Top

Evolutionary Intelligence

Published in:

11-02-2021 | Research Paper

A novel method to solve visual tracking problem: hybrid algorithm of grasshopper optimization algorithm and differential evolution

Authors: K. Narsimha Reddy, Polaiah Bojja

Published in: Evolutionary Intelligence | Issue 1/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

To provide effective resolutions for complex real-life problems and other optimization problems, abundant, various procedures have been presented in the last few decades. This paper proposes a simple but efficient hybrid evolutionary algorithm called GOA-DE for solving visual tracking problems. In the proposed hybrid algorithm, Grasshopper Optimization Algorithm (GOA) operates in refining the vector. In contrast, the Differential Evolution (DE) algorithm is used for transforming the decision vectors based on genetic operators. The improvement in maintaining the balance between exploration and exploitation abilities is made by incorporating genetic operators, namely, mutation and crossover in GOA. The success of GOA-DE is estimated by 23 classical benchmark functions, CEC05 functions, and CEC 2014 functions. The GOA-DE algorithm results prove that it is very viable associated with the metaheuristic up-to-date procedures. Similarly, visual tracking problems are resolved by the GOA-DE algorithm as a real challenging case study. Visual tracking several objects robustly in a video stream with complex backgrounds and objects are beneficial in subsequent generation computer vision structures. But, in exercise, it is problematic to plan an effective video-based visual tracking scheme owing to the fast-moving objects, probable occlusions, and diverse light circumstances. Investigational outcomes indicate that the GOA-DE-based tracker can energetically track a random target in many thought-provoking cases.

previous article A new metaheuristic algorithm based on water wave optimization for data clustering

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

John Holland H. (1992) Genetic algorithms, Sci Am a Div Nat Am Inc 267:1, pp 66–73

Dorigo M, Stützle T (2009) Ant colony optimization. Encycl Mach Learn 1(May):28–39. https://doi.org/10.1109/MCI.2006.329691CrossRefMATH

Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. https://doi.org/10.1023/A:1008202821328MathSciNetCrossRefMATH

Eberhart R, Kennedy J (1995) New optimizer using particle swarm theory. Proc Int Symp Micro Mach Hum Sci. pp 39–43. doi: https://doi.org/10.1109/mhs.1995.494215.

Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471. https://doi.org/10.1007/s10898-007-9149-xMathSciNetCrossRefMATH

Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. CAD Comput Aided Des 43(3):303–315. https://doi.org/10.1016/j.cad.2010.12.015CrossRef

Venkata Rao R (2016) Review of applications of tlbo algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decis Sci Lett. 5(1):1–30. https://doi.org/10.5267/j.dsl.2015.9.003MathSciNetCrossRef

Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006CrossRef

Rao RV (2016) Jaya : A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7:19–34. https://doi.org/10.5267/j.ijiec.2015.8.004CrossRef

10.

Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022CrossRef

11.

Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31. https://doi.org/10.1109/TEVC.2010.2059031CrossRef

12.

Das S, Mullick SS, Suganthan PN (2016) Recent advances in differential evolution—an updated survey. Swarm Evol Comput 27:1–30. https://doi.org/10.1016/j.swevo.2016.01.004CrossRef

13.

Squillero G, Tonda A (2016) Divergence of character and premature convergence: a survey of methodologies for promoting diversity in evolutionary optimization. Inf Sci (Ny) 329:782–799. https://doi.org/10.1016/j.ins.2015.09.056CrossRef

14.

Zhang C, Chen J, Xin B (2013) Distributed memetic differential evolution with the synergy of Lamarckian and Baldwinian learning. Appl Soft Comput J 13(5):2947–2959. https://doi.org/10.1016/j.asoc.2012.02.028CrossRef

15.

Zhang C, Ning J, Lu S, Ouyang D, Ding T (2009) A novel hybrid differential evolution and particle swarm optimization algorithm for unconstrained optimization. Oper Res Lett 37(2):117–122. https://doi.org/10.1016/j.orl.2008.12.008MathSciNetCrossRefMATH

16.

Basak A, Maity D, Das S (2013) A differential invasive weed optimization algorithm for improved global numerical optimization. Appl Math Comput 219(12):6645–6668. https://doi.org/10.1016/j.amc.2012.12.057MathSciNetCrossRefMATH

17.

Lin Q et al (2017) A local search enhanced differential evolutionary algorithm for sparse recovery. Appl Soft Comput J 57:144–163. https://doi.org/10.1016/j.asoc.2017.03.034CrossRef

18.

Liang JJ, Qu BY, Mao XB, Niu B, Wang DY (2014) Differential evolution based on fitness Euclidean-distance ratio for multimodal optimization. Neurocomput 137:252–260. https://doi.org/10.1016/j.neucom.2013.03.069CrossRef

19.

Zheng YJ, Xu XL, Ling HF, Chen SY (2015) A hybrid fireworks optimization method with differential evolution operators. Neurocomput 148:75–82. https://doi.org/10.1016/j.neucom.2012.08.075CrossRef

20.

Wang Q, Teng Z, Xing J, Gao J, Hu W, Maybank S (2018) Learning attentions: residual attentional siamese network for high performance online visual tracking. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. pp 4854–4863. doi: https://doi.org/10.1109/CVPR.2018.00510.

21.

Li B, Yan J, Wu W, Zhu Z, Hu X (2018) High performance visual tracking with siamese region proposal network. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. pp 8971–8980. doi: https://doi.org/10.1109/CVPR.2018.00935.

22.

Zhang Z, Peng H (2019) Deeper and wider siamese networks for real-time visual tracking. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. pp 4586–4595. doi: https://doi.org/10.1109/CVPR.2019.00472.

23.

Fan H, Ling H (2019) Siamese cascaded region proposal networks for real-time visual tracking. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. pp 7944–7953. doi: https://doi.org/10.1109/CVPR.2019.00814.

24.

Zhu Z, Wang Q, Li B, Wu W (2018) Distractor-aware siamese networks for visual object tracking (arXiv:1808.06048v1 [csCV]). Eccv pp 1–17

25.

Zhang Y, Wang L, Qi J, Wang D, Feng M, Lu H (2018) Structured siamese network for real-time visual tracking. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinf) 11213:355–370. https://doi.org/10.1007/978-3-030-01240-3_22CrossRef

26.

Li B, Wu W, Wang Q, Zhang F, Xing J, Yan J (2019) SIAMRPN++ Evolution of siamese visual tracking with very deep networks. In: Proceedings of the ieee conference on computer vision and pattern recognition, pp 4282–4291

27.

Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PHS (2016) Fully-convolutional siamese networks for object tracking. Lect Not Comput Sci (including Subser Lect Not Artif Intell Lect Not Bioinf) 9914:850–865. https://doi.org/10.1007/978-3-319-48881-3_56CrossRef

28.

Held D, Thrun S, Savarese S (2016) Learning to track at 100 FPS with deep regression networks . Lect Not Comput Sci Subser Lect Not Artif Intell Lect Not Bioinf 9905:749–765. https://doi.org/10.1007/978-3-319-46448-0_45CrossRef

29.

Tao R, Gavves E, Smeulders AWM (2016) Siamese instance search for tracking . Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 10:1420–1429. https://doi.org/10.1109/CVPR.2016.158CrossRef

30.

Bhat G, Danelljan M, Van Gool L, Timofte R (2019) Learning discriminative model prediction for tracking. Proc. IEEE Int Conf Comput Vis 6181–6190. doi: https://doi.org/10.1109/ICCV.2019.00628.

31.

Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417. https://doi.org/10.1109/TEVC.2008.927706CrossRef

32.

Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. Evol Comput IEEE Trans 10(6):646–657. https://doi.org/10.1109/TEVC.2006.872133CrossRef

33.

Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958. https://doi.org/10.1109/TEVC.2009.2014613CrossRef

34.

Gao ML, Li LL, Sun XM, Yin LJ, Li HT, Luo DS (2015) Firefly algorithm (FA) based particle filter method for visual tracking. Optik (Stuttg) 126(18):1705–1711. https://doi.org/10.1016/j.ijleo.2015.05.028CrossRef

35.

Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071. https://doi.org/10.1007/s10489-018-1190-6CrossRef

36.

Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466. https://doi.org/10.1016/j.jocs.2017.07.018CrossRef

37.

Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput Appl 32(16):12381–12401. https://doi.org/10.1007/s00521-020-04839-1CrossRef

38.

Nenavath H, Jatoth RK (2018) A novel object tracking method using binary bat algorithm. J Eng Appl Sci 13(4):3817–3825. https://doi.org/10.3923/jeasci.2018.3817.3825CrossRef

39.

Nenavath H, Jatoth RK (2018) A new method for ball tracking based on α-β, Linear Kalman and extended Kalman filters via bubble sort algorithm. Indones J Electr Eng Comput Sci 10(3):989–999. https://doi.org/10.11591/ijeecs.v10.i3.pp989-999CrossRef

40.

Narayana M, Nenavath H, Chavan S, Koteswara Rao L (2019) Intelligent visual object tracking with particle filter based on modified grey wolf optimizer. Optik Stuttg 193(3):162913. https://doi.org/10.1016/j.ijleo.2019.06.013CrossRef

41.

Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004CrossRef

42.

Parouha RP, Das KN (2016) A robust memory based hybrid differential evolution for continuous optimization problem. Knowledge-Based Syst 103:118–131. https://doi.org/10.1016/j.knosys.2016.04.004CrossRef

43.

Nenavath H, Kumar Jatoth DR, Das DS (2018) A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm Evol Comput 43(1):1–30. https://doi.org/10.1016/j.swevo.2018.02.011CrossRef

44.

Nenavath H, Jatoth RK (2018) Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking. Appl Soft Comput J 62:1019–1043. https://doi.org/10.1016/j.asoc.2017.09.039CrossRef

45.

Nenavath H, Jatoth RK (2019) Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking. Neural Comput Appl 31(9):5497–5526. https://doi.org/10.1007/s00521-018-3376-6CrossRef

46.

Niu J, Zhong W, Liang Y, Luo N, Qian F (2015) Fruit fly optimization algorithm based on differential evolution and its application on gasification process operation optimization. Knowledge-Based Syst 88:253–263. https://doi.org/10.1016/j.knosys.2015.07.027CrossRef

47.

Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12. https://doi.org/10.1016/j.compstruc.2016.03.001CrossRef

48.

Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008CrossRef

49.

Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007CrossRef

50.

Rahnamayan S, Tizhoosh HR, Salama MM (2008) Opposition-based differential evolution. Stud Comput Intell 143(1):155–171. https://doi.org/10.1007/978-3-540-68830-3_6CrossRef

51.

Deng C, Zhao B, Deng A, Hu R (2009) New differential evolution algorithm with a second enhanced mutation operator . Int Work Intell Syst Appl ISA 2009(50705039):1–4. https://doi.org/10.1109/IWISA.2009.5072971CrossRef

52.

Weber M, Tirronen V, Neri F (2010) Scale factor inheritance mechanism in distributed differential evolution. Soft Comput 14(11):1187–1207. https://doi.org/10.1007/s00500-009-0510-5CrossRef

53.

Zou D, Wu J, Gao L, Li S (2013) A modified differential evolution algorithm for unconstrained optimization problems. Neurocomput 120:469–481. https://doi.org/10.1016/j.neucom.2013.04.036CrossRef

54.

Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210. https://doi.org/10.1109/TEVC.2004.826074CrossRef

55.

T. Peram, K. Veeramachaneni, C. K. Mohan (2003) Fitness-distance-ratio based particle swarm optimization. IEEE Swarm Intell Symp SIS 2003 Proc 2:174–181

56.

Vandenbergh Engelbrecht FAP (2004) A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput 8(3):225–239. https://doi.org/10.1109/TEVC.2004.826069CrossRef

57.

Rao RV, Patel V (2012) An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput 3(4):535–560. https://doi.org/10.5267/j.ijiec.2012.03.007CrossRef

58.

Zou F, Wang L, Hei X, Chen D, Yang D (2014) Teaching-learning-based optimization with dynamic group strategy for global optimization. Inf Sci (Ny) 273:112–131. https://doi.org/10.1016/j.ins.2014.03.038CrossRef

59.

Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144. https://doi.org/10.1016/j.amc.2013.02.017MathSciNetCrossRefMATH

60.

Chen D, Zou F, Lu R, Wang P (2017) Learning backtracking search optimisation algorithm and its application. Inf Sci (Ny) 376:71–94. https://doi.org/10.1016/j.ins.2016.10.002CrossRef

61.

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. https://doi.org/10.1016/j.swevo.2011.02.002CrossRef

62.

Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://doi.org/10.1109/4235.585893CrossRef

63.

Wang J, Zhang W, Zhang J (2016) Cooperative differential evolution with multiple populations for multiobjective optimization. IEEE Trans Cybern 46(12):2848–2861. https://doi.org/10.1109/TCYB.2015.2490669CrossRef

64.

65.

Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577. https://doi.org/10.1109/TPAMI.2003.1195991CrossRef

66.

Khan ZH, Gu IY-H, Backhouse AG (2011) Robust visual object tracking using multi-mode anisotropic mean shift and particle filters. IEEE Trans Circuits Syst Video Technol 21(1):74–87CrossRef

67.

Thida M, Eng H, Monekosso DN, Remagnino P (2012) A particle swarm optimisation algorithm with interactive swarms for tracking multiple targets. Appl Soft Comput J 13(6):1–12

68.

Gao M-L et al (2016) A novel visual tracking method using bat algorithm. Neurocomput 177:612–619. https://doi.org/10.1016/j.neucom.2015.11.072CrossRef

Title: A novel method to solve visual tracking problem: hybrid algorithm of grasshopper optimization algorithm and differential evolution
Authors: K. Narsimha Reddy
Polaiah Bojja
Publication date: 11-02-2021
Publisher: Springer Berlin Heidelberg
Published in: Evolutionary Intelligence / Issue 1/2022
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI: https://doi.org/10.1007/s12065-021-00567-0

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 1/2022

Stochastic nonparallel hyperplane support vector machine for binary classification problems and no-free-lunch theorems

A multi-objective approach for solving transmission expansion planning problem considering wind power uncertainty

FAB classification of acute leukemia using an ensemble of neural networks

A novel disruption based symbiotic organisms search to solve economic dispatch

Metaheuristics on time series clustering problem: theoretical and empirical evaluation

Clustering method and sine cosine algorithm for image segmentation

Premium Partner