This paper presents a performance evaluation of a new hybrid Simulated Kalman Filter and Particle Swarm Optimization (SKFPSO), for continuous numerical optimization problems. Simulated Kalman filter (SKF) was inspired by the estimation capability of Kalman filter. Every agent in SKF is regarded as a Kalman filter. Inspired by the bird flocking, Particle Swarm Optimization (PSO), has been introduced in 1994. Four methods (models) to hybridize SKF and PSO are proposed in this paper. The performance of the hybrid SKF-PSO algorithms is compared against the original SKF using CEC2014 benchmark dataset for continuous numerical optimization problems. Based on the analysis of experimental results, we found that model 3 and model 4 are performed better than the original SKF.