Study of Methods of Complex Data Analysis that Based on Machine Learning Technologies

Ivanytska, Anastasiia; Zubyk, Liudmyla; Ivanov, Dmitry; Domracheva, Kateryna

doi:10.1109/atit49449.2019.9030531

Cited by 1 publication

(1 citation statement)

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“…In recent years, complex data analysis has become an essential research field due to the increasing and diversified data generated by various sources [1], such as traffic logistics, commercial transactions, financial investment, and healthcare services [2,3]. One of the most significant and prevalent challenges in analyzing complex data include the multiobjective optimization problems (MOPs) [4], as optimizing one objective often leads to the deterioration of others.…”

Section: Introductionmentioning

confidence: 99%

A Synergistic MOEA Algorithm with GANs for Complex Data Analysis

Qian,

Xu,

Chen

et al. 2024

Mathematics

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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.

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Section: Introductionmentioning

confidence: 99%