共识优化算法的研究进展综述

doi:10.15960/j.cnki.issn.1007-6093.2026.01.001

Abstract

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.

Key words: global optimization, nonsmooth and nonconvex optimization, consensus-base optimization algorithm, swarm intelligence algorithm, finite particle system

CLC Number:

O221.2

WEI Jiazhen, BIAN Wei. A survey on research advances in consensus-based optimization algorithm[J]. Operations Research Transactions, 2026, 30(1): 1-23.

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A survey on research advances in consensus-based optimization algorithm

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