Surrogate-Assisted Differential Evolution With Adaptive Multisubspace Search for Large-Scale Expensive Optimization
Haoran Gu, Handing Wang, Yaochu Jin
Abstract
Real-world industrial engineering optimization problems often have a large number of decision variables. Most existing large-scale evolutionary algorithms (EAs) need a large number of function evaluations to achieve high-quality solutions. However, the function evaluations can be computationally intensive for many of these problems, particularly, which makes large-scale expensive optimization challenging. To address this challenge, surrogate-assisted EAs based on the divide-and-conquer strategy have been proposed and shown to be promising. Following this line of research, we propose a surrogate-assisted differential evolution algorithm with adaptive multisubspace search for large-scale expensive optimization to take full advantage of the population and the surrogate mechanism. The proposed algorithm constructs multisubspace based on principal component analysis and random decision variable selection, and searches adaptively in the constructed subspaces with three search strategies. The experimental results on a set of large-scale expensive test problems have demonstrated its superiority over three state-of-the-art algorithms on the optimization problems with up to 1000 decision variables.