A New Hierarchical Structure for Combining Different Versions of PSO

Abstract

Particle swarm optimization is a population-based algorithm and used for optimization in a wide range of problems. In this article, a method that is called Hybrid Particle Swarm Optimization or HPSO is proposed. It is composed of some versions of particle swarm optimization algorithms, which have subgroups in their structures. They are DMS-PSO, PS2OS and MCPSO. In fact, a hierarchical structure is used to compose a new version of optimization algorithm and combine the results of other structures of PSO. Proposed structure has been tested on four unimodal and four multimodal test functions. Although the memory usage has no difference with other compared versions, it is much faster in many cases. Also the rank of fitness values, are good and suitable in all test functions. In addition, it is possible to execute it concurrently.