Abstract
Global optimization problems have widespread applications across various fields such as scientific research, engineering, economics, and artificial intelligence. Consensus-based optimization algorithm is a class of multi-agent, meta-heuristic and derivative-free algorithms. It is designed to solve global nonsmooth and nonconvex optimization problems, while also being conducive to theoretical analysis and algorithm implementation. In this paper, we first introduce the fundamental principles and analytical results of the original algorithm. Subsequently, the latest development of the consensus-based optimization algorithms and their variants are discussed in detail. And the applications in fields such as machine learning and image processing are briefly described. Finally, we explore future research directions across three key areas: theoretical innovation, algorithm design, and application expansion.
| Translated title of the contribution | A survey on research advances in consensus-based optimization algorithm |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 1-23 |
| Number of pages | 23 |
| Journal | Operations Research Transactions |
| Volume | 30 |
| Issue number | 1 |
| DOIs | |
| State | Published - 15 Mar 2026 |
| Externally published | Yes |
Fingerprint
Dive into the research topics of 'A survey on research advances in consensus-based optimization algorithm'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver