Adaptive Particle Swarm Optimization
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.
Reference: Adaptive Particle Swarm Optimization, Zhi-Hui Zhan, Student Member, IEEE, Jun Zhang, Senior Member, IEEE, Yun Li, Member, IEEE, and Henry Shu-Hung Chung, Senior Member, IEEE.
