Srinivasan J.; R. Naveenkumar; S. Thenappan; R. Pushpavalli; Vidya Kamma; Nalini Chekuri

Energy-Efficient Wireless and Microwave Networks Based on Hybrid Salp Swarm–Whale Optimization Techniques

PDF (839KB), PP.48-65

Views: 0 Downloads: 0

Author(s)

Srinivasan J. ¹ R. Naveenkumar ^2,* S. Thenappan ³ R. Pushpavalli ⁴ Vidya Kamma ⁵ Nalini Chekuri ⁶

1. Department of Computer Applications, Madanapalle Institute of Technology & Science (MITS) Deemed to be University, Madanapalle, Andhra Pradesh, 517325, India

2. Department of Computer Science and Engineering, CGC University Mohali, Mohali, Punjab, 140307, India

3. Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, 600062, India

4. Department of Electronics and Communication Engineering, Paavai Engineering College, Namakkal, Tamil Nadu, India

5. Department of Computer Science and Engineering, Neil Gogte Institute of Technology, Rangareddy, Hyderabad, Telangana, India

6. Department of Electronics and Communication Engineering, Mohan Babu University, Tirupati, Andhra Pradesh, 517102, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijwmt.2026.05.04

Received: 8 Jul. 2026 / Revised: 5 Aug. 2026 / Accepted: 8 Sep. 2026 / Published: 8 Oct. 2026

Index Terms

Energy-Efficient Wireless Networks, Hybrid Salp Swarm–Whale Optimization (HSSWO), Salp Swarm Algorithm, Whale Optimization Algorithm, Artificial Intelligence, Microwave Communication, 6G Wireless Networks

Abstract

With the faster growth of fifth-generation (5G) and sixth-generation (6G) wireless communication systems, there are unprecedented requirements for ultra-low energy consumption wireless and microwave networks facing an increasing demand for enabling extreme-bandwidth data rates, massive connectivity, and low-latency communications. For optimization problems such as network routing, microwave resource allocation and antenna parameters adaptation, even though the general optimality of the solutions can be proven or demonstrated while optimizing directly with traditional algorithms, they often converge slowly and get trapped in local optima. In response to these issues, this paper presents an Energy Efficient Wireless and Microwave Network Framework using Advanced Hybrid Salp Swarm–Whale Optimization (HSSWO) algorithm. It provides a hybrid method that uses SSA with its good exploration ability and WOA for adaptively exploiting the routing paths, transmission power, microwave antenna parameters, and spectrum assignment. It further adds an AI-assisted network evaluation module for intelligent decision-making. The experimental results show that the proposed HSSWO framework attains 95.82% energy efficiency, a 41.6% improvement in network lifetime, throughput of 12.47 Gbps, packet delivery ratio of 99.21%, end–to–end latency of only 0.58 ms and a packet loss of only 0.69%. Moreover, the utilization of the proposed method leads to a reduction of optimization time for an average of 31.8% as well as improving convergence speed by an average of 36.4% over state-of-the-art optimization methods. The proposed HSSWO framework is an efficient energy-aware architecture for next-generation wireless and microwave communication systems as these results confirm.

Cite This Paper

Srinivasan J., R. Naveenkumar, S. Thenappan, R. Pushpavalli, Vidya Kamma, Nalini Chekuri, "Energy-Efficient Wireless and Microwave Networks Based on Hybrid Salp Swarm–Whale Optimization Techniques", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.5, pp. 48-65, 2026. DOI:10.5815/ijwmt.2026.05.04

Reference

[1]Manoharan, M., Ponnusamy, T., & Subramaniam, U. (2024). Hybrid Salp Swarm and Improved Whale Optimization Algorithm-based clustering scheme for improving network lifespan in wireless sensor networks. International Journal of Communication Systems, 37(14), e5875. DOI: https://doi.org/10.1002/dac.5875
[2]Patel, K. P., & Patel, N. A. (2025). Hybrid Blue Whale and Salp Swarm Optimization for Optimal Placement of TCSC in Power Systems. Proceedings of the 2025 World Conference on Cutting-Edge Science and Technology (WCCEST), 1–8. IEEE. DOI: https://doi.org/10.1109/WCCEST66994.2025.11389877.
[3]Nadimi-Shahraki, M. H., Zamani, H., Asghari Varzaneh, Z., & Mirjalili, S. (2023). A systematic review of the whale optimization algorithm: Theoretical foundation, improvements, and hybridizations. Archives of Computational Methods in Engineering, 30(7), 4113–4159. DOI: https://doi.org/10.1007/s11831-023-09928-7
[4]Hsu, H. P., & Wang, C. N. (2023). Hybridizing whale optimization algorithm with particle swarm optimization for scheduling a dual-command storage/retrieval machine. IEEE Access, 11, 21264–21282. DOI: https://doi.org/10.1109/ACCESS.2023.3246518
[5]Li, M., Xu, G. H., Zeng, L., & Lai, Q. (2023). Hybrid whale optimization algorithm based on symbiosis strategy for global optimization. Applied Intelligence, 53(13), 16663–16705. DOI: https://doi.org/10.1007/s10489-022-04132-9
[6]Abed-Alguni, B. H., Paul, D., & Hammad, R. (2022). Improved Salp Swarm Algorithm for solving single-objective continuous optimization problems. Applied Intelligence, 52(15), 17217–17236. DOI: https://doi.org/10.1007/s10489-022-03269-x
[7]El-Shorbagy, M. A., & Nasar, I. (2025). Salp Swarm Optimization: A comprehensive review of recent advances, variants, applications, and future research directions. European Journal of Pure and Applied Mathematics, 18(4), 7030–7059. DOI: https://doi.org/10.29020/nybg.ejpam.v18i4.7030.
[8]Thawkar, S. (2021). A hybrid model using teaching-learning-based optimization and Salp Swarm Algorithm for feature selection and classification in digital mammography. Journal of Ambient Intelligence and Humanized Computing, 12(9), 8793–8808. DOI: https://doi.org/10.1007/s12652-020-02662-z
[9]Yıldız, A. R., & Erdaş, M. U. (2021). A new Hybrid Taguchi-Salp Swarm Optimization algorithm for the robust design of real-world engineering problems. Materials Testing, 63(2), 157–162. DOI: https://doi.org/10.1515/mt-2020-0022
[10]Chakraborty, S., Saha, A. K., Sharma, S., Chakraborty, R., & Debnath, S. (2023). A hybrid whale optimization algorithm for global optimization. Journal of Ambient Intelligence and Humanized Computing, 14(1), 431–467. DOI: https://doi.org/10.1007/s12652-021-03304-8
[11]Makhadmeh, S. N., Kassaymeh, S., Rjoub, G., Bataineh, B., Sanjalawe, Y., & Al-Betar, M. A. (2025). Recent advances in multi-objective whale optimization algorithm, its versions and applications. Journal of King Saud University – Computer and Information Sciences, 37(7), Article 200. DOI: https://doi.org/10.1007/s44443-025-00184-2
[12]Laskar, N. M., Guha, K., Chatterjee, I., Chanda, S., Baishnab, K. L., & Paul, P. K. (2019). HWPSO: A new hybrid whale-particle swarm optimization algorithm and its application in electronic design optimization problems. Applied Intelligence, 49(1), 265–291. DOI: https://doi.org/10.1007/s10489-018-1247-6
[13]Budimirovic, N., Prabhu, E., Antonijevic, M., Zivkovic, M., Bacanin, N., Strumberger, I., & Venkatachalam, K. (2022). COVID-19 severity prediction using enhanced whale with Salp Swarm feature classification. CMC-Computers, Materials & Continua, 72(1), 1685–1698. DOI: https://doi.org/10.32604/cmc.2022.023418
[14]Abualigah, L., Shehab, M., Diabat, A., & Abraham, A. (2022). Selection scheme sensitivity for a hybrid Salp Swarm Algorithm: Analysis and applications. Engineering with Computers, 38(2), 1149–1175. DOI: https://doi.org/10.1007/s00366-020-01067-y
[15]Nguyen, B. M., Tran, T., Nguyen, T., & Nguyen, G. (2020). Hybridization of galactic swarm and evolution whale optimization for global search problem. IEEE Access, 8, 74991–75010. DOI: https://doi.org/10.1109/ACCESS.2020.2988717
[16]Issa, A., Ali, Y., & Rashid, T. (2022). An efficient hybrid classification approach for COVID-19 based on Harris Hawks Optimization and Salp Swarm Optimization. arXiv preprint arXiv:2301.05296. DOI: Not applicable.
[17]Reenadevi, R., Sathiyabhama, B., Sankar, S., & Pandey, D. (2021). Breast cancer detection in digital mammography using a novel hybrid approach of Salp Swarm and Cuckoo Search algorithm with Deep Belief Network classifier. The Imaging Science Journal, 69(5–8), 364–378. DOI: https://doi.org/10.1080/13682199.2022.2161149
[18]Majumdar, P., Mitra, S., Mirjalili, S., & Bhattacharya, D. (2024). Whale optimization algorithm: Comprehensive meta-analysis on hybridization, latest improvements, variants and applications for complex optimization problems. In Handbook of Whale Optimization Algorithm (pp. 81–90). Elsevier. DOI: https://doi.org/10.1016/B978-0-32-395365-8.00012-9
[19]Yaghoubi, M., Eslami, M., Noroozi, M., Mohammadi, H., Kamari, O., & Palani, S. (2022). Modified Salp Swarm Optimization for parameter estimation of solar PV models. IEEE Access, 10, 110181–110194. DOI: https://doi.org/10.1109/ACCESS.2022.3213746
[20]Kundu, T., Deepmala, & Jain, P. K. (2022). A hybrid Salp Swarm Algorithm based on TLBO for reliability redundancy allocation problems. Applied Intelligence, 52(11), 12630–12667. DOI: https://doi.org/10.1007/s10489-021-02862-w

International Journal of Wireless and Microwave Technologies (IJWMT)