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Novel Consensus-Based Particle Swarm Optimization-Guided Surrogate-Enhanced Methodology for Solving Expensive Black-Box Optimization

Year: 2025

Type: article

Abstract: A novel Consensus-Based Particle Swarm Optimization-Guided Surrogate-Enhanced Methodology (ACP-S) is developed to solve expensive black-box optimization (EBBO) problems. ACP-S formed form Global Exploration and Grouping Stage, Local Exploitation via the Surrogate Model Stage and Ranking, Refined Exploitation and Feedback Stage only search in 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.

Source: Complex System Modeling and Simulation

DOI: 10.23919/csms.2025.0006

Authors: Yongfeng Zhang, Zhihao Liu, Kaili Jia, Zhenbin Zhang, Hsiaodong Chiang

Institutions: University of Jinan, State Grid Corporation of China (China), Shandong University, Cornell University

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4409560147

FWCI: 0.427

Cites: 27

Cited by: 1

Related to: 10