A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network
In this article, a novel technique is proposed, namely rank-based multi-objective antlion optimization (RMOALO), and applied to optimize the performance of the energy harvesting cognitive radio network (EHCRN). The original selection method in multi-objective antlion optimizer (MOALO) is suitably ch...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Kaunas University of Technology
2022-06-01
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| Series: | Elektronika ir Elektrotechnika |
| Subjects: | |
| Online Access: | https://eejournal.ktu.lt/index.php/elt/article/view/31245 |
| _version_ | 1811226760703901696 |
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| author | Shalley Bakshi Surbhi Sharma Rajesh Khanna |
| author_facet | Shalley Bakshi Surbhi Sharma Rajesh Khanna |
| author_sort | Shalley Bakshi |
| collection | DOAJ |
| description | In this article, a novel technique is proposed, namely rank-based multi-objective antlion optimization (RMOALO), and applied to optimize the performance of the energy harvesting cognitive radio network (EHCRN). The original selection method in multi-objective antlion optimizer (MOALO) is suitably changed to improve the algorithm, thus reaching the optimal solution for the problem. The proposed technique shows considerable performance improvement over the method used in the multi-objective antlion optimizer (MOALO). The performance of the proposed RMOALO is demonstrated on five benchmark mathematical functions and compared to multi-objective particle swarm optimization (MOPSO), multi-objective moth flame optimization (MOMFO), MOALO-Tournament, and MOALO-Roulette. The simulation results show an improved convergence of RMOALO and find the optimal solution to the throughput maximization problem. We show that RMOALO provides 16.33 % improved average throughput with the optimal value of sensing duration for the varying amount of harvested energy compared to MOPSO, MOMFO, MOALO-Roulette, and MOALO-Tournament. |
| first_indexed | 2024-04-12T09:31:49Z |
| format | Article |
| id | doaj.art-e5a24afc3867438faf4704d3b193e944 |
| institution | Directory Open Access Journal |
| issn | 1392-1215 2029-5731 |
| language | English |
| last_indexed | 2024-04-12T09:31:49Z |
| publishDate | 2022-06-01 |
| publisher | Kaunas University of Technology |
| record_format | Article |
| series | Elektronika ir Elektrotechnika |
| spelling | doaj.art-e5a24afc3867438faf4704d3b193e9442022-12-22T03:38:21ZengKaunas University of TechnologyElektronika ir Elektrotechnika1392-12152029-57312022-06-01283788910.5755/j02.eie.3124536499A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio NetworkShalley Bakshi0https://orcid.org/0000-0002-1615-128XSurbhi Sharma1https://orcid.org/0000-0002-4414-9355Rajesh Khanna2https://orcid.org/0000-0002-2331-1667Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, IndiaDepartment of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, IndiaDepartment of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, IndiaIn this article, a novel technique is proposed, namely rank-based multi-objective antlion optimization (RMOALO), and applied to optimize the performance of the energy harvesting cognitive radio network (EHCRN). The original selection method in multi-objective antlion optimizer (MOALO) is suitably changed to improve the algorithm, thus reaching the optimal solution for the problem. The proposed technique shows considerable performance improvement over the method used in the multi-objective antlion optimizer (MOALO). The performance of the proposed RMOALO is demonstrated on five benchmark mathematical functions and compared to multi-objective particle swarm optimization (MOPSO), multi-objective moth flame optimization (MOMFO), MOALO-Tournament, and MOALO-Roulette. The simulation results show an improved convergence of RMOALO and find the optimal solution to the throughput maximization problem. We show that RMOALO provides 16.33 % improved average throughput with the optimal value of sensing duration for the varying amount of harvested energy compared to MOPSO, MOMFO, MOALO-Roulette, and MOALO-Tournament.https://eejournal.ktu.lt/index.php/elt/article/view/31245cognitive radioenergy harvestingmetaheuristic optimizationmoalospectrum sensing |
| spellingShingle | Shalley Bakshi Surbhi Sharma Rajesh Khanna A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network Elektronika ir Elektrotechnika cognitive radio energy harvesting metaheuristic optimization moalo spectrum sensing |
| title | A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network |
| title_full | A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network |
| title_fullStr | A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network |
| title_full_unstemmed | A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network |
| title_short | A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network |
| title_sort | novel metaheuristic optimization for throughput maximization in energy harvesting cognitive radio network |
| topic | cognitive radio energy harvesting metaheuristic optimization moalo spectrum sensing |
| url | https://eejournal.ktu.lt/index.php/elt/article/view/31245 |
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