Fast convergence strategy for Particle Swarm Optimization using spread factor

Particle Swarm Optimization (PSO) is a relatively new evolutionary computation technique compared to the more established ones like Genetic Algorithms, Evolution Strategies and Genetic Programming. In this study, a new parameter referred to as the spread factor is introduced so as to speed up the PSO convergence. This factor continuously modifies the inertia weight of the PSO velocity equation during the search process by measuring the distribution of particles around the global best particle. Test results show that the spread factor enables the PSO to achieve a good balance between exploration and exploitation. Consequently, escape from local optima and fast convergence to global optima can be guaranteed. This is due to the ability of the algorithm to maintain the search momentum especially when some particles are trapped at local optima, and to expedite convergence once all particles are within the vicinity of the global optima. The test results presented here illustrate the improvement of this adaptive approach over methods using either fixed or linearly decreasing inertia weights.