Constrained Multi-objective Optimization with Search Direction Learning
Mingcheng Zuo, Dunwei Gong, Tianyang Xue, Chunliang Zhao, Yongde Guo · Proceedings of the Genetic and Evolutionary Computation Conference · 2025
The solving process of constrained multi-objective evolutionary optimization algorithms (CMOEAs) is closely related to the search direction of the population. How to learn promising search directions through population data remains challenging. Therefore, this paper proposes a CMOEA with search direction learning. In this method, principal component analysis (PCA) is first used to learn the mainstream direction of population evolution, then single-constraint domination is used to learn the tributary direction of population evolution, and finally, the search directions are summarized to guide the generation of high-quality offspring populations. The performance comparison with five state-of-the-art algorithms on three standard test problems demonstrates the superiority of the proposed method. Its applicability in the field of simulated integrated circuits proves the scalability of the proposed method.