A novel improvement of particle swarm optimization using Dual Factors strategy

The particle swarm optimization, inspired by nature, is widely used for optimizing complex problems and achieves many good stories in practical applications. However, the traditional PSO only focuses on the function value during evolutionary process. It ignores the information of distance between particles and potential regions. A Dual Factors Particle Swarm Optimization (DFPSO) incorporating both of distance and function information is proposed in this paper to help PSO in finding potential global optimal regions. The strategy of the DFPSO increases the diversity of population to yield improved results. The experimental results manifest that the performance, including accuracy and speed, are improved.