Doubly Robust Causal Learning-Driven Adaptive Evolutionary Constrained Optimization Algorithm

Constrained optimization problems are widely prevalent, with evolutionary algorithms serving as predominant solution approaches. However, these algorithms exhibit inadequate adaptability and lack causal interpretability due to excessive dependence on predefined heuristic rules. This paper proposes an evolutionary constrained optimization framework driven by doubly robust causal learning, which quantifies the causal interaction between objective optimization and constraint satisfaction through the use of causal random forests, and designs a dynamic adaptive strategy-switching mechanism to autonomously select constraint priority, objective priority, and their corresponding complete comparison strategies. Comprehensive experiments conducted on mainstream benchmark suites demonstrate that the proposed framework outperforms state-of-the-art algorithms in convergence speed, solution quality, and stability, while exhibiting enhanced robustness in high-dimensional and strongly constrained scenarios.