期刊
SWARM INTELLIGENCE
卷 11, 期 3-4, 页码 295-315出版社
SPRINGER
DOI: 10.1007/s11721-017-0144-7
关键词
Particle swarm optimisation; Criticality; Random dynamical systems; Random matrix products; Parameter selection
资金
- Engineering and Physical Sciences Research Council (EPSRC) [EP/K503034/1]
Particle swarm optimisation (PSO) is a metaheuristic algorithm used to find good solutions in a wide range of optimisation problems. The success of metaheuristic approaches is often dependent on the tuning of the control parameters. As the algorithm includes stochastic elements that effect the behaviour of the system, it may be studied using the framework of random dynamical systems (RDS). In PSO, the swarm dynamics are quasi-linear, which enables an analytical treatment of their stability. Our analysis shows that the region of stability extends beyond those predicted by earlier approximate approaches. Simulations provide empirical backing for our analysis and show that the best performance is achieved in the asymptotic case where the parameters are selected near the margin of instability predicted by the RDS approach.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据