This paper presents a performance evaluation of hybrid Simulated Kalman Filter Gravitational Algorithm (SKF-GSA), and hybrid Simulated Kalman Filter Particle Swarm Optimization (SKF-PSO), 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. The performance of the hybrid algorithms (SKF-GSA and SKF-PSO) is compared using CEC2014 benchmark dataset for continuous numerical optimization problems. Based on the analysis of experimental results, we found that the SKF-PSO performs the best among all.