Inspired by the estimation capability of Kalman filter, we have recently introduced novel estimation-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering and measurement process, every agent estimates the global minimum/maximum. Measurement, which is required in Kalman filtering, is mathematically modelled and simulated. Agents communicate among them to update and improve the solution during the search process. However, the SKF is only capable to solve continuous numerical optimization problem. In order to solve combinatorial optimization problems, three extended versions of SKF algorithm, which is termed as Angle Modulated SKF (AMSKF), Distance Evaluated SKF (DESKF), and Binary SKF (BSKF), are proposed. A set of traveling salesman problems is used to evaluate the performance of the proposed algorithms.