Evolution control in the surrogate-assisted evolutionary and other meta-heuristic optimization algorithms is essential for their success in efficiently achieving the global optimum. In order to further reduce the number of fitness evaluations, a similarity-based evolution control method is introduced into the fitness estimation strategy for particle swarm optimization (FESPSO) [1]. In the proposed method, the fitness of a particle is either estimated or evaluated, depending on its similarity to the particle whose fitness is known. The performance of the proposed algorithm is examined on eight benchmark problems, and the simulation results show that the proposed algorithm is highly competitive on reducing the number of required fitness evaluations using the computationally expensive fitness function.