Consensus based optimization
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Consensus-based optimization (CBO) [1] is a multi-agent derivative-free optimization method, designed to obtain solutions for global optimization problems of the form where denotes the objective function acting on the state space . can potentially be nonconvex and nonsmooth. The algorithm employs particles or agents to explore the state space, which communicate with each other to update their positions. Their dynamics follows the paradigm of metaheuristics, which blend exporation with exploitation. In this sense, CBO is comparable to ant colony optimization, wind driven optimization[2], particle swarm optimization or Simulated annealing. However, compared to other heuristics, CBO was designed to have a well-posed mean-field limit
Convergence
Variants
Polarization
See also
References
- ^ Pinnau, René; Totzeck, Claudia; Tse, Oliver; Martin, Stephan (January 2017). "A consensus-based model for global optimization and its mean-field limit". Mathematical Models and Methods in Applied Sciences. 27 (1): 183–204. arXiv:1604.05648. doi:10.1142/S0218202517400061. ISSN 0218-2025. S2CID 119296432.
- ^ "The Wind Driven Optimization Technique and its Application in Electromagnetics | IEEE Journals & Magazine | IEEE Xplore". ieeexplore.ieee.org. Retrieved 2024-02-03.