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

  • Hathiram Nenavath
  • Ravi Kumar Jatoth
Original Article
  • 131 Downloads

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.

Keywords

Metaheuristic Global optimization Visual tracking Population-based algorithm 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNational Institute of TechnologyWarangalIndia

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