Evolutionary Constrained Optimization Based on Causal Random Forest

Constrained optimization problems constitute a class of optimization tasks that aim to maximize or minimize an objective function subject to intricate constraints. Evolutionary algorithms are extensively employed to tackle these problems, but the inherent nonlinearity, discontinuity, and restricted feasible regions of constrained optimization problems present significant challenges, and existing approaches often rely on predefined rules or empirical thresholds, which limit their adaptability and hinder causal interpretability. To overcome this limitation, this study proposes an evolutionary constrained optimization algorithm based on causal random forest that leverages causal random forest to quantify the causal strength between objective optimization and constraint satisfaction, thereby guiding the evolutionary search in a principled and informed manner. Furthermore, a dynamic adaptive strategy-switching mechanism is incorporated into the algorithm to reduce the reliance on empirical thresholds and fixed rules, which enhances the self-adaptive capability of the algorithm under complex and sophisticated constraints. Extensive experimental results on the CEC2006, CEC2010, and CEC2017 benchmark suites demonstrate that the proposed method consistently outperforms existing methods, underscoring its effectiveness and robustness in handling constrained optimization problems.