A Novel Hybrid Improved RIME Algorithm for Global Optimization Problems
The RIME algorithm is a novel physical-based meta-heuristic algorithm with a strong ability to solve global optimization problems and address challenges in engineering applications. It implements exploration and exploitation behaviors by constructing a rime-ice growth process. However, RIME comes wi...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/1/14 |
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| _version_ | 1832588961538637824 |
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| author | Wuke Li Xiong Yang Yuchen Yin Qian Wang |
| author_facet | Wuke Li Xiong Yang Yuchen Yin Qian Wang |
| author_sort | Wuke Li |
| collection | DOAJ |
| description | The RIME algorithm is a novel physical-based meta-heuristic algorithm with a strong ability to solve global optimization problems and address challenges in engineering applications. It implements exploration and exploitation behaviors by constructing a rime-ice growth process. However, RIME comes with a couple of disadvantages: a limited exploratory capability, slow convergence, and inherent asymmetry between exploration and exploitation. An improved version with more efficiency and adaptability to solve these issues now comes in the form of Hybrid Estimation Rime-ice Optimization, in short, HERIME. A probabilistic model-based sampling approach of the estimated distribution algorithm is utilized to enhance the quality of the RIME population and boost its global exploration capability. A roulette-based fitness distance balanced selection strategy is used to strengthen the hard-rime phase of RIME to effectively enhance the balance between the exploitation and exploration phases of the optimization process. We validate HERIME using 41 functions from the IEEE CEC2017 and IEEE CEC2022 test suites and compare its optimization accuracy, convergence, and stability with four classical and recent metaheuristic algorithms as well as five advanced algorithms to reveal the fact that the proposed algorithm outperforms all of them. Statistical research using the Friedman test and Wilcoxon rank sum test also confirms its excellent performance. Moreover, ablation experiments validate the effectiveness of each strategy individually. Thus, the experimental results show that HERIME has better search efficiency and optimization accuracy and is effective in dealing with global optimization problems. |
| format | Article |
| id | doaj-art-36debca49ed743a0b19e57ffd9ac1607 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-36debca49ed743a0b19e57ffd9ac16072025-01-24T13:24:35ZengMDPI AGBiomimetics2313-76732024-12-011011410.3390/biomimetics10010014A Novel Hybrid Improved RIME Algorithm for Global Optimization ProblemsWuke Li0Xiong Yang1Yuchen Yin2Qian Wang3School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, ChinaZhicheng College, Fuzhou University, Fuzhou 350002, ChinaTeachers College, Columbia University, 525 West 120th Street, New York, NY 10027, USADepartment of Computer Science, Durham University, Durham DH1 3LE, UKThe RIME algorithm is a novel physical-based meta-heuristic algorithm with a strong ability to solve global optimization problems and address challenges in engineering applications. It implements exploration and exploitation behaviors by constructing a rime-ice growth process. However, RIME comes with a couple of disadvantages: a limited exploratory capability, slow convergence, and inherent asymmetry between exploration and exploitation. An improved version with more efficiency and adaptability to solve these issues now comes in the form of Hybrid Estimation Rime-ice Optimization, in short, HERIME. A probabilistic model-based sampling approach of the estimated distribution algorithm is utilized to enhance the quality of the RIME population and boost its global exploration capability. A roulette-based fitness distance balanced selection strategy is used to strengthen the hard-rime phase of RIME to effectively enhance the balance between the exploitation and exploration phases of the optimization process. We validate HERIME using 41 functions from the IEEE CEC2017 and IEEE CEC2022 test suites and compare its optimization accuracy, convergence, and stability with four classical and recent metaheuristic algorithms as well as five advanced algorithms to reveal the fact that the proposed algorithm outperforms all of them. Statistical research using the Friedman test and Wilcoxon rank sum test also confirms its excellent performance. Moreover, ablation experiments validate the effectiveness of each strategy individually. Thus, the experimental results show that HERIME has better search efficiency and optimization accuracy and is effective in dealing with global optimization problems.https://www.mdpi.com/2313-7673/10/1/14RIMEglobal optimizationfitness distance balancehybridmetaheuristic optimizationsynergistic fusion framework |
| spellingShingle | Wuke Li Xiong Yang Yuchen Yin Qian Wang A Novel Hybrid Improved RIME Algorithm for Global Optimization Problems Biomimetics RIME global optimization fitness distance balance hybrid metaheuristic optimization synergistic fusion framework |
| title | A Novel Hybrid Improved RIME Algorithm for Global Optimization Problems |
| title_full | A Novel Hybrid Improved RIME Algorithm for Global Optimization Problems |
| title_fullStr | A Novel Hybrid Improved RIME Algorithm for Global Optimization Problems |
| title_full_unstemmed | A Novel Hybrid Improved RIME Algorithm for Global Optimization Problems |
| title_short | A Novel Hybrid Improved RIME Algorithm for Global Optimization Problems |
| title_sort | novel hybrid improved rime algorithm for global optimization problems |
| topic | RIME global optimization fitness distance balance hybrid metaheuristic optimization synergistic fusion framework |
| url | https://www.mdpi.com/2313-7673/10/1/14 |
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