In this paper, a novel population-based metaheuristic optimization algorithm, which is named as Simulated Kalman Filter (SKF), is introduced for global optimization problem. This new algorithm is inspired by the estimation capability of the well-known Kalman Filter. In principle, state estimation problem is regarded as an optimization problem and each agent in SKF acts as a Kalman Filter. An agent in the population finds solution to optimization problem using a standard Kalman Filter framework, which includes a simulated measurement process and a bestso- far solution as a reference. To evaluate the performance of the SKF algorithm, it is applied to 30 benchmark functions of CEC 2014 for real-parameter single-objective optimization problems. Statistical analysis is then carried out to rank SKF results to those obtained by other metaheuristic algorithms. The experimental results show that the proposed SKF algorithm is a promising approach and able to outperform some well-known metaheuristic algorithms, such as Genetic Algorithm, Particle Swarm Optimization, Black Hole Algorithm, and Grey Wolf Optimizer.