Simulated kalman filter: a novel estimation-based metaheuristic optimization algorithm

Abstract

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 framewo rk, which includes a simulated measurement process and a best - so - 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. S tatistical analysis is then carried out to rank SKF results to those obtained by other metaheuristic algo rithms. The experimental results show that the proposed SKF algorithm is a promising approach and able to outperform some well - known metaheuristic algo rithms , such as Genetic Algorithm, Particle Swarm Optimization, Black Hole Algor ithm, and Grey Wolf Optimizer.