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

  • Hathiram Nenavath
  • Ravi Kumar Jatoth
Original Article


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.


Metaheuristic Global optimization Visual tracking Population-based algorithm 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Sullivan KA, Jacobson SH (2001) A convergence analysis of generalized hill climbing algorithms. IEEE Trans Autom Control 46(8):1288–1293. MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Paravati G, Sanna A, Pralio B, Lamberti F (2009) A genetic algorithm for target tracking in FLIR video sequences using intensity variation function. IEEE Trans Instrum Meas 58(10):3457–3467. CrossRefGoogle Scholar
  3. 3.
    Fonseca CM, Fleming PJ (1995) An overview of evolutionary algorithms in multi objective optimization. Evol Comput 3:1–16. CrossRefGoogle Scholar
  4. 4.
    Biswas A, Mishra KK, Tiwari S, Misra AK (2013) Physics-inspired optimization algorithms: a survey. J Optim. Google Scholar
  5. 5.
    Parpinelli RS, Lopes HS (2011) New inspirations in swarm intelligence: a survey. Int J Bioinspired Comput 3:1–16. CrossRefGoogle Scholar
  6. 6.
    Li R et al (2013) Mixed integer evolution strategies for parameter optimization. Evol Comput 21(1):29–64. MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chakraborty G (1999) Genetic programming for a class of constrained optimization problems. In: 1999 IEEE international conference on systems, man, and cybernetics, 1999. IEEE SMC’99 Conference Proceedings, vol 1, Tokyo, pp 314–319.
  8. 8.
    Dasgupta D, Zbigniew M (2013) Evolutionary algorithms in engineering applications. Springer Science & Business Media, Berlin. zbMATHGoogle Scholar
  9. 9.
    Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713. CrossRefGoogle Scholar
  10. 10.
    Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–224813. CrossRefzbMATHGoogle Scholar
  11. 11.
    Rutenbar RA (1989) Simulated annealing algorithms: an overview. IEEE Circuits Devices Mag 5(1):19–26. CrossRefGoogle Scholar
  12. 12.
    Kumar Singh H, Isaacs A, Ray T, Smith W (2008) A simulated annealing algorithm for constrained multi-objective optimization. In: 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence), Hong Kong, pp 1655–1662.
  13. 13.
    Du H, Wu X, Zhuang J (2006) Small-world optimization algorithm for function optimization. Adv Nat Comput. Google Scholar
  14. 14.
    Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112–113:283–294CrossRefGoogle Scholar
  15. 15.
    Formato RA (2007) Central force optimization: a new metaheuristic with applications in applied electromagnetics. Prog Electromagn Res 77:425–491. CrossRefGoogle Scholar
  16. 16.
    Alatas B (2011) ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst Appl 38(10):13170–13180. CrossRefGoogle Scholar
  17. 17.
    Shah-Hosseini H (2011) Principal components analysis by the galaxy based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 6:132–140. CrossRefGoogle Scholar
  18. 18.
    Kaveh A, Mahdavi VR (2014) Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27CrossRefGoogle Scholar
  19. 19.
    Kanagaraj G, Ponnambalam SG, Loo CK (2015) Charged system search algorithm for robotic drill path optimization. In: 2015 international conference on advanced mechatronic systems (ICAMechS), Beijing, pp 125–130.
  20. 20.
    Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the 6th international symposium on micro machine and human science, pp 39–43.
  21. 21.
    Dorigo M, Birattari M (2010) Ant colony optimization. Encyclopedia of machine learning. Springer, Berlin, pp 36–39Google Scholar
  22. 22.
    Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behaviour. Neural Comput Appl 24:1867–1877. CrossRefGoogle Scholar
  23. 23.
    Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68CrossRefGoogle Scholar
  24. 24.
    Rao RV, Savsani VJ, Vakharia DP (2011) Teaching learning based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315. CrossRefGoogle Scholar
  25. 25.
    Rao RV, Savsani VJ, Vakharia DP (2012) Teaching learning based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183(1):1–15. MathSciNetCrossRefGoogle Scholar
  26. 26.
    He S, Wu QH, Saunders JR (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13(5):973–990. CrossRefGoogle Scholar
  27. 27.
    Fanni A, Manunza A, Marchesi M, Pilo F (1998) Tabu search metaheuristics for global optimization of electromagnetic problems. IEEE Trans Magn 34(5):2960–2963. CrossRefGoogle Scholar
  28. 28.
    Hosseini SM, Al Khaled A (2014) A survey on the Imperialist Competitive Algorithm metaheuristic: implementation in engineering domain and directions for future research. Appl Soft Comput 24:1078–1094CrossRefGoogle Scholar
  29. 29.
    Eita MA, Fahmy MM (2014) Group counseling optimization. Appl Soft Comput 22:585–604CrossRefGoogle Scholar
  30. 30.
    Kashan AH (2011) An efficient algorithm for constrained global optimization and application to mechanical engineering design: league championship algorithm (LCA). Comput Aided Des 43(12):1769–1792. CrossRefGoogle Scholar
  31. 31.
    Moosavian N, Roodsari BK (2014) Soccer league competition algorithm: a novel meta-heuristic algorithm for optimal design of water distribution networks. Swarm Evol Comput 17:14–24CrossRefGoogle Scholar
  32. 32.
    Ghorbani N, Babaei E (2016) Exchange market algorithm for economic load dispatch. Int J Electr Power Energy Syst 75:19–27CrossRefGoogle Scholar
  33. 33.
    Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13(5):2592–2612CrossRefGoogle Scholar
  34. 34.
    Ramezani F, Lotfi S (2013) Social-based algorithm (SBA). Appl Soft Comput 13(5):2837–2856CrossRefGoogle Scholar
  35. 35.
    Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. Adv Swarm Intell. Google Scholar
  36. 36.
    Dai C, Zhu Y, Chen W (2007) Seeker optimization algorithm. Comput Intell Sec. Google Scholar
  37. 37.
    Blum C, Roli A (2008) Hybrid meta-heuristics: an introduction. Hybrid Metaheuristics. CrossRefGoogle Scholar
  38. 38.
    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. CrossRefGoogle Scholar
  39. 39.
    Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133CrossRefGoogle Scholar
  40. 40.
    Crepinsek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv. zbMATHGoogle Scholar
  41. 41.
    Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102. CrossRefGoogle Scholar
  42. 42.
    Jamil M, Yang XS (2013) A literature survey of benchmark functions for global optimisation problems. Int J Math Model Numer Optim 4(2):150–194. zbMATHGoogle Scholar
  43. 43.
    Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18. CrossRefGoogle Scholar
  44. 44.
    Yang H, Shao L, Zheng F, Wang L, Song Z (2011) Recent advances and trends in visual tracking: a review. Neurocomputing 74:3823–3831. CrossRefGoogle Scholar
  45. 45.
    Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):1–45. CrossRefGoogle Scholar
  46. 46.
    Sokhandan A, Monadjemi A (2016) A novel biologically inspired computational framework for visual tracking task. Biol Inspired Cogn Archit 18:68–79Google Scholar
  47. 47.
    Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577. CrossRefGoogle Scholar
  48. 48.
    Hare S et al (2016) Struck: structured output tracking with kernels. IEEE Trans Pattern Anal Mach Intell 38(10):2096–2109. CrossRefGoogle Scholar
  49. 49.
    Yi S, Jiang N, Feng B, Wang X, Liu W (2016) Online similarity learning for visual tracking. Inf Sci 364–365:33–50CrossRefGoogle Scholar
  50. 50.
    Chen W, Zhang K, Liu Q (2016) Robust visual tracking via patch based kernel correlation filters with adaptive multiple feature ensemble. Neurocomputing 214:607–617CrossRefGoogle Scholar
  51. 51.
    Gao M-L, Yin L-J, Zou G-F, Li H-T, Liu W (2015) Visual tracking method based on cuckoo search algorithm. Opt Eng 54(7):073105CrossRefGoogle Scholar
  52. 52.
    Gao M-L, Shen J, Yin L-J, Liu W, Zou G-F, Li H-T, Gui-Xia Fu (2016) A novel visual tracking method using bat algorithm. Neurocomputing 177:612–619CrossRefGoogle Scholar
  53. 53.
    Crouse DF (2015) A general solution to optimal fixed-gain (αβ–γ etc) filters. IEEE Signal Process Lett 22(7):901–904CrossRefGoogle Scholar
  54. 54.
    Simon D (2010) Kalman filtering with state constraints: a survey of linear and nonlinear algorithms. IET Control Theory Appl 4(8):1303–1318MathSciNetCrossRefGoogle Scholar
  55. 55.
    Khan ZH, Gu IYH, Backhouse AG (2011) Robust visual object tracking using multi-mode anisotropic mean shift and particle filters. IEEE Trans Circuits Syst Video Technol 21(1):74–87CrossRefGoogle Scholar
  56. 56.
    Zhou H, Yuan Y, Shi C (2009) Object tracking using SIFT features and mean shift. Comput Vis Image Underst 113:345–352CrossRefGoogle Scholar
  57. 57.
    Thida M, Eng H-L, Monekosso DN, Remagnino P (2013) A particle swarm optimisation algorithm with interactive swarms for tracking multiple targets. Appl Soft Comput 13:3106–3117CrossRefGoogle Scholar
  58. 58.
    Wu Y, Lim JW, Yang MH (2015) Object tracking benchmark. IEEE Trans Pattern Anal 37(9):1834–1848CrossRefGoogle Scholar

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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