Swarm-based optimization with jumps: a kinetic BGK framework and convergence analysis
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917354416373760 |
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| author | Borghi, Giacomo Im, Hyesung Pareschi, Lorenzo |
| author_facet | Borghi, Giacomo Im, Hyesung Pareschi, Lorenzo |
| contents | Metaheuristic algorithms are powerful tools for global optimization, particularly for non-convex and non-differentiable problems where exact methods are often impractical. Particle-based optimization methods, inspired by swarm intelligence principles, have shown effectiveness due to their ability to balance exploration and exploitation within the search space. In this work, we introduce a novel particle-based optimization algorithm where velocities are updated via random jumps, a strategy commonly used to enhance stochastic exploration. We formalize this approach by describing the dynamics through a kinetic modelling of BGK type, offering a unified framework that accommodates general noise distributions, including heavy-tailed ones like Cauchy. Under suitable parameter scaling, the model reduces to the Consensus-Based Optimization (CBO) dynamics. For non-degenerate Gaussian noise in bounded domains, we prove propagation of chaos and convergence towards minimizers. Numerical results on benchmark problems validate the approach and highlight its connection to CBO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00871 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Swarm-based optimization with jumps: a kinetic BGK framework and convergence analysis Borghi, Giacomo Im, Hyesung Pareschi, Lorenzo Optimization and Control Numerical Analysis 65K10, 90C26, 65C35, 82C40, 35Q90 Metaheuristic algorithms are powerful tools for global optimization, particularly for non-convex and non-differentiable problems where exact methods are often impractical. Particle-based optimization methods, inspired by swarm intelligence principles, have shown effectiveness due to their ability to balance exploration and exploitation within the search space. In this work, we introduce a novel particle-based optimization algorithm where velocities are updated via random jumps, a strategy commonly used to enhance stochastic exploration. We formalize this approach by describing the dynamics through a kinetic modelling of BGK type, offering a unified framework that accommodates general noise distributions, including heavy-tailed ones like Cauchy. Under suitable parameter scaling, the model reduces to the Consensus-Based Optimization (CBO) dynamics. For non-degenerate Gaussian noise in bounded domains, we prove propagation of chaos and convergence towards minimizers. Numerical results on benchmark problems validate the approach and highlight its connection to CBO. |
| title | Swarm-based optimization with jumps: a kinetic BGK framework and convergence analysis |
| topic | Optimization and Control Numerical Analysis 65K10, 90C26, 65C35, 82C40, 35Q90 |
| url | https://arxiv.org/abs/2507.00871 |