Velocity self-adaptation made Particle Swarm Optimization faster

The lognormal self-adaptation has been used extensively in evolutionary programming (EP) and evolution strategies (ES) to adjust the search step size for each objective variable. The particle swarm optimization (PSO) relies on two kinds of factors: velocity and position of particles to generate better particles. In this paper, we propose self-adaptive velocity PSO (SAVPSO) in which we firstly introduce lognormal self-adaptation strategies to efficiently control the velocity of PSO. Extensive empirical studies have been carried out to evaluate the performance of SAVPSO, standard PSO and some other improved versions of PSO. From the experimental results on 7 widely used test functions, we can show that SAVPSO outperforms standard PSO.