Skip to main content

Advertisement

SpringerLink
Log in

Application of hybrid metaheuristic with Levenberg-Marquardt algorithm for 6-dimensional magnetic localization

  • Original Paper
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

Wireless capsule endoscopies are being developed for gastrointestinal tract examination via magnetic tracking technology. These capsules make it possible to examine the patient’s digestive system without pain and easily diagnose diseases. However, one of the most important problems of these capsules is localization. This localization information includes 3-dimensional position and 3-dimensional orientation data from a set of magnetic sensors. These data must be obtained with the smallest error values. In recent years, metaheuristic algorithms have become popular in many fields due to their flexible nature. In this paper, the performances of frequently used algorithms such as artificial bee colony (ABC), differential evolution (DE), particle swarm optimization (PSO), teaching-learning based optimization (TLBO), genetic algorithm (GA), gravitational search algorithm (GSA), and whale optimization algorithm (WOA) are compared for magnetic localization problems. In addition, hybrid models combined with the Levenberg-Marquardt (LM) algorithm have been developed to increase their performance. In particular, while the PSO+LM algorithm is more successful than other algorithms, an adaptive version of this algorithm has been proposed to improve its performance further. Using the proposed version, the errors in the PSO+LM algorithm are further reduced and thus the localization efficiency of the capsules is increased.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Ackerman MJ (1998) The visible human project. Proc IEEE 86(3):504–511

    Google Scholar 

  • Aje OF, Josephat AA (2020) The particle swarm optimization (pso) algorithm application-a review. Glob J Eng Technol Adv 3(3):001–006

    Google Scholar 

  • Akay B, Karaboga D (2009) Parameter tuning for the artificial bee colony algorithm. In: International conference on computational collective intelligence, pp 608–619. Springer, Berlin

  • Aziz SM, Grcic M, Vaithianathan T (2008) A real-time tracking system for an endoscopic capsule using multiple magnetic sensors. In: Smart sensors and sensing technology, pp 201–218

  • Das S, Mullick SS, Suganthan PN (2016) Recent advances in differential evolution-an updated survey. Swarm Evol Comput 27:1–30

    Google Scholar 

  • de Moura Oliveira P, Oliveira J, Cunha JB (2017) Trends in gravitational search algorithm. In: International symposium on distributed computing and artificial intelligence, pp 270–277. Springer

  • Elbes M, Alzubi S, Kanan T, Al-Fuqaha A, Hawashin B (2019) A survey on particle swarm optimization with emphasis on engineering and network applications. Evol Intel 12(2):113–129

    Google Scholar 

  • Furlani EP (2001) Permanent magnet and electromechanical devices: materials, analysis, and applications. Academic Press, Cambridge

    Google Scholar 

  • Gu X, Angelov P, Rong H-J (2019) Local optimality of self-organising neuro-fuzzy inference systems. Inf Sci 503:351–380

    Google Scholar 

  • Guo X, Yan G, He W, Jiang P (2010) Improved modeling of electromagnetic localization for implantable wireless capsules. Biomed Instrum Technol 44(4):354–359

    Google Scholar 

  • Guo X, Wang C, Yan R (2011) An electromagnetic localization method for medical micro-devices based on adaptive particle swarm optimization with neighborhood search. Measurement 44(5):852–858

    Google Scholar 

  • Guo X, Lu Z, Cui H, Liu B, Jiang Q, Wang S (2018) Modelling and solving the position tracking problem of remote-controlled gastrointestinal drug-delivery capsules. Biomed Signal Process Control 39:213–218

    Google Scholar 

  • Hamzeh M, Vahidi B, Nematollahi AF (2018) Optimizing configuration of cyber network considering graph theory structure and teaching-learning-based optimization (gt-tlbo). IEEE Trans Ind Inf 15(4):2083–2090

    Google Scholar 

  • Kanaan M, Akay R, Suveren M (2018) In-body ranging for ultra-wide band wireless capsule endoscopy using neural networks based on particle swarm optimization. Selcuk Univ Muhendislik Bilim Ve Teknoloji Dergisi 6(2):207–217

    Google Scholar 

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Global Optim 39(3):459–471

    MathSciNet  MATH  Google Scholar 

  • Kaur G, Gill SS, Rattan M (2020) Whale optimization algorithm for performance improvement of silicon-on-insulator finfets. Int J Artif Intell 18(1):63–81

    Google Scholar 

  • Kuth R, Reinschke J, Rockelein R (2007) Method for determining the position and orientation of an endoscopy capsule guided through an examination object by using a navigating magnetic field generated by means of a navigation device. Google Patents. US Patent App. 11/481,935

  • Long W, Wu T, Jiao J, Tang M, Xu M (2020) Refraction-learning-based whale optimization algorithm for high-dimensional problems and parameter estimation of pv model. Eng Appl Artif Intell 89:103457

    Google Scholar 

  • Lourakis MI, Argyros AA (2009) Sba: a software package for generic sparse bundle adjustment. ACM Trans Math Softw (TOMS) 36(1):1–30

    MathSciNet  MATH  Google Scholar 

  • Lv B, Qin Y, Dai H, Su S (2021) Improving localization success rate of three magnetic targets using individual memory-based wo-lm algorithm. IEEE Sens J 2021:2

    Google Scholar 

  • Madsen K, Nielsen HB, Tingleff O (2004) Methods for non-linear least squares problems, 2nd edn. Springer, Berlin

    Google Scholar 

  • Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312

    Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  • Mishra DB, Bilgaiyan S, Mishra R, Acharya AA, Mishra S (2017) A review of random test case generation using genetic algorithm. Indian J Sci Technol 10:30

    Google Scholar 

  • Muhammad K, Khan S, Kumar N, Del Ser J, Mirjalili S (2020) Vision-based personalized wireless capsule endoscopy for smart healthcare: taxonomy, literature review, opportunities and challenges. Futur Gener Comput Syst 113:266–280. https://doi.org/10.1016/j.future.2020.06.048

    Google Scholar 

  • Nagy Z, Fluckiger M, Ergeneman O, Pane S, Probst M, Nelson BJ (2009) A wireless acoustic emitter for passive localization in liquids. In: 2009 IEEE international conference on robotics and automation, IEEE, pp 2593–2598

  • Osaba E, Villar-Rodriguez E, Del Ser J, Nebro AJ, Molina D, LaTorre A, Suganthan PN, Coello CAC, Herrera F (2021) A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems. Swarm Evol Comput 2021:100888

    Google Scholar 

  • Pant M, Zaheer H, Garcia-Hernandez L, Abraham A et al (2020) Differential evolution: a review of more than two decades of research. Eng Appl Artif Intell 90:103479

    Google Scholar 

  • Pawar S (2020) A survey on genetic algorithms. Int J Eng Res Appl 10(12):25–29

    Google Scholar 

  • Plotkin A, Kucher V, Horen Y, Paperno E (2008) A new calibration procedure for magnetic tracking systems. IEEE Trans Magn 44(11):4525–4528

    Google Scholar 

  • Precup R-E, David R-C, Petriu EM, Preitl S, Paul AS (2011) Gravitational search algorithm-based tuning of fuzzy control systems with a reduced parametric sensitivity. In: Soft computing in industrial applications, pp 141–150

  • Precup R-E, David R-C, Roman R-C, Szedlak-Stinean A-I, Petriu EM (2021) Optimal tuning of interval type-2 fuzzy controllers for nonlinear servo systems using slime mould algorithm. Int J Syst Sci 2021:1–16

    Google Scholar 

  • Ranganathan A (2004) The levenberg-marquardt algorithm. Tutoral LM Algor 11(1):101–110

    Google Scholar 

  • Rashedi E, Rashedi E, Nezamabadi-pour H (2018) A comprehensive survey on gravitational search algorithm. Swarm Evol Comput 41:141–158

    MATH  Google Scholar 

  • Riccioni ME, Urgesi R, Cianci R, Bizzotto A, Spada C, Costamagna G (2012) Colon capsule endoscopy: advantages, limitations and expectations which novelties? World J Gastrointest Endosc 4(4):99

    Google Scholar 

  • Sabri NM, Puteh M, Mahmood MR (2013) A review of gravitational search algorithm. Int J Adv Soft Comput Appl 5(3):1–39

    Google Scholar 

  • Schlageter V, Drljaca P, Popovic RS, KuČERA P (2002) A magnetic tracking system based on highly sensitive integrated hall sensors. JSME Int J Ser C 45(4):967–973

    Google Scholar 

  • Sengupta S, Basak S, Peters RA (2019) Particle swarm optimization: a survey of historical and recent developments with hybridization perspectives. Mach Learn Knowl Extract 1(1):157–191

    Google Scholar 

  • Silva J, Histace A, Romain O, Dray X, Granado B (2014) Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. Int J Comput Assist Radiol Surg 9(2):283–293

    Google Scholar 

  • Song S, Hu C, Li M, Yang W, Meng MQ-H (2009) Real time algorithm for magnet’s localization in capsule endoscope. In: 2009 IEEE international conference on automation and logistics, IEEE, pp 2030–2035

  • Suveren M (2015) Ultra wide band (uwb) wireless systems in implant body area networks and modelling of the distance measurement error. In: Master’s thesis, Mechatronic Engineering, Erciyes University, Kayseri

  • Suveren M, Kanaan M (2019) 5d magnetic localization for wireless capsule endoscopy using the levenberg-marquardt method and artificial bee colony algorithm. In: 2019 IEEE 30th international symposium on personal, indoor and mobile radio communications (PIMRC Workshops), IEEE, pp 1–6

  • Suveren M, Kanaan M (2021) Performance analysis of localization system for wireless robotic capsule endoscopy based on 5 dof. In: International Workshop IFToMM for Sustainable Development Goals, pp 335–344. Springer

  • Swain P, Iddan GJ, Meron G, Glukhovsky A (2001) Wireless capsule endoscopy of the small bowel: development, testing, and first human trials. In: Biomonitoring and endoscopy technologies, vol 4158, pp 19–23. International society for optics and photonics

  • Than TD, Alici G, Zhou H, Li W (2012) A review of localization systems for robotic endoscopic capsules. IEEE Trans Biomed Eng 59(9):2387–2399

    Google Scholar 

  • Transtrum MK, Sethna JP (2012) Improvements to the levenberg-marquardt algorithm for nonlinear least-squares minimization. arXiv:1201.5885

  • Transtrum MK, Machta BB, Sethna JP (2011) Geometry of nonlinear least squares with applications to sloppy models and optimization. Phys Rev E 83(3):036701

    Google Scholar 

  • Veček N, Liu S-H, Črepinšek M, Mernik M (2017) On the importance of the artificial bee colony control parameter ‘limit. Inf Technol Control 46(4):566–604

    Google Scholar 

  • Woods SP, Constandinou TG (2013) Wireless capsule endoscope for targeted drug delivery: mechanics and design considerations. IEEE Trans Biomed Eng 60(4):945–953. https://doi.org/10.1109/TBME.2012.2228647

    Google Scholar 

  • Xuan Z, Shanshan S, Jiansheng X (2019) A positioning system of capsule endoscope based on the combination of particle swarm optimization and lm algorithm. In: 2019 3rd international conference on electronic information technology and computer engineering (EITCE), IEEE, pp 1263–1267

  • Xudong G, Zhengping L, Qinfen J, Shuyi W, Haipo C (2018) Algorithm for tracking position and orientation of drug delivery capsules in gastrointestinal tract. J Syst Simul 30(6):2288

    Google Scholar 

  • Yang W, Hu C, Meng MQ-H, Song S, Dai H (2009) A six-dimensional magnetic localization algorithm for a rectangular magnet objective based on a particle swarm optimizer. IEEE Trans Magn 45(8):3092–3099. https://doi.org/10.1109/TMAG.2009.2019116

    Google Scholar 

  • Zou F, Chen D, Xu Q (2019) A survey of teaching-learning-based optimization. Neurocomputing 335:366–383

    Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge and thank the Scientific Research Projects Support Office of the Erciyes University, which has supported this work under grant number FDK-2018-7833.

Author information

Authors and Affiliations

  1. Department of Mechatronics Engineering, Erciyes University, Kayseri, 38280, Turkey

    Memduh Suveren, Rustu Akay & Muzaffer Kanaan

  2. Institute of Natural and Applied Sciences, Erciyes University, Kayseri, 38280, Turkey

    Mustafa Yusuf Yildirim

Authors
  1. Memduh Suveren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rustu Akay
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mustafa Yusuf Yildirim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muzaffer Kanaan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rustu Akay.

Ethics declarations

Conflict of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Suveren, M., Akay, R., Yildirim, M.Y. et al. Application of hybrid metaheuristic with Levenberg-Marquardt algorithm for 6-dimensional magnetic localization. Evolving Systems 13, 849–867 (2022). https://doi.org/10.1007/s12530-022-09418-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-022-09418-4

Keywords

Search

Navigation