A revamped black winged kite algorithm with advanced strategies for engineering optimization
Abstract This paper proposed the Revamped Black-winged Kite Algorithm (RBKA), a newly developed optimization intelligence method to boost the performance of classic Black-winged Kite Algorithm (BKA). It employs three revolutionary tactics to enhance its efficiency. Initially, the technique uses a lo...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-93370-1 |
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| author | Sarada Mohapatra Deepa Kaliyaperumal Farhad Soleimanian Gharehchopogh |
| author_facet | Sarada Mohapatra Deepa Kaliyaperumal Farhad Soleimanian Gharehchopogh |
| author_sort | Sarada Mohapatra |
| collection | DOAJ |
| description | Abstract This paper proposed the Revamped Black-winged Kite Algorithm (RBKA), a newly developed optimization intelligence method to boost the performance of classic Black-winged Kite Algorithm (BKA). It employs three revolutionary tactics to enhance its efficiency. Initially, the technique uses a logistic map for population initialization, swapping random generation to enhance global search effectiveness and fast convergence. Secondly, a novel search strategy is devised, incorporating chaotic perturbation factor-based attack behaviour and Brownian motion-based migratory behaviour to find an ideal balance between exploration and exploitation. An opposition-based learning (OBL) technique is utilized to tackle stagnation in local optima and augment the algorithm’s capacity to identify global solutions. The effectiveness and stability of RBKA are systematically evaluated using established benchmark functions, such as CEC2005, CEC2020, and CEC2022. Additionally, the method is utilized in fifteen constraint optimization problems from the CEC2011 test suite and six complex engineering design problems, demonstrating its versatility and efficacy. The comparative statistical evaluation demonstrates that RBKA outperforms the other intelligence algorithms in terms of convergence speediness, stability, and overall effectiveness, positioning it as a robust and adaptable solution for complex optimization problems. |
| format | Article |
| id | doaj-art-1eb5255d2ee04071ad5d0ae055951a59 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-1eb5255d2ee04071ad5d0ae055951a592025-08-20T02:29:51ZengNature PortfolioScientific Reports2045-23222025-05-0115114510.1038/s41598-025-93370-1A revamped black winged kite algorithm with advanced strategies for engineering optimizationSarada Mohapatra0Deepa Kaliyaperumal1Farhad Soleimanian Gharehchopogh2Department of EEE, Amrita School of Engineering, Amrita Vishwa VidyapeethamDepartment of EEE, Amrita School of Engineering, Amrita Vishwa VidyapeethamDepartment of Computer Engineering, Urmia Branch, Islamic Azad UniversityAbstract This paper proposed the Revamped Black-winged Kite Algorithm (RBKA), a newly developed optimization intelligence method to boost the performance of classic Black-winged Kite Algorithm (BKA). It employs three revolutionary tactics to enhance its efficiency. Initially, the technique uses a logistic map for population initialization, swapping random generation to enhance global search effectiveness and fast convergence. Secondly, a novel search strategy is devised, incorporating chaotic perturbation factor-based attack behaviour and Brownian motion-based migratory behaviour to find an ideal balance between exploration and exploitation. An opposition-based learning (OBL) technique is utilized to tackle stagnation in local optima and augment the algorithm’s capacity to identify global solutions. The effectiveness and stability of RBKA are systematically evaluated using established benchmark functions, such as CEC2005, CEC2020, and CEC2022. Additionally, the method is utilized in fifteen constraint optimization problems from the CEC2011 test suite and six complex engineering design problems, demonstrating its versatility and efficacy. The comparative statistical evaluation demonstrates that RBKA outperforms the other intelligence algorithms in terms of convergence speediness, stability, and overall effectiveness, positioning it as a robust and adaptable solution for complex optimization problems.https://doi.org/10.1038/s41598-025-93370-1MetaheuristicsBlack-winged kite algorithmOptimizationEngineering design problems |
| spellingShingle | Sarada Mohapatra Deepa Kaliyaperumal Farhad Soleimanian Gharehchopogh A revamped black winged kite algorithm with advanced strategies for engineering optimization Scientific Reports Metaheuristics Black-winged kite algorithm Optimization Engineering design problems |
| title | A revamped black winged kite algorithm with advanced strategies for engineering optimization |
| title_full | A revamped black winged kite algorithm with advanced strategies for engineering optimization |
| title_fullStr | A revamped black winged kite algorithm with advanced strategies for engineering optimization |
| title_full_unstemmed | A revamped black winged kite algorithm with advanced strategies for engineering optimization |
| title_short | A revamped black winged kite algorithm with advanced strategies for engineering optimization |
| title_sort | revamped black winged kite algorithm with advanced strategies for engineering optimization |
| topic | Metaheuristics Black-winged kite algorithm Optimization Engineering design problems |
| url | https://doi.org/10.1038/s41598-025-93370-1 |
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