An Enhanced Naked Mole Rat Algorithm for Optimal Cross-Layer Solution for Wireless Underground Sensor Networks

Pratap Singh, Rishi Pal Singh, Yudhvir Singh, Jana Shafi and Muhammad Fazal Ijaz
Additional contact information
Pratap Singh: Department of Computer Science and Engineering, Guru Jambheshwar University of Science & Technology, Hisar 125001, India
Rishi Pal Singh: Department of Computer Science and Engineering, Guru Jambheshwar University of Science & Technology, Hisar 125001, India
Yudhvir Singh: Department of Computer Science and Engineering, Maharshi Dayanand University, Rohtak 124001, India
Jana Shafi: Department of Computer Science, College of Arts and Science, Prince Sattam Bin Abdul University, Wadi Ad-Dawasir 11991, Saudi Arabia
Muhammad Fazal Ijaz: Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea

Mathematics, 2021, vol. 9, issue 22, 1-24

Abstract: Nature-inspired algorithms serve as the backbone of modern computing technology, and over the past three decades, the field has grown enormously. Many applications were solved by such algorithms and are replacing the traditional classical optimization processes. A recent naked mole-rat algorithm (NMRA) was proposed based on the mating patterns of naked mole-rats. This algorithm proved its worth in terms of competitiveness and application to various domains of research. The aim was to propose an algorithm based on NMRA, named enhanced NMRA (ENMRA), by mitigating the problems that this algorithm suffers from: slow convergence, poor exploration, and local optima stagnation. To enhance the exploration capabilities of basic NMRA, grey wolf optimization (GWO)-based search equations were employed. Exploitation was improved using population division methods based on local neighborhood search (LNS) and differential evolution (DE) equations. To avoid the local stagnation problem, a neighborhood search strategy around the best individual was utilized. Such improvements help the new variant to solve highly challenging optimization problems in contrast to existing algorithms. The efficacy of ENMRA was evaluated using CEC 2019 benchmark test suite. The results were statistically analyzed by the Wilcoxon rank-sum test and Friedman rank (f-rank) test. The resulting analysis proved that ENMRA is superior to the competitive algorithms for test functions CEC 2019 with overall effectiveness of 60.33%. Moreover, the real-world optimization problem from underground wireless sensor networks for an efficient cross-layer solution was also used to test the efficiency of ENMRA. The results of comparative study and statistical tests affirmed the efficient performance of the proposed algorithm.

Keywords: nature-inspired algorithms; evolutionary algorithms; swarm intelligent algorithms; NMRA; wireless underground sensor network; distributed protocol; distributed power control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/22/2942/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/22/2942/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:22:p:2942-:d:682206

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().