An Adaptive Particle Swarm Optimization Using Hybrid Strategy

As an intelligent algorithm inspired by the foraging behavior in nature, particle swarm optimization (PSO) is famous for its few parameters, easy to implement and higher convergence accuracy. However, PSO also has a weakness over the local search, also called the prematurity, which resulted in the convergence accuracy reduced and the convergence speed slowed. For this, extremal optimization (EO), an excellent local search algorithm, has been introduced to be improved (CEO) and enhance the local search of PSO. Meanwhile, for improving its global search further, an improved opposition-based learning based on refraction principle (UOBL) has been chosen to enhance the global search of PSO, which is a better global optimization algorithm. In order to balance both of PSO to improve its optimization performance further, an adaptive hybrid PSO based on UOBL and CEO (AHOPSO-CEO) is proposed in this article. The large number of experiment results and analysis reveals that AHOPSO-CEO achieves better performance with other algorithms on the convergence speed and convergence accuracy for optimization problems.