An enhanced whale optimization algorithm with inertia weight and dynamic parameter adaptation for wireless sensor network deployment (original) (raw)

Abstract

The Whale Optimization Algorithm (WOA) has effectively solved various optimization problems with reasonable results, but it suffers from premature convergence and an imbalanced exploration–exploitation mechanism. To address these shortcomings, this paper presents an Enhanced Whale Optimization Algorithm (EWOA) with three key improvements: (1) adaptive adjustment of the coefficients to manage exploration behavior dynamically, (2) the addition of an inertia weight to ensure stable convergence, and (3) a proposed selection probability parameter to achieve global and exploitation balances. These improvements collectively enhance the algorithm’s convergence speed, accuracy, and resilience. The proposed EWOA is applied to optimize the deployment of Wireless Sensor Networks (WSNs). Various node density simulations prove that EWOA outperforms existing algorithms in terms of F-value, packet delivery ratio, throughput, and computational efficiency.

Access this article

Log in via an institution

Subscribe and save

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

Data availability

No datasets were generated or analysed during the current study.

References

  1. Khiadani N, Hendessi F (2025) An energy efficient prediction based protocol for target tracking in wireless sensor networks. Ad Hoc Netw 167:103688
    Article Google Scholar
  2. Chen L, Qiu Z, Wu Y, Tang Z (2024) Optimizing k-coverage in energy-saving wireless sensor networks based on the Elite Global Growth Optimizer. Expert Syst Appl 256:124878
    Article Google Scholar
  3. Webber JL et al (2023) An efficient intrusion detection framework for mitigating blackhole and sinkhole attacks in healthcare wireless sensor networks. Comput Electr Eng 111:108964
    Article Google Scholar
  4. Nasiri S, Shahabi S, Shafiesabet A, Talebbeidokhti M, Behineh EA (2024) Cybersecurity in action: unraveling the effects of individual, social, and organizational determinants. TEHNIČKI GLASNIK 20(2):1–10. https://doi.org/10.31803/tg-20240627004731
    Article Google Scholar
  5. Ahmad R, Alhasan W, Wazirali R and Aleisa N (2024) Optimization algorithms for wireless sensor networks node localization: an overview," IEEE Access.
  6. Mehbodniya A, Webber JL, Karupusamy S (2022) Improving the geo-drone-based route for effective communication and connection stability improvement in the emergency area ad-hoc network. Sustain Energy Technol Assess 53:102558
    Google Scholar
  7. Osamy W, Khedr AM, Salim A, Al Ali AI, El-Sawy AA (2022) Coverage, deployment and localization challenges in wireless sensor networks based on artificial intelligence techniques: a review. IEEE Access 10:30232–30257
    Article Google Scholar
  8. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
    Article Google Scholar
  9. Pourghebleh B, Hekmati N, Davoudnia Z, Sadeghi M (2022) A roadmap towards energy-efficient data fusion methods in the Internet of Things. Concurr Comput: Practice Exp 34(15):e6959
    Article Google Scholar
  10. Mehbodniya A, Bhatia S, Mashat A, Elangovan M, Sengan S (2022) Proportional fairness based energy efficient routing in wireless sensor network. Comput Syst Sci Eng. https://doi.org/10.32604/csse.2022.021529
    Article Google Scholar
  11. Di C, Li F, Li S (2020) Sensor deployment for wireless sensor networks: A conjugate learning automata-based energy-efficient approach. IEEE Wirel Commun 27(5):80–87
    Article Google Scholar
  12. Kumar P et al (2022) Machine learning enabled techniques for protecting wireless sensor networks by estimating attack prevalence and device deployment strategy for 5G networks. Wirel Commun Mob Comput 2022(1):5713092
    Article Google Scholar
  13. Mehbodniya A, Haq MA, Kumar A, Ismail ME, Dahiya P, Karupusamy S (2022) Data reinforcement control technique-based monitoring and controlling of environmental factors for IoT applications. Arab J Geosci 15(7):620
    Article Google Scholar
  14. Bhat SJ (2022) A localization and deployment model for wireless sensor networks using arithmetic optimization algorithm. Peer-to-Peer Netw Appl 15(3):1473–1485
    Article Google Scholar
  15. Mohar SS, Goyal S, Kaur R (2022) Optimum deployment of sensor nodes in wireless sensor network using hybrid fruit fly optimization algorithm and bat optimization algorithm for 3D environment. Peer-to-Peer Netw Appl 15(6):2694–2718
    Article Google Scholar
  16. Xie M, Pi D, Dai C, Xu Y (2024) A metaheuristic-based algorithm for optimizing node deployment in wireless sensor network. Neural Comput Appl 36:1–23
    Article Google Scholar
  17. Zhu J et al (2024) Deployment optimization in wireless sensor networks using advanced artificial bee colony algorithm. Peer-to-peer Netw Appl 17:1–12
    Article Google Scholar
  18. Wang J, Luo D, Chen W, Peng F, Li Z (2024) Deployment method of wireless sensor networks based on MaOEA/P-GM algorithm. Soft Comput. https://doi.org/10.1007/s00500-024-09850-5
    Article Google Scholar
  19. Rathee M, Kumar S, Dilip K, Dohare U (2024) Towards energy balancing optimization in wireless sensor networks: a novel quantum inspired genetic algorithm based sinks deployment approach. Ad Hoc Netw 153:103350
    Article Google Scholar
  20. Shu T, Pan Z, Ding Z, Zu Z (2024) Resource scheduling optimization for industrial operating system using deep reinforcement learning and WOA algorithm. Expert Syst Appl 255:124765
    Article Google Scholar
  21. Ma Y, Wang X, Meng W (2024) A reinforced whale optimization algorithm for solving mathematical optimization problems. Biomimetics 9(9):576
    Article Google Scholar
  22. Wu L, Xu D, Guo Q, Chen E, Xiao W (2024) A nonlinear randomly reuse-based mutated whale optimization algorithm and its application for solving engineering problems. Appl Soft Comput 167:112271
    Article Google Scholar
  23. Liang Z, Shu T, Ding Z (2024) A novel improved whale optimization algorithm for global optimization and engineering applications. Mathematics 12(5):636
    Article Google Scholar
  24. Qu S et al (2024) Application of spiral enhanced whale optimization algorithm in solving optimization problems. Sci Rep 14(1):24534
    Article Google Scholar
  25. Jia R and Zhang H (2024) "Wireless sensor network (WSN) model targeting energy efficient wireless sensor networks node coverage," IEEE Access.
  26. Nadimi-Shahraki MH, Zamani H, Asghari Varzaneh Z, Mirjalili S (2023) A systematic review of the whale optimization algorithm: theoretical foundation, improvements, and hybridizations. Arch Comput Methods Eng 30(7):4113–4159
    Article Google Scholar
  27. Rajeswari M, Amudhavel J, Pothula S, Dhavachelvan P (2017) Directed bee colony optimization algorithm to solve the nurse rostering problem. Comput Intell Neurosci 2017(1):6563498
    Google Scholar
  28. Pandey A, Rajan A, Nandi A, Balas VE (2021) Lifetime enhancement of sensor networks by the moth flame optimization. Wireless Pers Commun 118(4):2807–2820
    Article Google Scholar
  29. Jagan G, Jesu Jayarin P (2022) Wireless sensor network cluster head selection and short routing using energy efficient ElectroStatic discharge algorithm. J Eng 2022:8429285
    Google Scholar
  30. Mistarihi MZ, Bany Salameh HA, Alsaadi MA, Beyca OF, Heilat L, Al-Shobaki R (2023) Energy-efficient bi-objective optimization based on the moth–flame algorithm for cluster head selection in a wireless sensor network. Processes 11(2):534
    Article Google Scholar
  31. Wang J, Gao Y, Liu W, Sangaiah AK, Kim HJ (2019) An improved routing schema with special clustering using PSO algorithm for heterogeneous wireless sensor network. Sensors 19(3):671
    Article Google Scholar

Download references

Acknowledgements

Not applicable.

Author information

Authors and Affiliations

  1. School of Transportation Management, Hunan Communication Polytechnic, Changsha, 410132, China
    Qiulin Wu

Contributions

Q.W. contributed to writing the draft, editing the manuscript, and conceptualizing the research.

Corresponding author

Correspondence toQiulin Wu.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article

Wu, Q. An enhanced whale optimization algorithm with inertia weight and dynamic parameter adaptation for wireless sensor network deployment.Computing 107, 167 (2025). https://doi.org/10.1007/s00607-025-01509-9

Download citation

Keywords

Mathematics Subject Classification