Expensive Optimization via Surrogate-Assisted and Model-Free Evolutionary Optimization

Abstract

The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient approaches for solving expensive optimization problems. However, it still faces challenges when dealing with complex and high-dimensional problems. To fill this gap, a new algorithm (called SAMFEO) that combines surrogate-assisted and model-free evolutionary optimization is proposed in this article. SAMFEO consists of a local surrogate-assisted multioperator evolutionary optimization (LSA-MoEO) and a model-free single-operator evolutionary optimization (MF-SoEO). Specifically, LSA-MoEO adopts multiple evolutionary operators to generate a set of offspring and prescreens the best one as the final offspring by using a lightweight local surrogate model trained by some newest evaluated solutions. MF-SoEO follows the traditional evolutionary optimization paradigm and is triggered based on the optimization utility of the LSA-MoEO. It plays a crucial role in preventing the population from getting stagnation. Experimental results show that SAMFEO has significant advantages over several state-of-the-art SAEAs on some complex benchmark problems and one real-world problem.