Enhancing Metaheuristic Optimization: A Novel Nature-Inspired Hybrid Approach Incorporating Selected Pseudorandom Number Generators
In this paper, a hybrid nature-inspired metaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm and the African Buffalo Optimization during the optimization process, leveraging thei...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/16/9/413 |
| _version_ | 1797581660712075264 |
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| author | Marko Gulić Martina Žuškin |
| author_facet | Marko Gulić Martina Žuškin |
| author_sort | Marko Gulić |
| collection | DOAJ |
| description | In this paper, a hybrid nature-inspired metaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm and the African Buffalo Optimization during the optimization process, leveraging their respective strengths to improve performance. To improve randomness, the hybrid approach uses two high-quality pseudorandom number generators—the 64-bit and 32-bit versions of the SIMD-Oriented Fast Mersenne Twister. The effectiveness of the hybrid algorithm is evaluated on the NP-hard Container Relocation Problem, focusing on a test set of restricted Container Relocation Problems with higher complexity. The results show that the hybrid algorithm outperforms the individual Genetic Algorithm and the African Buffalo Optimization, which use standard pseudorandom number generators. The adaptive switch method allows the algorithm to adapt to different optimization problems and mitigate problems such as premature convergence and local optima. Moreover, the importance of pseudorandom number generator selection in metaheuristic algorithms is highlighted, as it directly affects the optimization results. The use of powerful pseudorandom number generators reduces the probability of premature convergence and local optima, leading to better optimization results. Overall, the research demonstrates the potential of hybrid metaheuristic approaches for solving complex optimization problems, which makes them relevant for scientific research and practical applications. |
| first_indexed | 2024-03-10T23:07:40Z |
| format | Article |
| id | doaj.art-9741a85755f44befbcf106b60fafcc3c |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T23:07:40Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-9741a85755f44befbcf106b60fafcc3c2023-11-19T09:12:47ZengMDPI AGAlgorithms1999-48932023-08-0116941310.3390/a16090413Enhancing Metaheuristic Optimization: A Novel Nature-Inspired Hybrid Approach Incorporating Selected Pseudorandom Number GeneratorsMarko Gulić0Martina Žuškin1Faculty of Maritime Studies, University of Rijeka, Studentska 2, 51000 Rijeka, CroatiaFaculty of Maritime Studies, University of Rijeka, Studentska 2, 51000 Rijeka, CroatiaIn this paper, a hybrid nature-inspired metaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm and the African Buffalo Optimization during the optimization process, leveraging their respective strengths to improve performance. To improve randomness, the hybrid approach uses two high-quality pseudorandom number generators—the 64-bit and 32-bit versions of the SIMD-Oriented Fast Mersenne Twister. The effectiveness of the hybrid algorithm is evaluated on the NP-hard Container Relocation Problem, focusing on a test set of restricted Container Relocation Problems with higher complexity. The results show that the hybrid algorithm outperforms the individual Genetic Algorithm and the African Buffalo Optimization, which use standard pseudorandom number generators. The adaptive switch method allows the algorithm to adapt to different optimization problems and mitigate problems such as premature convergence and local optima. Moreover, the importance of pseudorandom number generator selection in metaheuristic algorithms is highlighted, as it directly affects the optimization results. The use of powerful pseudorandom number generators reduces the probability of premature convergence and local optima, leading to better optimization results. Overall, the research demonstrates the potential of hybrid metaheuristic approaches for solving complex optimization problems, which makes them relevant for scientific research and practical applications.https://www.mdpi.com/1999-4893/16/9/413optimizationnature-inspired metaheuristic algorithmshybrid metaheuristicsadaptive switch methodpseudorandom number generators |
| spellingShingle | Marko Gulić Martina Žuškin Enhancing Metaheuristic Optimization: A Novel Nature-Inspired Hybrid Approach Incorporating Selected Pseudorandom Number Generators Algorithms optimization nature-inspired metaheuristic algorithms hybrid metaheuristics adaptive switch method pseudorandom number generators |
| title | Enhancing Metaheuristic Optimization: A Novel Nature-Inspired Hybrid Approach Incorporating Selected Pseudorandom Number Generators |
| title_full | Enhancing Metaheuristic Optimization: A Novel Nature-Inspired Hybrid Approach Incorporating Selected Pseudorandom Number Generators |
| title_fullStr | Enhancing Metaheuristic Optimization: A Novel Nature-Inspired Hybrid Approach Incorporating Selected Pseudorandom Number Generators |
| title_full_unstemmed | Enhancing Metaheuristic Optimization: A Novel Nature-Inspired Hybrid Approach Incorporating Selected Pseudorandom Number Generators |
| title_short | Enhancing Metaheuristic Optimization: A Novel Nature-Inspired Hybrid Approach Incorporating Selected Pseudorandom Number Generators |
| title_sort | enhancing metaheuristic optimization a novel nature inspired hybrid approach incorporating selected pseudorandom number generators |
| topic | optimization nature-inspired metaheuristic algorithms hybrid metaheuristics adaptive switch method pseudorandom number generators |
| url | https://www.mdpi.com/1999-4893/16/9/413 |
| work_keys_str_mv | AT markogulic enhancingmetaheuristicoptimizationanovelnatureinspiredhybridapproachincorporatingselectedpseudorandomnumbergenerators AT martinazuskin enhancingmetaheuristicoptimizationanovelnatureinspiredhybridapproachincorporatingselectedpseudorandomnumbergenerators |