MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems
In the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), a novel approach that synergistic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/8/615 |
| _version_ | 1797381933969178624 |
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| author | Jeyaganesh Kumar Kailasam Rajkumar Nalliah Saravanakumar Nallagoundanpalayam Muthusamy Premkumar Manoharan |
| author_facet | Jeyaganesh Kumar Kailasam Rajkumar Nalliah Saravanakumar Nallagoundanpalayam Muthusamy Premkumar Manoharan |
| author_sort | Jeyaganesh Kumar Kailasam |
| collection | DOAJ |
| description | In the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), a novel approach that synergistically integrates Q-learning, competitive learning, and adaptive learning techniques. The essence of multi-learning lies in harnessing the strengths of these individual learning paradigms to foster a more robust and versatile search mechanism. Q-learning brings the advantage of reinforcement learning, enabling the algorithm to make informed decisions based on past experiences. On the other hand, competitive learning introduces an element of competition, ensuring that the best solutions are continually evolving and adapting. Lastly, adaptive learning ensures the algorithm remains flexible, adjusting the traditional Reptile Search Algorithm (RSA) parameters. The application of the MLBRSA to numerical benchmarks and a few real-world engineering problems demonstrates its ability to find optimal solutions in complex problem spaces. Furthermore, when applied to the complicated task of software requirement prioritization, MLBRSA showcases its capability to rank requirements effectively, ensuring that critical software functionalities are addressed promptly. Based on the results obtained, the MLBRSA stands as evidence of the potential of multi-learning, offering a promising solution to engineering and software-centric challenges. Its adaptability, competitiveness, and experience-driven approach make it a valuable tool for researchers and practitioners. |
| first_indexed | 2024-03-08T20:58:13Z |
| format | Article |
| id | doaj.art-e76af4c912c94dfc847f645a0e846823 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-08T20:58:13Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-e76af4c912c94dfc847f645a0e8468232023-12-22T13:55:42ZengMDPI AGBiomimetics2313-76732023-12-018861510.3390/biomimetics8080615MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization ProblemsJeyaganesh Kumar Kailasam0Rajkumar Nalliah1Saravanakumar Nallagoundanpalayam Muthusamy2Premkumar Manoharan3Department of Artificial Intelligence and Data Science, M. Kumarasamy College of Engineering, Karur 639113, Tamilnadu, IndiaDepartment of Computer Science and Engineering, KGiSL Institute of Technology, Coimbatore 641035, Tamilnadu, IndiaDepartment of Information Technology, Karpagam College of Engineering, Coimbatore 641032, Tamilnadu, IndiaDepartment of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bangalore 560078, Karnataka, IndiaIn the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), a novel approach that synergistically integrates Q-learning, competitive learning, and adaptive learning techniques. The essence of multi-learning lies in harnessing the strengths of these individual learning paradigms to foster a more robust and versatile search mechanism. Q-learning brings the advantage of reinforcement learning, enabling the algorithm to make informed decisions based on past experiences. On the other hand, competitive learning introduces an element of competition, ensuring that the best solutions are continually evolving and adapting. Lastly, adaptive learning ensures the algorithm remains flexible, adjusting the traditional Reptile Search Algorithm (RSA) parameters. The application of the MLBRSA to numerical benchmarks and a few real-world engineering problems demonstrates its ability to find optimal solutions in complex problem spaces. Furthermore, when applied to the complicated task of software requirement prioritization, MLBRSA showcases its capability to rank requirements effectively, ensuring that critical software functionalities are addressed promptly. Based on the results obtained, the MLBRSA stands as evidence of the potential of multi-learning, offering a promising solution to engineering and software-centric challenges. Its adaptability, competitiveness, and experience-driven approach make it a valuable tool for researchers and practitioners.https://www.mdpi.com/2313-7673/8/8/615competitive learningadaptive learningmulti-learning-based reptile search algorithm (MLBRSA)optimizationQ-learningsoftware requirement prioritization |
| spellingShingle | Jeyaganesh Kumar Kailasam Rajkumar Nalliah Saravanakumar Nallagoundanpalayam Muthusamy Premkumar Manoharan MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems Biomimetics competitive learning adaptive learning multi-learning-based reptile search algorithm (MLBRSA) optimization Q-learning software requirement prioritization |
| title | MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems |
| title_full | MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems |
| title_fullStr | MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems |
| title_full_unstemmed | MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems |
| title_short | MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems |
| title_sort | mlbrsa multi learning based reptile search algorithm for global optimization and software requirement prioritization problems |
| topic | competitive learning adaptive learning multi-learning-based reptile search algorithm (MLBRSA) optimization Q-learning software requirement prioritization |
| url | https://www.mdpi.com/2313-7673/8/8/615 |
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