A Dual-Surrogate Competition-Assisted Evolutionary Algorithm With Triggered Constraint First Search for Expensive Constrained Optimization

Expensive constrained optimization problems (ECOPs), which frequently arise in real-world engineering optimization, are often limited by the number of evaluations. Using surrogate-assisted evolutionary algorithms (EAs) to reduce computational costs is a common approach. However, surrogate model errors are inevitable and often mislead the search direction of EAs. Most existing algorithms overlook the inevitability of such errors and attempt to minimize them through techniques like data selection, which might be ineffective for problems with highly complex constraint and objective functions. Therefore, we propose a dual-surrogate competition assisted EA with triggered constraint-first search (DC-TCFS) for ECOPs, aiming to reduce the misleading effects of surrogate models on the evolutionary process. In this study, two surrogate models are used to assist local searches through competition, effectively mitigating the impact of errors from a single surrogate model. A triggered constraint-first search method is proposed to quickly identify a feasible solution for problems with complex constraints. Additionally, an adaptive sampling criterion is designed to guide the algorithm toward solutions that are more beneficial to the evolutionary process. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art methods across various benchmark problems, real-world problems and a motor design optimization problem, highlighting its effectiveness in solving ECOPs.