataset and reduce fitting errors. Then, we trained radial basis function networks and carried out adaptive design for their parameters. We then aggregated radial basis function networks using a unique model management framework and demonstrated its accuracy and stability. Finally, fitness evaluations were obtained and used for optimization. Through numerical experiments and comparisons with other algorithms, this algorithm has been proven to be an excellent DDEA that suits data optimization problems.

Keywords: data-driven evolutionary algorithm (DDEA); surrogate models; radial basis function networks; bagging (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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