Objective-Constraint Correlation-Guided Evolutionary Direction Adaptive Adjustment for Expensive Constrained Optimization
针对目标与约束评估均昂贵的优化问题，提出一种代理模型辅助进化算法，通过分析目标与约束的相关性自适应调整进化方向，以更有效地找到可行解并避免局部最优。
For expensive constrained optimization problems (ECOPs), the evaluation of the objective and constraints are both expensive. Due to the low computational cost of the surrogate model and the excellent search capabilities of evolutionary algorithms, surrogate-assisted evolutionary algorithms (SAEAs) have become a popular approach for solving ECOPs. When solving ECOPs, the errors in the objective and constraint surrogates will inevitably mislead the direction of evolution, making it difficult to find feasible solutions and avoid local optima. To defeat this issue, we propose an SAEA capable of adjusting the evolutionary direction to search in the correct direction as much as possible. Specifically, the correlation between objective and constraint is first analyzed, and then adaptive adjustments are made based on this correlation to revise the evolutionary direction throughout the three stages of the evolutionary process. For reproduction, an offspring enhanced generation strategy is proposed to generate promising and diverse offspring. For sampling, a dynamic infill sampling criterion is designed to select the most suitable solutions for expensive evaluations, thereby accelerating convergence. Finally, an adaptive environment selection strategy is designed to choose parents with more potential for improvement. The proposed method is evaluated on commonly used benchmark test functions and four engineering examples, with experimental results indicating its superior performance compared to other advanced methods.