A Synergistic MOEA Algorithm with GANs for Complex Data Analysis
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Published:2024-01-05
Issue:2
Volume:12
Page:175
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ISSN:2227-7390
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Container-title:Mathematics
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language:en
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Short-container-title:Mathematics
Author:
Qian Weihua1, Xu Hang2ORCID, Chen Houjin1, Yang Lvqing1, Lin Yuanguo1, Xu Rui3, Yang Mulan4, Liao Minghong1
Affiliation:
1. School of Informatics, Xiamen University, Xiamen 361000, China 2. School of Mechanical, Electrical & Information Engineering, Putian University, Putian 351100, China 3. College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China 4. School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710061, China
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.
Funder
2021 Fujian Foreign Cooperation Project the Horizontal project (Co-construction platform), 2023 Project of Xiamen University: Joint Laboratory of Public Safety and Artificial Intelligence the State Key Laboratory of Process Automation in Mining & Metallurgy, Beijing Key Laboratory of Process Automation in Mining & Metallurgy National Key R&D Program of China-Sub-project of Major Natural Disaster Monitoring, Early Warning and Prevention Scientific Research Project of Putian Science and Technology Bureau National Natural Science Foundation of China Natural Science Foundation of Fujian Province
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
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