Cluster Head Selection in Heterogeneous Wireless Sensor Network Using a New Evolutionary Algorithm


In wireless sensor network (WSN), limited energy resources with the nodes is a complex challenge as far as data routing, collecting and aggregating the data is concerned as all these processes are energy demanding. Network lifetime, stability period, and potential of the WSN are some of the parameters which are to be maximized subject to the constraints. The cluster head selection in the heterogeneous wireless sensor network has not been explored much and needs to be improved further to discover the potential of WSN in this area. In this study, optimal cluster head selection in heterogeneous wireless sensor network through Diversity-Driven Multi-Parent Evolutionary Algorithm with Adaptive Non-Uniform Mutation has been suggested. The efficacy of the proposed technique is tested on Classical Benchmark Functions, and obtained results are compared with the state of the art of algorithms. This algorithm is also validated on a heterogeneous wireless sensor network with cluster head selection as a multi-objective optimization problem. The residual energy of sensor node and distance travelled are to be optimized in order to minimize the fitness function. Simulation suggested that the proposed algorithm is found to be reliable and outperforms in terms of remaining energy of nodes, alive nodes versus round, dead nodes versus rounds, the lifespan of network, throughput, and stability period.

This is a preview of subscription content, access via your institution.

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


  1. 1.

    Potthuri, S., Shankar, T., & Rajesh, A. (2018). Lifetime improvement in wireless sensor networks using hybrid differential evolution and simulated annealing ( DESA ). Ain Shams Engineering Journal, 9(4), 655–663.

    Article  Google Scholar 

  2. 2.

    John, J., & Rodrigues, P. (2019). MOTCO: Multi-objective Taylor crow optimization algorithm for cluster head selection in energy aware wireless sensor network. Mobile Networks and Applications, 24(5), 1509–1525.

    Article  Google Scholar 

  3. 3.

    Kumar, D. (2013). Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET Wireless Sensor System, 4(1), 9–16.

    Article  Google Scholar 

  4. 4.

    Simon, G., et al. (2004). Sensor network-based countersniper system. In Proceedings of second international conference embeded networked sensor systems (Sensys), Balt. MD.

  5. 5.

    Yick, J., Mukherjee, B., & Ghosal, D. (2005). Analysis of a prediction-based mobility adaptive tracking algorithm. In 2nd international conference broadband networks, BROADNETS (vol. 2005, pp. 809–816).

  6. 6.

    Castillo-Effen, M., Quintela, D. H., Jordan, R., Westhoff, W., & Moreno, W. (2004). Wireless sensor networks for flash-flood alerting. In Proceedings of IEEE international caracas conference devices, circuits system ICCDCS (pp. 142–146).

  7. 7.

    Gao, T., Greenspan, D., Welsh, M., Juang, R. R., & Alm, A. (2005). Vital signs monitoring and patient tracking over a wireless network. In Annual international conference ieee engineering in medicine and biology proceedings (vol. 7, pp. 102–105).

  8. 8.

    Lorincz, K., et al. (2004). Sensor networks for emergency response: Challenges and opportunities. In IEEE pervasive computing pervasive computing first response (Special Issue).

  9. 9.

    Werner-Allen, G., et al. (2006). Deploying a wireless sensor network on an active volcano. IEEE Internet Computing, 10(2), 18–25.

    Article  Google Scholar 

  10. 10.

    Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

    Article  Google Scholar 

  11. 11.

    Bagci, F. (xxxx). Energy-efficient communication protocol for wireless microsensor networks. In Proceeding of 33rd Hawai international conference system science

  12. 12.

    Shepard, T. J. (xxxx). A Channel access scheme for large dense packet radio networks. In Proceeding of ACM SIGCOMM (pp. 219–230).

  13. 13.

    Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for Stochastic optimization. Future Generation Computer Systems, 2, 13.

    Google Scholar 

  14. 14.

    Mirjalili, S., Gandomi, A. H., Zahra, S., & Saremi, S. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191.

    Article  Google Scholar 

  15. 15.

    Faris, H., Mirjalili, S., Aljarah, I., Mafarja, M., & Heidari, A. A. (2020). Salp Swarm algorithm: Theory, literature review, and application in extreme learning machines. Studies in Computational Intelligence, 811, 185–199.

    Article  Google Scholar 

  16. 16.

    Wu, J., Nan, R., & Chen, L. (2019). Improved salp swarm algorithm based on weight factor and adaptive mutation. Journal of Experimental and Theoretical Artificial Intelligence, 00(00), 1–23.

    Article  Google Scholar 

  17. 17.

    Mirjalili, S. (2016). SCA: A Sine Cosine Algorithm for solving optimization problems. Knowledge-Based System, 96, 120–133.

    Article  Google Scholar 

  18. 18.

    Nenavath, H., Kumar, R., & Das, S. (2018). A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm and Evolutionary Computation, 43, 1–30.

    Article  Google Scholar 

  19. 19.

    Gupta, S., Deep, K., Mirjalili, S., & Hoon, J. (2020). A modified sine cosine algorithm with novel transition parameter and mutation operator for global optimization. Expert Systems with Applications, 2020, 113395.

    Article  Google Scholar 

  20. 20.

    Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems.

    Article  Google Scholar 

  21. 21.

    Kamboj, V. K., Nandi, A., Bhadoria, A., & Sehgal, S. (2020). An intensify Harris Hawks optimizer for numerical and engineering optimization problems. Applied Soft Computing, 89, 106018.

    Article  Google Scholar 

  22. 22.

    Mirjalili, S., Mohammad, S., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61.

    Article  Google Scholar 

  23. 23.

    Miao, Z., Yuan, X., Zhou, F., Qiu, X., Song, Y., & Chen, K. (2020). Grey wolf optimizer with an enhanced hierarchy and its application to the wireless sensor network coverage optimization problem. Applied Soft Computing Journal, 96(2020), 106602.

    Article  Google Scholar 

  24. 24.

    Al-betar, M. A., Awadallah, M. A., Faris, H., Aljarah, I., & Hammouri, A. I. (2018). Natural selection methods for Grey Wolf Optimizer. Expert Systems with Applications, 113, 481–498.

    Article  Google Scholar 

  25. 25.

    Golzari, S., Zardehsavar, M. N., Mousavi, A., Saybani, M. R., Khalili, A., & Shamshirband, S. (2018). KGSA: A gravitational search algorithm for multimodal optimization based on k-means niching technique and a novel elitism strategy. Open Mathematics, 16(1), 1582–1606.

    MathSciNet  Article  MATH  Google Scholar 

  26. 26.

    Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495–513.

    Article  Google Scholar 

  27. 27.

    Mirjalili, S., & Lewis, A. (2016). The Whale optimization algorithm. Advances in Engineering Software, 95, 51–67.

    Article  Google Scholar 

  28. 28.

    Jadhav, A. N., & Gomathi, N. (2018). WGC: Hybridization of exponential grey Wolf optimizer with whale optimization for data clustering. Alexandria Engineering Journal, 57(3), 1569–1584.

    Article  Google Scholar 

  29. 29.

    Zhou, W., Zhou, P., Wang, Y., & Wang, N. (2019). Station-keeping control of an underactuated stratospheric airship. International Journal of Fuzzy Systems, 21(3), 715–732.

    MathSciNet  Article  Google Scholar 

  30. 30.

    Singh, M., & Dhillon, J. S. (2016). Multiobjective thermal power dispatch using opposition-based greedy heuristic search. International Journal of Electrical Power and Energy Systems, 82, 339–353.

    Article  Google Scholar 

  31. 31.

    Yassein, L. (2009). Improvement on LEACH protocol of wireless sensor network (VLEACH). International Journal of Digital Content Technology and its Applications, 3(2), 132–136.

    Article  Google Scholar 

  32. 32.

    Mu, T., & Tang, M. (2010). LEACH-B: An improved LEACH protocol for wireless sensor network. In 2010 6th international conference wireless communication network mobile computing WiCOM 2010 (pp. 2–5).

  33. 33.

    Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.

    Article  Google Scholar 

  34. 34.

    Mirzaie, M., & Mazinani, S. M. (2017). Adaptive MCFL: An adaptive multi-clustering algorithm using fuzzy logic in wireless sensor network. Computer Communications, 111, 56–67.

    Article  Google Scholar 

  35. 35.

    Mahajan, S., Malhotra, J., & Sharma, S. (2014). An energy balanced QoS based cluster head selection strategy for WSN. Egyptian Informatics Journal, 15(3), 189–199.

    Article  Google Scholar 

  36. 36.

    Shankar, T. (xxxx). Whale optimization based energy-efficient cluster head selection algorithm for wireless sensor networks (pp. 1–22).

  37. 37.

    Guo, L., Li, Q., & Chen, F. (2011). A novel cluster-head selection algorithm based on hybrid Genetic Optimization for wireless sensor networks. Journal Networks, 6(5), 815–822.

    Article  Google Scholar 

  38. 38.

    Kuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.

    Article  Google Scholar 

  39. 39.

    Zeng, B., & Dong, Y. (2016). An improved harmony search based energy-efficient routing algorithm for wireless sensor networks. Applied Soft Computing Journal, 41, 135–147.

    Article  Google Scholar 

  40. 40.

    Karaboga, D., Okdem, S., & Ozturk, C. (2012). Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Networks, 18(7), 847–860.

    Article  Google Scholar 

  41. 41.

    Chen, R. C., Chang, W. L., Shieh, C. F., & Zou, C. C. (2012). Using hybrid artificial bee colony algorithm to extend wireless sensor network lifetime. In Proceeding 3rd international conference innovation bio-inspired computing application IBICA (pp. 156–161).

  42. 42.

    Kumar, R., & Kumar, D. (2016). Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wireless Networks, 22(5), 1461–1474.

    Article  Google Scholar 

  43. 43.

    Vinodhini, R., & Gomathy, C. (2020). MOMHR: A dynamic multi-hop routing protocol for WSN using Heuristic based multi-objective function. Wireless Personal Communication, 111(2), 883–907.

    Article  Google Scholar 

  44. 44.

    Ghugar, U., Pradhan, J., Bhoi, S. K., & Sahoo, R. R. (2019). LB-IDS: Securing wireless sensor network using protocol layer trust-based intrusion detection system. Journal Computing Networks Communication., 5, 71.

    Google Scholar 

  45. 45.

    Ghugar, U., Pradhan, J., & Kumar, S. (2018). PL-IDS: physical layer trust based intrusion detection system for wireless sensor networks. International Journal Information Technology, 10(4), 489–494.

    Article  Google Scholar 

  46. 46.

    Ranjan, R., Sudhabindu, S., Souvik, R., Sourav, S., & Bhoi, K. (2018). Guard against trust management vulnerabilities in Wireless Sensor Network. Arabian Journal for Science and Engineering, 43(12), 7229–7251.

    Article  Google Scholar 

  47. 47.

    Bhoi, S. K., Panda, S. K., & Khilar, P. M. (2013). A density-based clustering paradigm to detect faults in wireless sensor networks. In International conference on advances in computing (pp. 865–871).

  48. 48.

    Bhoi, S. K., Obaidat, M. S., Puthal, D., Singh, M., & Hsiao, K.-F. (2018). Software defined network based fault detection in industrial wireless sensor networks. In IEEE global communication conference (GLOBECOM) (pp. 1–6).

  49. 49.

    Singh, M., Bhoi, S. K., & Khilar, P. M. (2017). Geometric constraint-based range-free localization scheme for wireless sensor networks. IEEE Sensors Journal, 17(16), 5350–5366.

    Article  Google Scholar 

  50. 50.

    Swain, R. R., Khilar, P. M., & Bhoi, S. K. (2018). Heterogeneous fault diagnosis for wireless sensor networks. Ad Hoc Networks, 69, 15–37.

    Article  Google Scholar 

  51. 51.

    Chauhan, S., Singh, M., & Aggarwal, A. K. (2020). Diversity driven multi-parent evolutionary algorithm with adaptive non-uniform mutation. Journal of Experimental and Theoretical Artificial Intelligence, 2020, 1–32.

    Article  Google Scholar 

  52. 52.

    Ali, M. Z., Awad, N. H., Suganthan, P. N., Shatnawi, A. M., & Reynolds, R. G. (2018). An improved class of real-coded Genetic Algorithms for numerical optimization✰. Neurocomputing, 275, 155–166.

    Article  Google Scholar 

  53. 53.

    Wang, H., Wang, W., & Wu, Z. (2013). Particle Swarm optimization with adaptive mutation for multimodal optimization. Applied Mathematics and Computation, 221, 296–305.

    MathSciNet  Article  MATH  Google Scholar 

  54. 54.

    Jun, T., & Xiaojuan, Z. (2009). Particle swarm optimization with adaptive mutation. In 2009 WASE international conference information engineering ICIE 2009 (Vol. 2, No. 1, pp. 234–237).

  55. 55.

    Verma, S., Sood, N., & Sharma, A. K. (2019). Genetic algorithm-based Optimized Cluster Head selection for single and multiple data sinks in Heterogeneous Wireless Sensor Network. Applied Soft Computing Journal, 85, 105788.

    Article  Google Scholar 

  56. 56.

    Dhillon, J. S., Parti, S. C., & Kothari, D. P. (2001). Fuzzy decision making in multiobjective long-term scheduling of hydrothermal system.

  57. 57.

    Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceeding of sixth international symposium micro machine human science IEEE (pp. 39–43).

  58. 58.

    Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computing Design, 43(3), 303–315.

    Article  Google Scholar 

  59. 59.

    Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based System, 89, 228–249.

    Article  Google Scholar 

  60. 60.

    Storn, R. (1997) Differrential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. In Technical report, international computing science institution (Vol. 11).

  61. 61.

    Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers and Structures, 169, 1–12.

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Sumika Chauhan.

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

Verify currency and authenticity via CrossMark

Cite this article

Chauhan, S., Singh, M. & Aggarwal, A.K. Cluster Head Selection in Heterogeneous Wireless Sensor Network Using a New Evolutionary Algorithm. Wireless Pers Commun (2021).

Download citation


  • Evolutionary algorithm
  • Heterogeneous wireless sensor network
  • Cluster-head selection
  • Clustering
  • Fuzzy set theory