Adaptive Particle Swarm Optimization

9 years 10 months ago
Adaptive Particle Swarm Optimization
An adaptive particle swarm optimization (APSO) that features better search efficiency than classical particle swarm optimization (PSO) is presented. More importantly, it can perform a global search over the entire search space with faster convergence speed. The APSO consists of two main steps. First, by evaluating the population distribution and particle fitness, a real-time evolutionary state estimation procedure is performed to identify one of the following four defined evolutionary states, including exploration, exploitation, convergence, and jumping out in each generation. It enables the automatic control of inertia weight, acceleration coefficients, and other algorithmic parameters at run time to improve the search efficiency and convergence speed. Then, an elitist learning strategy is performed when the evolutionary state is classified as convergence state. The strategy will act on the globally best particle to jump out of the likely local optima. The APSO has comprehensively bee...
Zhi-hui Zhan, Jun Zhang
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Authors Zhi-hui Zhan, Jun Zhang
Comments (0)