Hybrid Intelligence: A Synergistic Metaheuristic for Advanced Numerical Optimization
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901163669979136 |
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| author | IJMSRT |
| author_facet | IJMSRT |
| contents | <p>This <br>study systematically evaluates and <br>enhances the performance of the Harris Hawks <br>Optimization (HHO) algorithm across twenty- <br>three benchmark functions. The primary <br>objective is to minimize solution errors and <br>optimize convergence by repeatedly testing <br>functions F1 to F23 in MATLAB, incorporating <br>AI-driven <br>code <br>improvements. <br>These <br>enhancements focus on dynamic parameter <br>adjustments and escape mechanisms to avoid <br>local optima, effectively mimicking HHO’s <br>cooperative hunting strategy. Preliminary results <br>reveal a 15–22% decrease in fitness values <br>compared to the basic HHO, particularly when <br>applied to multimodal functions such as F7 and <br>F15. This approach demonstrates the efficacy of <br>iterative testing and machine-learning-based <br>code optimizations in developing advanced</p> <p>metaheuristic <br>algorithms for real-world optimization challenges. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15589569 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Hybrid Intelligence: A Synergistic Metaheuristic for Advanced Numerical Optimization IJMSRT <p>This <br>study systematically evaluates and <br>enhances the performance of the Harris Hawks <br>Optimization (HHO) algorithm across twenty- <br>three benchmark functions. The primary <br>objective is to minimize solution errors and <br>optimize convergence by repeatedly testing <br>functions F1 to F23 in MATLAB, incorporating <br>AI-driven <br>code <br>improvements. <br>These <br>enhancements focus on dynamic parameter <br>adjustments and escape mechanisms to avoid <br>local optima, effectively mimicking HHO’s <br>cooperative hunting strategy. Preliminary results <br>reveal a 15–22% decrease in fitness values <br>compared to the basic HHO, particularly when <br>applied to multimodal functions such as F7 and <br>F15. This approach demonstrates the efficacy of <br>iterative testing and machine-learning-based <br>code optimizations in developing advanced</p> <p>metaheuristic <br>algorithms for real-world optimization challenges. </p> |
| title | Hybrid Intelligence: A Synergistic Metaheuristic for Advanced Numerical Optimization |
| url | https://doi.org/10.5281/zenodo.15589569 |