Skip to main content

Advertisement

Log in

hPSO-SA: hybrid particle swarm optimization-simulated annealing algorithm for relay node selection in wireless body area networks

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In the modern world, wireless body area networks (WBANs) play an essential role in psychological and biomedical applications. The use of WBANs in medical applications is limited due to various issues related to the sensors, viz., irregularity in data production, replacement and recharging of their batteries and the energy consumed by the networks. This manuscript addresses how these problems can be solved along with optimization of the energy consumption through efficient design of the system by applying routing protocols and heuristic-based optimization algorithms. In this paper, the particle swarm optimization (PSO) algorithm is a heuristic search algorithm that relies on an upgrade mechanism of the velocity and position of swarms. Although PSO has excellent exploration capability in global search, it becomes quickly stuck in local minima. To enhance the local search function of the current PSO algorithm, a simulated annealing (SA) algorithm has been incorporated in the exploitation phase. The newly developed hybrid PSO-SA (hPSO-SA) algorithm is validated with other state-of-the-art nature-inspired algorithms on eighteen benchmarks and five real engineering design problems. The statistical results of the proposed hPSO-SA algorithm are promising and indicate very good efficiency. The paper also aims at the application of the proposed algorithm to the WBAN design problem for minimization of the energy consumption through better selection of the relay node. The proposed hPSO-SA algorithm outperforms twelve other metaheuristic algorithms, taking hybrid variants for comparison.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Cai X, Li J, Yuan J et al (2014) Energy-aware adaptive topology adjustment in wireless body area networks. Telecommun Syst 58:139–152

    Google Scholar 

  2. Elias J (2014) Optimal design of energy-efficient and cost-effective wireless body area networks. Ad Hoc Netw 13:560–574

    Google Scholar 

  3. Javaid N, Ahmad A, Nadeem Q, Imran M, Haider N (2015) IM-SIMPLE: IMproved stable increased-throughput multi-hop link efficient routing protocol for wireless body area networks. Comput Human Behav 51:1003–1011

    Google Scholar 

  4. Wang J, Cho J, Lee S et al (2010) Hop-based energy aware routing algorithm for wireless sensor networks. IEICE Trans Commun E93–B:305–316

    Google Scholar 

  5. Tauqir A, Javaid N, Akram S et al (2013) Distance aware relaying energy-efficient: DARE to monitor patients in multi-hop body area sensor networks. In: Proceedings - 2013 8th international conference on broadband, wireless computing, communication and applications, BWCCA 2013. Compiegne, France, pp 206–213

  6. Jing L, Ming L, Bin Y, Wenlong L (2015) A novel energy efficient MAC protocol for wireless body area network. China Commun 12:11–20

    Google Scholar 

  7. Yuan X, Li C, Yang L, Yue W, Zhang B, Ullah S (2016) A token-based dynamic scheduled MAC protocol for health monitoring. EURASIP J Wirel Commun Netw 2016:125

    Google Scholar 

  8. Kim J, Song I, Choi S (2015) Priority-based adaptive transmission algorithm for medical devices in wireless body area networks (WBANs). J Cent South Univ 22:1762–1768

    Google Scholar 

  9. Vimalarani C, Subramanian R, Sivanandam SN (2016) An enhanced PSO-based clustering energy optimization algorithm for wireless sensor network. Sci World J 2016:1–12

    Google Scholar 

  10. Dhadwal MK, Jung SN, Kim CJ (2014) Advanced particle swarm assisted genetic algorithm for constrained optimization problems. Comput Optim Appl 58:781–806

    MathSciNet  MATH  Google Scholar 

  11. Xu G, Wang M (2014) An energy-efficient routing mechanism based on genetic ant Colony algorithm for wireless body area networks. J Networks 9:3366–3372

    Google Scholar 

  12. D’Andreagiovanni F, Nardin A (2015) Towards the fast and robust optimal design of wireless body area networks. Appl Soft Comput J 37:971–982

    Google Scholar 

  13. Sangwan A, Bhattacharya PP (2016) An optimization to routing approach under WBAN architectural constraints. Intell Syst Technol Appl 385:75–89

    Google Scholar 

  14. Kaur N, Singh S (2017) Optimized cost effective and energy efficient routing protocol for wireless body area networks. Ad Hoc Netw 61:65–84

    Google Scholar 

  15. Qais MH, Hasanien HM, Alghuwainem S, Nouh AS (2019) Coyote optimization algorithm for parameters extraction of three- diode photovoltaic models of photovoltaic modules. Energy 187:116001

    Google Scholar 

  16. Hayyolalam V, Asghar A, Kazem P (2020) Engineering applications of artificial intelligence black widow optimization algorithm : a novel meta-heuristic approach for solving engineering optimization problems. Eng Appl Artif Intell 87:103249

    Google Scholar 

  17. Gao W (2020) New ant Colony optimization algorithm for the traveling salesman problem. Int J Comput Intell Syst 13:44–55

    Google Scholar 

  18. Garg H (2016) A hybrid PSO-GA algorithm for constrained optimization problems. Appl Math Comput 274:292–305

    MathSciNet  MATH  Google Scholar 

  19. Asghar A, Mirjalili S, Faris H, Aljarah I (2019) Harris hawks optimization : algorithm and applications. Futur Gener Comput Syst 97:849–872

    Google Scholar 

  20. Wu T, Lin C (2015) Low-SAR path discovery by particle swarm optimization algorithm in wireless body area networks. IEEE Sensors J 15:928–936

    Google Scholar 

  21. Yan J, Peng Y, Shen D, Yan X, Deng Q (2018) An artificial bee colony-based green routing mechanism in WBANs for sensor-based E-healthcare systems. Sensors 18:3268

    Google Scholar 

  22. Agnihotri A, Gupta IK (2018) A hybrid PSO-GA algorithm for routing in wireless sensor network. In: In 2018 4th international conference on recent advances in information technology. IEEE, Dhanbad, pp 1–6

  23. Bilandi N, Verma HK, Dhir R (2019) PSOBAN : a novel particle swarm optimization based protocol for wireless body area networks. SN Appl Sci 1:1492

    Google Scholar 

  24. Samal TK, Patra SC, Kabat MR (2019) An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks. J King Saud Univ – Comput Inf Sci:14

  25. Raj AS, Chinnadurai M (2020) Energy efficient routing algorithm in wireless body area networks for smart wearable patches. Comput Commun 153:85–94

    Google Scholar 

  26. Singh P, Satvir M (2019) Improved artificial bee colony metaheuristic for energy-efficient clustering in wireless sensor networks. Artif Intell Rev 51:329–354

    MATH  Google Scholar 

  27. Mittal N (2019) Moth flame optimization based energy efficient stable clustered routing approach for wireless sensor networks. Wirel Pers Commun 104:677–694

    Google Scholar 

  28. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82

    Google Scholar 

  29. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Perth A (ed) Proceedings of the IEEE international conference on neural networks, pp 1942–1948

  30. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248

    MATH  Google Scholar 

  31. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82

    Google Scholar 

  32. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Google Scholar 

  33. Cuevas E, Echavarría A, Ramírez-Ortegón MA (2014) An optimization algorithm inspired by the states of matter that improves the balance between exploration and exploitation. Appl Intel 40:256–272

    Google Scholar 

  34. Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, pp 65–74

  35. Yang X-S (2012) Flower pollination algorithm for global optimization. In: Unconventional computation and natural computation. Springer, Berlin, pp 240–249

  36. Yang X (2012) Multiobjective firefly algorithm for continuous optimization, pp 13–15

  37. algorithm YXF (2010) Levy flights and global optimization. Swarm Intell bio-inspired Comput 2013:49–72

    Google Scholar 

  38. Holland J (1992) Adaptation in natural and artificial systems. MIT Press, Cambridge

    Google Scholar 

  39. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowledge-Based Syst 89:228–249

    Google Scholar 

  40. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513

    Google Scholar 

  41. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  42. Jamili A, Shafia MA (2011) A hybrid algorithm based on particle swarm optimization and simulated annealing for a periodic job shop scheduling problem. Int J Adv Manuf Technol 54:309–322

    Google Scholar 

  43. Wang X, Li J (2004) Hybrid particle swarm optimization with simulated annealing. In: Proceedings of the third international conference on machine learning and cybernetics, Shanghai, In, pp 26–29

  44. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. 1998 IEEE Int Conf Evol Comput proceedings IEEE World Congr Comput Intell (Cat No98TH8360), pp 69–73

  45. Url S, Archive TJ, Archive T (2007) Optimization by simulated annealing. Science (80) 220:671–680

    MathSciNet  Google Scholar 

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

    Google Scholar 

  47. Mareli M, Twala B (2017) An adaptive Cuckoo search algorithm for optimisation. Appl Comput Informatics 14:107–115

  48. Yang XS (2008) Firefly algorithm. Eng Optim pp 20:79–90

    Google Scholar 

  49. Kazarlis SA, Bakirtzis AG, Petridis V (1996) A genetic algorithm solution to the unit commitment problem. IEEE Trans Power Syst 11:83–92

    Google Scholar 

  50. Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10:629–640

    Google Scholar 

  51. 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 J 13:2592–2612

    Google Scholar 

  52. Hossein A, Yang GX (2013) Cuckoo search algorithm : a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35

    Google Scholar 

  53. Taylor P, Ray T, Saini P (2007) Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng Optim 33:735–748

    Google Scholar 

  54. Taylor P, Tsai J (2007) Global optimization of nonlinear fractional programming problems in engineering design. Eng Optim 37:399–409

    MathSciNet  Google Scholar 

  55. Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178:3043–3074

    Google Scholar 

  56. He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20:89–99

    Google Scholar 

  57. Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41:113–127

    Google Scholar 

  58. Taylor P, Mezura-montes E, Coello CAC (2008) An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 37:443–473

    MathSciNet  MATH  Google Scholar 

  59. Li LJ (2007) A heuristic particle swarm optimizer for optimization of pin connected structures. Comput Struct 85:340–349

    Google Scholar 

  60. Kaveh A (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput 27:155–182

    MATH  Google Scholar 

  61. Kannan BK, Gradient RC, Kramer SN (1994) An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization. J Mech Des 116:405–411

    Google Scholar 

  62. Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Des 112:223–229

    Google Scholar 

  63. Phillips DT (1976) Optimal design of a class of welded structures using geometric programming. J Manuf Sci Eng 98:1021–1025

    Google Scholar 

  64. Journal I, Numerical FOR, In M et al (1996) Structural optimization using a new local approximation method. Int J Numer Methods Eng 39:829–846

    MathSciNet  Google Scholar 

  65. Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112

    Google Scholar 

  66. Ravi KKGRSV (2019) Genetic algorithm based sensor node classifications in wireless body area networks (WBAN). Cluster Comput 22:12849–12855

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Naveen Bilandi.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Additional information

Publisher’s note

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

Appendix

Appendix

Table 22 Unimodal BFs
Table 23 Multimodal BFs
Table 24 Fixed dimension BFs
Fig. 14
figure 14

Pseudocode for Particle Swarm Optimization algorithm

Fig. 15
figure 15

Pseudocode for SA algorithm

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bilandi, N., Verma, H.K. & Dhir, R. hPSO-SA: hybrid particle swarm optimization-simulated annealing algorithm for relay node selection in wireless body area networks. Appl Intell 51, 1410–1438 (2021). https://doi.org/10.1007/s10489-020-01834-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-01834-w

Keywords

Navigation