Abstract:
Simulated Kalman filter (SKF) is a recent metaheuristic optimization algorithm established in 2015. In the present study, we introduce a prediction operator in SKF to pro...Show MoreMetadata
Abstract:
Simulated Kalman filter (SKF) is a recent metaheuristic optimization algorithm established in 2015. In the present 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. The results show that using CEC2014 as benchmark problems, the SKF algorithm with oppositional learning prediction operator outperforms the original SKF algorithm in most cases.
Published in: 2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)
Date of Conference: 28-30 July 2018
Date Added to IEEE Xplore: 13 May 2019
ISBN Information: