As a population-based meta-heuristic technique for global search, particle swarm optimization (PSO) performs quite well on a variety of problems. However, the requirement on a large number of fitness evaluations poses an obstacle for the PSO algorithm to be applied to solve complex optimization problems with computationally expensive objective functions. This paper extends a fitness estimation strategy for PSO (FESPSO) based on its search dynamics to reduce fitness evaluations using the real fitness function. In order to further save the fitness evaluations and improve the estimation accuracy, a similarity measure and a reliability measure are introduced into the FESPSO. The similarity measure is used to judge whether the fitness of a particle will be estimated or evaluated using the real fitness function, and the reliability measure is adopted to determine whether the approximated value will be trusted. Experimental results on six commonly used benchmark problems show the effectiveness and competitiveness of our proposed algorithm. Preliminary empirical analysis of the search behavior is also performed to illustrate the benefit of the proposed estimation mechanism.