Adaptive Exploration Artificial Bee Colony for Mathematical Optimization
The artificial bee colony (ABC) algorithm is a famous swarm intelligence method utilized across various disciplines due to its robustness. However, it exhibits limitations in exploration mechanisms, particularly in high-dimensional or complex landscapes. This article introduces the adaptive explorat...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/5/4/109 |
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| author | Shaymaa Alsamia Edina Koch Hazim Albedran Richard Ray |
| author_facet | Shaymaa Alsamia Edina Koch Hazim Albedran Richard Ray |
| author_sort | Shaymaa Alsamia |
| collection | DOAJ |
| description | The artificial bee colony (ABC) algorithm is a famous swarm intelligence method utilized across various disciplines due to its robustness. However, it exhibits limitations in exploration mechanisms, particularly in high-dimensional or complex landscapes. This article introduces the adaptive exploration artificial bee colony (AEABC), a novel variant that reinspires the ABC algorithm based on real-world phenomena. AEABC incorporates new distance-based parameters and mechanisms to correct the original design, enhancing its robustness. The performance of AEABC was evaluated against 33 state-of-the-art metaheuristics across twenty-five benchmark functions and an engineering application. AEABC consistently outperformed its counterparts, demonstrating superior efficiency and accuracy. In a variable-sized problem (n = 10), the traditional ABC algorithm converged to 3.086 × 10<sup>6</sup>, while AEABC achieved a convergence of 2.0596 × 10<sup>−255</sup>, highlighting its robust performance. By addressing the shortcomings of the traditional ABC algorithm, AEABC significantly advances mathematical optimization, especially in engineering applications. This work underscores the significance of the inspiration of the traditional ABC algorithm in enhancing the capabilities of swarm intelligence. |
| format | Article |
| id | doaj-art-f0a8b1865fa34a39bffff1566d03c767 |
| institution | DOAJ |
| issn | 2673-2688 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-f0a8b1865fa34a39bffff1566d03c7672025-08-20T02:55:35ZengMDPI AGAI2673-26882024-11-01542218223610.3390/ai5040109Adaptive Exploration Artificial Bee Colony for Mathematical OptimizationShaymaa Alsamia0Edina Koch1Hazim Albedran2Richard Ray3Department of Structural and Geotechnical Engineering, Széchenyi István University, 9026 Győr, HungaryDepartment of Structural and Geotechnical Engineering, Széchenyi István University, 9026 Győr, HungaryFaculty of Engineering, University of Kufa, Najaf P.O. Box 21, IraqDepartment of Structural and Geotechnical Engineering, Széchenyi István University, 9026 Győr, HungaryThe artificial bee colony (ABC) algorithm is a famous swarm intelligence method utilized across various disciplines due to its robustness. However, it exhibits limitations in exploration mechanisms, particularly in high-dimensional or complex landscapes. This article introduces the adaptive exploration artificial bee colony (AEABC), a novel variant that reinspires the ABC algorithm based on real-world phenomena. AEABC incorporates new distance-based parameters and mechanisms to correct the original design, enhancing its robustness. The performance of AEABC was evaluated against 33 state-of-the-art metaheuristics across twenty-five benchmark functions and an engineering application. AEABC consistently outperformed its counterparts, demonstrating superior efficiency and accuracy. In a variable-sized problem (n = 10), the traditional ABC algorithm converged to 3.086 × 10<sup>6</sup>, while AEABC achieved a convergence of 2.0596 × 10<sup>−255</sup>, highlighting its robust performance. By addressing the shortcomings of the traditional ABC algorithm, AEABC significantly advances mathematical optimization, especially in engineering applications. This work underscores the significance of the inspiration of the traditional ABC algorithm in enhancing the capabilities of swarm intelligence.https://www.mdpi.com/2673-2688/5/4/109artificial bee colonyoptimizationswarm intelligencemetaheuristicsoptimal design |
| spellingShingle | Shaymaa Alsamia Edina Koch Hazim Albedran Richard Ray Adaptive Exploration Artificial Bee Colony for Mathematical Optimization AI artificial bee colony optimization swarm intelligence metaheuristics optimal design |
| title | Adaptive Exploration Artificial Bee Colony for Mathematical Optimization |
| title_full | Adaptive Exploration Artificial Bee Colony for Mathematical Optimization |
| title_fullStr | Adaptive Exploration Artificial Bee Colony for Mathematical Optimization |
| title_full_unstemmed | Adaptive Exploration Artificial Bee Colony for Mathematical Optimization |
| title_short | Adaptive Exploration Artificial Bee Colony for Mathematical Optimization |
| title_sort | adaptive exploration artificial bee colony for mathematical optimization |
| topic | artificial bee colony optimization swarm intelligence metaheuristics optimal design |
| url | https://www.mdpi.com/2673-2688/5/4/109 |
| work_keys_str_mv | AT shaymaaalsamia adaptiveexplorationartificialbeecolonyformathematicaloptimization AT edinakoch adaptiveexplorationartificialbeecolonyformathematicaloptimization AT hazimalbedran adaptiveexplorationartificialbeecolonyformathematicaloptimization AT richardray adaptiveexplorationartificialbeecolonyformathematicaloptimization |