Graph Neural Network-Guided Evolutionary Algorithm for Solving Constrained Multi-Objective Optimization Problems
ZHANG Yiqin
HAN Zongchen
SUN Jing
ZHAO Chunliang
Abstract：Constrained multi-objective optimization problems are typically challenging due to the complexi-ty of constraints，irregularity of the feasible region，and sparsity of feasible solutions. These factors make it difficult to precisely characterize the constraint relationships and to find Pareto non-dominated solutions that are both well-converged and evenly distributed. To address these challenges, this paper proposes a graph neural network-guided constrained multi-objective evolutionary algorithm. The algorithm includes a learning module and an adaptive weight vector strategy. The learning module leverages a graph neural net-work trained to rapidly evaluate solution sets，while the adaptive weight vector strategy enhances popula-tion diversity through a discrimination criterion and an update mechanism. Experimental results show that the proposed algorithm significantly outperforms five state-of-the-art algorithms on various benchmark test problems and performs exceptionally well on complex constrained multi-objective optimization problems.
Keywords：graph neural networksconstrained multi-objective optimization problemsconstraint-based multi-objective evolutionary algorithmsweight vector update
Publication Date：2025-02-27
Online Publishing Date：2026-08-28(First online date of this platform, not the publication date of the document)
Pages：12( 135-146 )