Oppositional Learning Prediction Operator with Jumping Rate for Simulated Kalman Filter
Badaruddin Muhammad, Zuwairie Ibrahim, Mohd Ibrahim Shapiai, Mohd Saberi Mohamad, Kamil Zakwan Mohd Azmi, Mohd Falfazli Mat Jusof · 2019
Simulated Kalman filter (SKF) is among the new generation of metaheuristic optimization algorithm established in 2015. In this study, we introduce a prediction operator in SKF to prolong its exploration and to avoid premature convergence. The proposed prediction operator is based on oppositional learning with a jumping rate. The results show that using CEC2014 as benchmark problems, the SKF algorithm with oppositional learning prediction operator with jumping rate outperforms the original SKF algorithm in most cases.