An Oppositional Learning Prediction Operator for Simulated Kalman Filter | IEEE Conference Publication | IEEE Xplore

An Oppositional Learning Prediction Operator for Simulated Kalman Filter


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 More

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.
Date of Conference: 28-30 July 2018
Date Added to IEEE Xplore: 13 May 2019
ISBN Information:
Conference Location: Hong Kong, China

References

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