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A comprehensive review on deep learning for multi-objective optimization
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AI
DOI：10.1016/j.swevo.2026.102304.png)
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Abstract
En 中文
Over the past decades, multi-objective optimization has established itself as a fundamental and continuously evolving research area within computational intelligence. While traditional methods remain relevant, the integration of deep learning techniques has recently opened up new possibilities for solving complex optimization problems with multiple competing objectives. This trend has led to the development of numerous innovative approaches that leverage the powerful pattern recognition and representation learning capabilities of deep neural networks. This review systematically examines the current landscape of deep learning applications in multi-objective optimization, beginning with essential foundational concepts before progressing to a detailed analysis of how various deep learning architectures have been adapted for optimization tasks. Then the review categorizes these applications across different engineering domains and discusses their practical implementations. Finally, the paper outlines several promising research directions for advancing this rapidly evolving field, including the development of novel network architectures, deeper integration of deep learning with established multi-objective optimization frameworks, the creation of user-oriented interactive systems, the establishment of explainable theoretical foundations, and the reduction of computational costs.
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