What is Simulated Kalman Filter?
Post date: Apr 23, 2016 3:30:32 AM
Simulated Kalman Filter (SKF) is a novel estimation-based metaheuristic optimization algorithm. This algorithm is inspired by the estimation capability of the Kalman Filter. In principle, the 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 a solution to the optimization problem using a standard Kalman Filter framework with the addition of simulated measurement process and best-so-far solution as a reference. The SKF has been hybridized with other metaheuristic and has been extended for solving combinatorial optimization problems.
References:
Zuwairie Ibrahim, Nor Hidayati Abdul Aziz, Nor Azlina Ab. Aziz, Saifudin Razali, Mohd Ibrahim Shapiai, Sophan Wahyudi Nawawi, and Mohd Saberi Mohamad, A Kalman Filter Approach for Solving Unimodal Optimization Problems, ICIC Express Letters, 2015, Vol. 9, Issue 12, pp. 3415-3422.
Zuwairie Ibrahim, Nor Hidayati Abdul Aziz, Nor Azlina Ab. Aziz, Saifudin Razali, Mohd Saberi Mohamad, Simulated Kalman Filter: A Novel Estimation-Based Metaheuristic Optimization Algorithm, Advance Science Letters, Vol. 22, pp. 2941-2946, 2016.