Cubature Kalman Optimizer versus Teaching Learning Based Optimization: A Performance Comparison based on CEC2014 Test Suite

This paper compares a new Cubature Kalman Optimizer performance against the Teaching Learning Based Optimization in solving the CEC2014 test suite. The Cubature Kalman Optimizer is inspired by the estimation algorithm named Cubature Kalman filter, while the Teaching Learning Based Optimization is in...

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Bibliographic Details
Published in:International Journal of Membrane Science and Technology 2023-09, Vol.10 (3), p.1872-1884
Main Authors: Musa, Zulkifli, Ibrahim, Zuwairie, Shapiai, Mohd Ibrahim, Aziz, Nor Azlina Ab
Format: Article
Language:English
Online Access:Get full text
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Summary:This paper compares a new Cubature Kalman Optimizer performance against the Teaching Learning Based Optimization in solving the CEC2014 test suite. The Cubature Kalman Optimizer is inspired by the estimation algorithm named Cubature Kalman filter, while the Teaching Learning Based Optimization is inspired by the teaching-learning process in a classroom. Both algorithms can be characterized as a parameter-less nature. Graphical analysis based on convergence curve shows that Cubature Kalman Optimizer has better exploration than Teaching Learning Based Optimization in the first half of the total iteration that make it able to find better solution. On the other hand, for boxplot, both algorithms show comparative based on consistency. Meanwhile, statistical analysis shows that the Cubature Kalman Optimizer algorithm is a promising approach compared to Teaching Learning Based Optimization.
ISSN:2410-1869
2410-1869
DOI:10.15379/ijmst.v10i3.1847