运筹学学报(中英文) ›› 2026, Vol. 30 ›› Issue (1): 1-23.doi: 10.15960/j.cnki.issn.1007-6093.2026.01.001
• •
魏佳祯, 边伟†
收稿日期:2025-04-06
发布日期:2026-03-16
通讯作者:
边伟 E-mail:bianweilvse520@163.com
基金资助:WEI Jiazhen, BIAN Wei†
Received:2025-04-06
Published:2026-03-16
摘要: 全局优化问题在科学研究、工程、经济学及人工智能等多个领域均有着广泛的应用。共识优化算法作为一类多智能体元启发式无导数优化算法,旨在解决非光滑非凸的全局优化问题,且易于理论分析和算法实现。本文首先介绍经典共识优化算法的基本原理及其分析结果;随后, 详细论述共识优化算法及其变形的最新进展,并简述其在机器学习、图像处理等领域的应用; 最后,从理论创新、算法设计和应用拓展三个维度对未来研究方向进行了展望。
中图分类号:
魏佳祯, 边伟. 共识优化算法的研究进展综述[J]. 运筹学学报(中英文), 2026, 30(1): 1-23.
WEI Jiazhen, BIAN Wei. A survey on research advances in consensus-based optimization algorithm[J]. Operations Research Transactions, 2026, 30(1): 1-23.
| [1] Fogel D B. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence [M]. Piscataway: IEEE Press, 2000. [2] Tang K S, Man K F, Kwong S, et al. Genetic algorithms and their applications [J]. IEEE Signal Processing Magazine, 1996, 13(6): 22-27. [3] Kirkpatrick S, Gelatt C D, Vecchi M P. Optimization by simulated annealing [J]. Science, 1983, 220(4598): 671-680. [4] Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: A gravitational search algorithm [J]. Information Sciences, 2009, 179(13): 2232-2248. [5] Dorigo M, Blum C. Ant colony optimization theory: A survey [J]. Theoretical Computer Science, 2005, 344(2-3): 243-278. [6] Kennedy J, Eberhart R. Particle swarm optimization [C]//Proceedings of ICNN’95- International Conference on Neural Networks, 1995: 1942-1948. [7] Pan W T. A new fruit fly optimization algorithm: Taking the financial distress model as an example [J]. Knowledge-Based Systems, 2012, 26: 69-74. [8] Krishnanand K N, Ghose D. Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions [J]. Swarm Intelligence, 2009, 3: 87-124. [9] Karaboga D, Gorkemli B, Ozturk C, et al. A comprehensive survey: Artificial bee colony (ABC) algorithm and applications [J]. Artificial Intelligence Review, 2014, 42(1): 21-57. [10] Atyabi A, Luerssen M H, Powers D M W. PSO-based dimension reduction of EEG recordings: Implications for subject transfer in BCI [J]. Neurocomputing, 2013, 119(7): 319-331. [11] Liang Y, Wang L D. Applying genetic algorithm and ant colony optimization algorithm into marine investigation path planning model [J]. Soft Computing, 2020, 24(11): 8199-8210. [12] Pinnau R, Totzeck C, Tse O, et al. A consensus-based model for global optimization and its mean-field limit [J]. Mathematical Models and Methods in Applied Sciences, 2017, 27(1): 183-204. [13] Grassi S, Pareschi L. From particle swarm optimization to consensus based optimization: Stochastic modeling and mean-field limit [J]. Mathematical Models and Methods in Applied Sciences, 2021, 31(8): 1625-1657. [14] Huang H, Qiu J N, Riedl K. On the global convergence of particle swarm optimization methods [J]. Applied Mathematics and Optimization, 2023, 88: 30. [15] Carrillo J A, Choi Y P, Totzeck C, et al. An analytical framework for consensus-based global optimization method [J]. Mathematical Models and Methods in Applied Sciences, 2018, 28(6): 1037-1066. [16] Carrillo J A, Jin S, Li L, et al. A consensus-based global optimization method for high dimensional machine learning problems [J]. ESAIM: Control; Optimisation and Calculus of Variations, 2021, 27: S5. [17] Ha S Y, Jin S, Kim D. Convergence of a first-order consensus-based global optimization algorithm [J]. Mathematical Models and Methods in Applied Sciences, 2020, 30(12): 2417-2444. [18] Fornasier M, Klock T, Riedl K. Consensus-based optimization methods converge globally [J]. SIAM Journal on Optimization, 2024, 34(3): 2973-3004. [19] Dembo A, Zeitouni O. Large Deviations Techniques and Applications [M]. Berlin: Springer, 2009. [20] Sznitman A S. Topics in propagation of chaos [C]//Ecole d’Ete de Probabilites de Saint-Flour XIX-1989, 1991: 165-251. [21] Øksendal B. Stochastic Differential Equations: An Introduction with Applications [M]. Berlin: Springer, 2003. [22] Huang H, Qiu J. On the mean-field limit for the consensus-based optimization [J]. Mathematical Models and Methods in Applied Sciences, 2022, 45(12): 7814-7831. [23] Chaintron L-P, Diez A. Propagation of chaos: A review of models, methods and applications. I. Models and methods [J]. Kinetic and Related Models, 2022, 15(6): 895-1015. [24] Gerber N J, Hoffmann F, Vaes U. Mean-field limits for consensus-based optimization and sampling [EB/OL]. [2025-03-01]. arXiv: 2312.07373v3. [25] Totzeck C, Wolfram M-T. Consensus-based global optimization with personal best [J]. Mathematical Biosciences and Engineering, 2020, 17(5): 6026-6044. [26] Borghi G, Grassi S, Pareschi L. Consensus based optimization with memory effects: Random selection and applications [J]. Chaos, Solitons and Fractals, 2023, 174: 113859. [27] Riedl K. Leveraging memory effects and gradient information in consensus-based optimisation: On global convergence in mean-field law [J]. European Journal of Applied Mathematics, 2024, 35: 483-514. [28] Chen J R, Jin S, Lyu L Y. A consensus-based global optimization method with adaptive momentum estimation [J]. Communications in Computational Physics, 2022, 31(4): 1296- 1316. [29] Fornasier M, Richtárik P, Riedl K, et al. Consensus-based optimisation with truncated noise [J]. European Journal of Applied Mathematics, 2025, 36: 292-315. [30] Ha S-Y, Jin S, Kim D. Convergence and error estimates for time-discrete consensus-based optimization algorithms [J]. Numerische Mathematik, 2021, 147: 255-282. [31] Ko D, Ha S Y, Jin S, et al. Convergence analysis of the discrete consensus-based optimization algorithm with random batch interactions and heterogeneous noises [J]. Mathematical Models and Methods in Applied Sciences, 2022, 32(6): 1071-1107. [32] Ha S Y, Hwang G, Kim S. Time-discrete momentum consensus-based optimization algorithm and its application to Lyapunov function approximation [J]. Mathematical Models and Methods in Applied Sciences, 2024, 24(6): 1153-1204. [33] Wei J Z, Bian W. A smoothing consensus-based optimization algorithm for nonsmooth nonconvex optimization [EB/OL]. [2025-03-01]. arXiv: 2501.06804. [34] Wei J Z, Bian W. Smoothing iterative consensus-based optimization algorithm for nonsmooth nonconvex optimization problems with global optimality [J]. Journal of Scientific Computing, 2025, 104: 22. [35] Chen X J. Smoothing methods for nonsmooth, nonconvex minimization [J]. Mathematical Programming, 2012, 134(1): 71-99. [36] Bian W, Chen X J. Worst-case complexity of smoothing quadratic regularization methods for non-Lipschitzian optimization [J]. SIAM Journal on Optimization, 2012, 23(3): 1718-1741. [37] Bian W, Chen X J. A smoothing proximal gradient algorithm for nonsmooth convex regression with cardinality penalty [J]. SIAM Journal on Numerical Analysis, 2012, 58(1): 858-883. [38] Wei J Z, Wu F, Bian W. A consensus-based optimization method for nonsmooth nonconvex programs with approximated gradient descent scheme [EB/OL]. [2025-03-01]. arXiv:2501.08906. [39] Berahas A S, Cao L Y, Choromanski K, et al. A theoretical and empirical comparison of gradient approximations in derivative-free optimization [J]. Foundations of Computational Mathematics, 2022, 22: 507-560. [40] Fornasier M, Huang H, Pareschi L, et al. Consensus-based optimization on the sphere: Convergence to global minimizers and machine learning [J]. Journal of Machine Learning Research, 2021, 22: 1-55. [41] Fornasier M, Huang H, Pareschi L, et al. Consensus-based optimization on hypersurfaces: Wellposedness and mean-field limit [J]. Mathematical Models and Methods in Applied Sciences, 2020, 30: 2725-2751. [42] Fornasier M, Huang H, Pareschi L, et al. Anisotropic diffusion in consensus-based optimization on the sphere [J]. SIAM Jouranl on Optimization, 2022, 32(3): 1984-2012. [43] Borghi G, Herty M, Pareschi L. Constrained consensus-based optimization [J]. SIAM Journal on Optimization, 2023, 33(1): 211-236. [44] Huang H, Qiu J N, Riedl K. Consensus-based optimization for saddle point problems [J]. Mathematical Models and Methods in Applied Sciences, 2024, 62(2): 1093-1121. [45] Von Neumann J, Morgenstern O. Theory of Games and Economic Behavior [M]. Princeton: Princeton University Press, 2007. [46] Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult [J]. IEEE Transactions on Neural Networks and Learning Systems, 1994, 5: 157-166. [47] Hanin B. Which neural net architectures give rise to exploding and vanishing gradients? [C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems, 2018: 580-589. [48] Carrillo J A, Trillos N G, Li S, et al. FedCBO: Reaching group consensus in clustered federated learning through consensus-based optimization [J]. Journal of Machine Learning Research, 2024, 25(214): 1-51. [49] Benfenati A, Borghi G, Pareschi L. Binary interaction methods for high dimensional global optimization and machine learning [J]. Applied Mathematics and Optimization, 2022, 86(1): 9. [50] Fornasier M, Klock T, Riedl K. Convergence of anisotropic consensus-based optimization in mean-field law [C]//Proceedings of the 25th International Conference on the Applications of Evolutionary Computation, 2022: 738-754. [51] Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition [C]//Proceedings of the IEEE, 1998: 2278-2324. |
| [1] | 张博, 王红雨, 高岳林. 线性乘积和规划问题的基于D.C.松弛的分支定界算法[J]. 运筹学学报(中英文), 2025, 29(4): 159-174. |
| [2] | 袁柳洋, 汤梦瑶, 迟晓妮. 一类新的无参数的填充打洞函数法[J]. 运筹学学报(中英文), 2025, 29(2): 214-220. |
| [3] | 马素霞, 高岳林, 林洪伟, 张博. 一种新的全局优化无参数填充函数方法[J]. 运筹学学报(中英文), 2025, 29(2): 141-157. |
| [4] | 白富生, 兰秘. 带隐藏约束昂贵黑箱问题的自适应代理优化方法[J]. 运筹学学报(中英文), 2024, 28(1): 89-100. |
| [5] | 黄小利, 高岳林, 张博, 刘霞. 一种求解二次约束二次规划问题的自适应全局优化算法[J]. 运筹学学报, 2022, 26(2): 83-100. |
| [6] | 白富生, 冯丹, 张柯. 求解昂贵黑箱全局优化问题的自适应采样组合响应面方法[J]. 运筹学学报, 2021, 25(2): 1-14. |
| [7] | 陈佳利, 张莹, 王胜刚, 谢笑盈. 一个新的填充函数及其在数据拟合问题中的应用[J]. 运筹学学报, 2021, 25(1): 81-88. |
| [8] | 赵丹, 高岳林. 非线性整数规划问题的无参数填充函数算法[J]. 运筹学学报, 2020, 24(4): 63-73. |
| [9] | 陈永, 王薇, 徐以汎. 具有间断扩散性质的线性约束全局优化随机算法[J]. 运筹学学报, 2020, 24(1): 88-100. |
| [10] | 王伟祥, 尚有林, 王朵. 求解带箱子集约束的非光滑全局优化问题的填充函数方法[J]. 运筹学学报, 2019, 23(1): 28-34. |
| [11] | 郑跃, 庄道元, 万仲平. 求解弱线性双层规划问题的一种全局优化方法[J]. 运筹学学报, 2017, 21(3): 86-94. |
| [12] | 石礼堂, 陈伟. 非线性无约束优化问题的滤子填充函数算法[J]. 运筹学学报, 2017, 21(1): 55-64. |
| [13] | 胡铨, 王薇. 求解带箱式约束全局优化问题的滤子填充函数方法[J]. 运筹学学报, 2016, 20(3): 57-67. |
| [14] | 戴彧虹,刘新为. 线性与非线性规划算法与理论[J]. 运筹学学报, 2014, 18(1): 69-92. |
| [15] | 汤丹. 基于模拟退火的CRS算法[J]. 运筹学学报, 2011, 15(4): 124-128. |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||