A Synergistic MOEA Algorithm with GANs for Complex Data Analysis

Weihua Qian, Hang Xu, Houjin Chen, Lvqing Yang (), Yuanguo Lin, Rui Xu, Mulan Yang and Minghong Liao
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Weihua Qian: School of Informatics, Xiamen University, Xiamen 361000, China
Hang Xu: School of Mechanical, Electrical & Information Engineering, Putian University, Putian 351100, China
Houjin Chen: School of Informatics, Xiamen University, Xiamen 361000, China
Lvqing Yang: School of Informatics, Xiamen University, Xiamen 361000, China
Yuanguo Lin: School of Informatics, Xiamen University, Xiamen 361000, China
Rui Xu: College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
Mulan Yang: School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710061, China
Minghong Liao: School of Informatics, Xiamen University, Xiamen 361000, China

Mathematics, 2024, vol. 12, issue 2, 1-30

Abstract: The multi-objective evolutionary algorithm optimization (MOEA) is a challenging but critical approach for tackling complex data analysis problems. However, prevailing MOEAs often rely on single strategies to obtain optimal solutions, leading to concerns such as premature convergence and insufficient population diversity, particularly in high-dimensional data scenarios. In this paper, we propose a novel adversarial population generation algorithm, APG-SMOEA, which synergistically combines the benefits of MOEAs and Generative Adversarial Networks (GANs) to address these limitations. In order to balance the efficiency and quality of offspring selection, we introduce an adaptive population entropy strategy, which includes control parameters based on population entropy and a learning pool for storing and retrieving optimal solutions. Additionally, we attempt to alleviate the training complexity and model collapse problems common in GANs with APG-SMOEA. Experimental results on benchmarks demonstrate that the proposed algorithm is superior to the existing algorithms in terms of solution quality and diversity of low-dimensional or high-dimensional complex data.

Keywords: multi-objective optimization; evolutionary algorithm; generative adversarial network; complex data analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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