Process / pipelineSimulation / optimization
随机粒子群优化 — 随机化群体智能全局搜索
Stochastic Particle Swarm Optimization (Stochastic PSO) 是一种基于群体智能的元启发式算法，它通过引入显式的随机元素——随机惯性权重、概率性速度重置或噪声注入——来扩展标准的 PSO 框架，以逃离局部最优并维持整个搜索过程中的种群多样性。该算法广泛应用于工程、运筹学和基于仿真的设计中的连续、混合和有噪声优化问题。
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来源
- Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE. DOI: 10.1109/ICNN.1995.488968 ↗
- Clerc, M., Kennedy, J. (2002). The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58-73. DOI: 10.1109/4235.985692 ↗
如何引用本页
ScholarGate. (2026, June 3). Stochastic Particle Swarm Optimization (Stochastic PSO). ScholarGate. https://scholargate.app/zh/simulation/stochastic-particle-swarm-optimization
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