Adaptive Particle Swarm Optimization

This paper proposes an adaptive particle swarm optimization (APSO) with adaptive parameters and elitist learning strategy (ELS) based on the evolutionary state estimation (ESE) approach. The ESE approach develops an `evolutionary factor' by using the population distribution information and relative particle fitness information in each generation, and estimates the evolutionary state through a fuzzy classification method. According to the identified state and taking into account various effects of the algorithm-controlling parameters, adaptive control strategies are developed for the inertia weight and acceleration coefficients for faster convergence speed. Further, an adaptive `elitist learning strategy' (ELS) is designed for the best particle to jump out of possible local optima and/or to refine its accuracy, resulting in substantially improved quality of global solutions. The APSO algorithm is tested on 6 unimodal and multimodal functions, and the experimental results demonstrate that the APSO generally outperforms the compared PSOs, in terms of solution accuracy, convergence speed and algorithm reliability.