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A Novel Particle Swarm Optimization Algorithm

In this study a novel memory based particle swarm optimization algorithm is presented. This algorithm utilizes external memory. A set of globally found best and worst positions, along with their parameters are stored in two separate external memories. At each iteration, a coefficient, based on the distance of the current particle to the closest best and closest worst particles is calculated. When updating the velocity component, this coefficient is added to the current velocity of the particle with a certain probability. Also randomized upper and lower bound values have been defined for the inertia component. The algorithm is tested on benchmark functions and it is shown empirically that it converges faster to the optima. It also outperforms the PSO and a recent improved PSO, as well as maintaining a superior precision in comparison. Convergence speed is particularly important since the method will be used in a realistic robot motion simulator environment in which the simulation time is long enough to make convergence speed a primary concern.