@article{Zhang2025, 
author = {Yongfeng Zhang and Zhihao Liu and Kaili Jia and Zhenbin Zhang and Hsiaodong Chiang},
title = {Novel Consensus-Based Particle Swarm Optimization-Guided Surrogate-Enhanced Methodology for Solving Expensive Black-Box Optimization},
year = {2025},
journal = {Complex System Modeling and Simulation},
volume = {5},
number = {4},
pages = {340-353},
keywords = {black box optimization, consensus-based particle swarm optimization, local cheap surrogate model, high-quality optimal solutions},
url = {https://www.sciopen.com/article/10.23919/CSMS.2025.0006},
doi = {10.23919/CSMS.2025.0006},
abstract = {A novel consensus-based particle swarm optimization-guided surrogate-enhanced methodology (ACP-S) is developed to solve expensive black-box optimization (EBBO) problems. The proposed methodology consists of three stages: the global exploration and grouping stage, local exploitation via the surrogate model, and the ranking, refinement, and feedback stage. The methodology only searches a subset of the entire search space that contains high-quality optimal solutions and may include the global optimal solution. The proposed three-stage method is fast and deterministic in computing high-quality optimal solutions. Extensive experimental results demonstrate that this proposed method can obtain high-quality optimal solutions and outperforms several existing methods on small or large EBBO test functions.}
}