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Motivated by the estimation capability of Kalman filter, a new meta-heuristic optimization algorithm known as Simulated Kalman Filter (SKF) has been introduced recently. According to the components of Kalman filtering, which includes prediction, measurement, and estimation, the global minimum/maximum can be estimated. Measurement process, which is needed in Kalman filtering, is mathematically modeled and simulated. Agents interact among them to modify and enhance the solution throughout the search process. Simultaneous Model Order and Parameter Estimation (SMOPE) and Simultaneous Model Order and Parameter Estimation based on Multi Swarm (SMOPE-MS) are two techniques of implementing meta-heuristic algorithm to iteratively establish an optimal model order and parameters simultaneously for an unknown system. The performance of SMOPE and SMOPE-MS has been examined through the utilization of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The objective of this paper is to test the effectiveness of SKF in solving system identification problem throughout SMOPE and SMOPE-MS. Experiments are conducted on six system identification problems. The obtained outcomes showed that the performance of SMOPE-MS(SKF) is better than SMOPE (SKF).