Surrogate-assisted grey wolf optimization for high-dimensional, computationally expensive black-box problems

In this paper, a Surrogate-Assisted Grey Wolf Optimization (SAGWO) algorithm for high-dimensional and computationally expensive problems is presented, where Radial Basis Function (RBF) is employed as the surrogate model. SAGWO conducts the search in three phases, initial exploration, RBF-assisted meta-heuristic exploration, and knowledge mining on RBF. In the initial exploration, the Design of Experiments is carried out to generate a group of well-distributed samples based on which the original wolf pack and wolf leaders are sequentially identified to approximate the high-dimensional space roughly. The knowledge mining on RBF includes a global search that is carried out using the grey wolf optimization and a local search that is performed over a focused local region using a search strategy combining global and multi-start local exploration. In the proposed SAGWO, knowledge gained from the RBF model assists the generation of new wolf leaders in each cycle, and the positions of the wolf pack are iteratively changed following the wolf leaders, thus reaching balanced exploitation and exploration. The new SAGWO algorithm presents superior computation efficiency and robustness as demonstrated by comparison tests with ten representative global optimization algorithms on 30, 50 and 100 design variables.