Boost particle swarm optimization with fitness estimation

It is well known that the classical particle swarm optimization (PSO) is time-consuming when used to solve complex fitness optimization problems. In this study, we perform in-depth research on fitness estimation based on the distance between particles and affinity propagation clustering. In addition, support vector regression is employed as a surrogate model for estimating fitness values instead of using the objective function. The particle swarm optimization algorithm based on affinity propagation clustering, the efficient particle swarm optimization algorithm, and the particle swarm optimization algorithm based on support vector regression machine are then proposed. The experimental results show that the new algorithms significantly reduce the computational counts of the objective function. Compared with the classical PSO, the optimization results exhibit no loss of accuracy or stability.