Energy-Stable Swarm-Based Inertial Algorithms for Optimization
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915388200058880 |
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| author | Gu, Xuelong Wang, Qi |
| author_facet | Gu, Xuelong Wang, Qi |
| contents | We formulate the swarming optimization problem as a weakly coupled, dissipative dynamical system governed by a controlled energy dissipation rate and initial velocities that adhere to the nonequilibrium Onsager principle. In this framework, agents' inertia, positions, and masses are dynamically coupled. To numerically solve the system, we develop a class of efficient, energy-stable algorithms that either preserve or enhance energy dissipation at the discrete level. At equilibrium, the system tends to converge toward one of the lowest local minima explored by the agents, thereby improving the likelihood of identifying the global minimum. Numerical experiments confirm the effectiveness of the proposed approach, demonstrating significant advantages over traditional swarm-based gradient descent methods, especially when operating with a limited number of agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09909 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Energy-Stable Swarm-Based Inertial Algorithms for Optimization Gu, Xuelong Wang, Qi Numerical Analysis We formulate the swarming optimization problem as a weakly coupled, dissipative dynamical system governed by a controlled energy dissipation rate and initial velocities that adhere to the nonequilibrium Onsager principle. In this framework, agents' inertia, positions, and masses are dynamically coupled. To numerically solve the system, we develop a class of efficient, energy-stable algorithms that either preserve or enhance energy dissipation at the discrete level. At equilibrium, the system tends to converge toward one of the lowest local minima explored by the agents, thereby improving the likelihood of identifying the global minimum. Numerical experiments confirm the effectiveness of the proposed approach, demonstrating significant advantages over traditional swarm-based gradient descent methods, especially when operating with a limited number of agents. |
| title | Energy-Stable Swarm-Based Inertial Algorithms for Optimization |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2507.09909 |