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An improved tuna swarm optimization algorithm based on behavior evaluation for wireless sensor network coverage optimization

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Abstract

Tuna swarm optimization algorithm (TSO) is an innovative swarm intelligence algorithm that possesses the advantages of having a small number of adjustable parameters and being straightforward to implement, but the TSO exhibits drawbacks including low computational accuracy and susceptibility to local optima. To solve the shortcomings of TSO, a TSO variant based on behavioral evaluation and simplex strategy is proposed by this study, named SITSO. Firstly, the behavior evaluation mechanism is used to change the updating mechanism of TSO, thereby improving the convergence speed and calculation accuracy of TSO. Secondly, the simplex method enhances the exploitation capability of TSO. Then, simulations of different dimensions of the CEC2017 standard functional test set are performed and compared with a variety of existing mature algorithms to verify the performance of all aspects of the SITSO. Finally, numerous simulation experiments are conducted to address the optimization of wireless sensor network coverage. Based on the experimental results, SITSO outperforms the remaining six comparison algorithms in terms of performance.

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References

  1. Ali, S., Qaisar, S. B., Saeed, H., Khan, M. F., Naeem, M., & Anpalagan, A. (2015). Network challenges for cyber physical systems with tiny wireless devices: A case study on reliable pipeline condition monitoring. Sensors, 15(4), 7172–7205. https://doi.org/10.3390/s150407172

    Article  Google Scholar 

  2. Singh, A., Nagar, J., Sharma, S., & Kotiyal, V. (2021). A gaussian process regression approach to predict the k-barrier coverage probability for intrusion detection in wireless sensor networks. Expert Systems with Applications, 172, 114603. https://doi.org/10.1016/j.eswa.2021.114603

    Article  Google Scholar 

  3. Xu, Y., Ding, O., Qu, R., & Li, K. (2018). Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization. Applied Soft Computing, 68, 268–282. https://doi.org/10.1016/j.asoc.2018.03.053

    Article  Google Scholar 

  4. Papan, J., Bridova, I., & Filipko, A. (2023). Design of a technique for accelerating the wsn convergence process. Sensors. https://doi.org/10.3390/s23218682

    Article  Google Scholar 

  5. Wilson, W. C., & Juarez, P. D. (2014). Emerging needs for pervasive passive wireless sensor networks on aerospace vehicles. Procedia Computer Science, 37, 101–108. https://doi.org/10.1016/j.procs.2014.08.018

    Article  Google Scholar 

  6. Stankunas, J., Rudinskas, D., & Lasauskas, E. (2011). Experimental research of wireless sensor network application in aviation. Elektronika ir Elektrotechnika, 111, 41–44. https://doi.org/10.5755/j01.eee.111.5.353

    Article  Google Scholar 

  7. Katzis, K., Berbakov, L., Gardašević, G., & Šveljo, O. (2022). Breaking barriers in emerging biomedical applications. Entropy, 24(2), 226.

    Article  Google Scholar 

  8. Mohajer, A., Sam Daliri, M., Mirzaei, A., Ziaeddini, A., Nabipour, M., & Bavaghar, M. (2023). Heterogeneous computational resource allocation for noma: Toward green mobile edge-computing systems. IEEE Transactions on Services Computing, 16(2), 1225–1238. https://doi.org/10.1109/TSC.2022.3186099

    Article  Google Scholar 

  9. Liang, J., Tian, M., Liu, Y., & Zhou, J. (2022). Coverage optimization of soil moisture wireless sensor networks based on adaptive Cauchy variant butterfly optimization algorithm. Scientific Reports, 12(1), 11687.

    Article  Google Scholar 

  10. Shi, C., Wei, R., & Zhang, Y. (2018). Application research of wireless sensor network in building structure safety monitoring. IOP Conference Series: Materials Science and Engineering, 366(1), 012084. https://doi.org/10.1088/1757-899X/366/1/012084

    Article  Google Scholar 

  11. Dong, S., Zhan, J., Hu, W., Mohajer, A., Bavaghar, M., & Mirzaei, A. (2023). Energy-efficient hierarchical resource allocation in uplink-downlink decoupled noma hetnets. IEEE Transactions on Network and Service Management, 20(3), 3380–3395. https://doi.org/10.1109/TNSM.2023.3239417

    Article  Google Scholar 

  12. Sharada, K. A., Mahesh, T. R., chandrasekaran, S., Shashikumar, R., Kumar, V. V., & Annand, J. R. (2024). Improved energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor networks. Scientific Reports, 14(1), 4391. https://doi.org/10.1038/s41598-024-55099-1

    Article  Google Scholar 

  13. Mohajer, A., Sorouri, F., Mirzaei, A., Ziaeddini, A., Rad, K. J., & Bavaghar, M. (2022). Energy-aware hierarchical resource management and backhaul traffic optimization in heterogeneous cellular networks. IEEE Systems Journal, 16(4), 5188–5199. https://doi.org/10.1109/JSYST.2022.3154162

    Article  Google Scholar 

  14. Chen, W., Yang, P., Zhao, W., & Wei, L. (2022). Improved ant lion optimizer for coverage optimization in wireless sensor networks. Wireless Communications and Mobile Computing, 2022, 8808575. https://doi.org/10.1155/2022/8808575

    Article  Google Scholar 

  15. Wang, D., Wang, H., Ban, X., Qian, X., & Ni, J. (2019). An adaptive, discrete space oriented wolf pack optimization algorithm for a movable wireless sensor network. Sensors. https://doi.org/10.3390/s19194320

    Article  Google Scholar 

  16. Qu, Y. G., Zhai, Y. J., Lin, Z. T., Zhao, B. H., & Zhang, Y. T. (2004). A novel sensor placement model in wireless sensor network. Journal of Bei**g University of Posts and Telecommunications, 27(6), 1–5.

    Google Scholar 

  17. Wang, S., Yang, X., Wang, X., & Qian, Z. (2019). A virtual force algorithm-Levy-embedded grey wolf optimization algorithm for wireless sensor network coverage optimization. Sensors. https://doi.org/10.3390/s19122735

    Article  Google Scholar 

  18. Rajendran, S., Čep, R., Narayanan, R. C., Pal, S., & Kalita, K. (2022). A conceptual comparison of six nature-inspired metaheuristic algorithms in process optimization. Processes, 10(2), 197.

    Article  Google Scholar 

  19. Roberts, M. K., & Thangavel, J. (2022). An optimized ticket manager based energy-aware multipath routing protocol design for iot based wireless sensor networks. Concurrency and Computation: Practice and Experience, 34(28), 7398. https://doi.org/10.1002/cpe.7398

    Article  Google Scholar 

  20. Chen, X., Qin, T., Wei, W., Fan, Y., Luo, X., & Yang, J. (2023). A data transmission protocol for wsn based on multi-strategy improved whale optimisation algorithm. International Journal of Modelling, Identification and Control, 43(4), 302–311. https://doi.org/10.1504/IJMIC.2023.133435

    Article  Google Scholar 

  21. Zheng, W.-M., Liu, N., Chai, Q.-W., & Liu, Y. (2023). Application of improved black hole algorithm in prolonging the lifetime of wireless sensor network. Complex & Intelligent Systems, 9(5), 5817–5829. https://doi.org/10.1007/s40747-023-01041-3

    Article  Google Scholar 

  22. Zhang, Y. (2020). Coverage optimization and simulation of wireless sensor networks based on particle swarm optimization. International Journal of Wireless Information Networks, 27(2), 307–316. https://doi.org/10.1007/s10776-019-00446-7

    Article  Google Scholar 

  23. He, Q., Lan, Z., Zhang, D., Yang, L., & Luo, S. (2022). Improved marine predator algorithm for wireless sensor network coverage optimization problem. Sustainability. https://doi.org/10.3390/su14169944

    Article  Google Scholar 

  24. 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, 96, 106602. https://doi.org/10.1016/j.asoc.2020.106602

    Article  Google Scholar 

  25. **e, L., Han, T., Zhou, H., Zhang, Z.-R., Han, B., & Tang, A. (2021). Tuna swarm optimization: A novel swarm-based metaheuristic algorithm for global optimization. Computational Intelligence and Neuroscience, 2021, 9210050. https://doi.org/10.1155/2021/9210050

    Article  Google Scholar 

  26. Tan, M., Li, Y., Ding, D., Zhou, R., & Huang, C. (2022). An improved jade hybridizing with tuna swarm optimization for numerical optimization problems. Mathematical Problems in Engineering, 2022, 7726548. https://doi.org/10.1155/2022/7726548

    Article  Google Scholar 

  27. Wang, W., & Tian, J. (2022). An improved nonlinear tuna swarm optimization algorithm based on circle chaos map and levy flight operator. Electronics. https://doi.org/10.3390/electronics11223678

    Article  Google Scholar 

  28. Tuerxun, W., Xu, C., Guo, H., Guo, L., Zeng, N., & Cheng, Z. (2022). An ultra-short-term wind speed prediction model using lstm based on modified tuna swarm optimization and successive variational mode decomposition. Energy Science & Engineering, 10(8), 3001–3022. https://doi.org/10.1002/ese3.1183

    Article  Google Scholar 

  29. Yan, Z., Yan, J., Wu, Y., Cai, S., & Wang, H. (2023). A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning. Mathematics and Computers in Simulation, 209, 55–86. https://doi.org/10.1016/j.matcom.2023.02.003

    Article  Google Scholar 

  30. Kumar, C., & Magdalin Mary, D. (2022). A novel chaotic-driven tuna swarm optimizer with Newton–Raphson method for parameter identification of three-diode equivalent circuit model of solar photovoltaic cells/modules. Optik, 264, 169379. https://doi.org/10.1016/j.ijleo.2022.169379

    Article  Google Scholar 

  31. **ao-Lei, L. I., Fei, L. U., Guo-Hui, T., & Ji-**n, Q. (2004). Applications of artificial fish school algorithm in combinatorial optimization problems. Journal of Shandong University (Engineering Science), 34(5), 64–67.

    Google Scholar 

  32. Tao, L., & Xueqiang, M. (2023). Hybrid strategy improved sparrow search algorithm in the field of intrusion detection. IEEE Access, 11, 32134–32151. https://doi.org/10.1109/ACCESS.2023.3259548

    Article  Google Scholar 

  33. **ao, H.-F., & Tan, G.-z. (2010). A novel particle swarm optimizer without velocity: Simplex-pso. Journal of Central South University of Technology, 17(2), 349–356. https://doi.org/10.1007/s11771-010-0052-0

    Article  Google Scholar 

  34. Awad, N., Ali, M., Liang, J., Qu, B., & Suganthan, P. (2016). Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective bound constrained real-parameter numerical optimization, pp. 1–34.

  35. Tuerxun, W., Xu, C., Guo, H., Guo, L., Zeng, N., & Cheng, Z. (2022). An ultra-short-term wind speed prediction model using lstm based on modified tuna swarm optimization and successive variational mode decomposition. Energy Science & Engineering, 10(8), 3001–3022. https://doi.org/10.1002/ese3.1183

    Article  Google Scholar 

  36. Han, L. (2023). Improved tuna swarm optimization algorithm based on hybrid strategy. Guangxi Sciences, 30(1), 208–218.

    Google Scholar 

  37. Wang, J., Zhu, L., Wu, B., & Ryspayev, A. (2022). Forestry canopy image segmentation based on improved tuna swarm optimization. Forests. https://doi.org/10.3390/f13111746

    Article  Google Scholar 

  38. Braik, M., Hammouri, A., Atwan, J., Al-Betar, M. A., & Awadallah, M. A. (2022). White shark optimizer: A novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowledge-Based Systems, 243, 108457. https://doi.org/10.1016/j.knosys.2022.108457

    Article  Google Scholar 

  39. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  40. Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249. https://doi.org/10.1016/j.knosys.2015.07.006

    Article  Google Scholar 

  41. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 - International Conference on Neural Networks (Vol. 4, pp. 1942–19484). https://doi.org/10.1109/ICNN.1995.488968

  42. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  43. Yang, X.-S. (2012). Flower pollination algorithm for global optimization. In J. Durand-Lose & N. Jonoska (Eds.), Unconventional Computation and Natural Computation (pp. 240–249). Berlin: Springer.

    Chapter  Google Scholar 

  44. Lam, F. C., & Longnecker, M. T. (1983). A modified Wilcoxon rank sum test for paired data. Biometrika, 70(2), 510–513.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Mathematics and Physics, Guangxi Minzu University, Nanning, 530006, Guangxi, China

    Yu Chang & Dengxu He

  2. School of Artificial Intelligence, Guangxi Minzu University, Nanning, 530006, Guangxi, China

    Liangdong Qu

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Contributions

Yu Chang: Methodology, Validation, Writing-original draft. Dengxu He: Conceptualization, Software,Visualization, Writing-editing. Liangdong Qu: Conceptualization, Software,Visualization, Writing-editing.

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Correspondence to Dengxu He.

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Chang, Y., He, D. & Qu, L. An improved tuna swarm optimization algorithm based on behavior evaluation for wireless sensor network coverage optimization. Telecommun Syst (2024). https://doi.org/10.1007/s11235-024-01168-9

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