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Neural Computing and Applications

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13.03.2018 | Original Article

Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking

verfasst von: Hathiram Nenavath, Ravi Kumar Jatoth

Erschienen in: Neural Computing and Applications | Ausgabe 9/2019

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Abstract

A novel optimization algorithm called hybrid sine–cosine algorithm with teaching–learning-based optimization algorithm (SCA–TLBO) is proposed in this paper, for solving optimization problems and visual tracking. The proposed hybrid algorithm has better capability to escape from local optima with faster convergence than the standard SCA and TLBO. The effectiveness of this algorithm is evaluated using 23 benchmark functions. Statistical parameters are employed to observe the efficiency of the hybrid SCA–TLBO qualitatively, and results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms. The hybrid SCA–TLBO algorithm is applied for visual tracking as a real thought-provoking case study. The hybrid SCA–TLBO-based tracking framework is used to experimentally measure object tracking error, absolute error, tracking detection rate, root mean square error and time cost as parameters. To reveal the capability of the proposed algorithm, a comparison of hybrid SCA–TLBO-based tracking framework and other trackers, viz. alpha–beta filter, linear Kalman filter and extended Kalman filter, particle filter, scale-invariant feature transform, particle swarm optimization and bat algorithm, is presented.

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Titel: Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking
verfasst von: Hathiram Nenavath
Ravi Kumar Jatoth
Publikationsdatum: 13.03.2018
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 9/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3376-6

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