Controlling the Parameters of the Particle Swarm Optimization with a Self-Organized Criticality Model

This paper investigates a Particle Swarm Optimization (PSO) with a Self-Organized Criticality (SOC) strategy that controls the parameter values and perturbs the position of the particles. The algorithm uses a SOC system known as Bak-Sneppen for establishing the inertia weight and acceleration coefficients for each particle in each time-step. Besides adjusting the parameters, the SOC model may be also used to perturb the particles' positions, thus increasing exploration and preventing premature convergence. The implementation of both schemes is straightforward and does not require hand-tuning. An empirical study compares the Bak-Sneppen PSO (BS-PSO) with other PSOs, including a state-of-the-art algorithm with dynamic variation of the weight and perturbation of the particles. The results demonstrate the validity of the algorithm.