Supervisor-student model in particle swarm optimization

Particle swarm optimization (PSO) algorithms have exhibited good performance on well-known numerical test problems. In this paper, we propose a supervisor-student model in particle swarm optimization (SSM-PSO) that may further reduce computational cost in two aspects. On the one hand, it introduces a new parameter, called momentum factor, into the position update equation, which can restrict the particles inside the defined search space without checking the boundary at every iteration. On the other hand, relaxation-velocity-update strategy that is to update the velocities of the particles as few times as possible during the run, is employed to reduce the computational cost for evaluating the velocity. Comparisons with the linear decreasing weight PSO on three benchmark functions indicate that SSM-PSO not only greatly reduces the computational cost for updating the velocity, but also exhibit good performance.