Survivor optimizer: A competitive strategy for enhanced search efficiency
In recent years, although optimization algorithms are essential for solving complicated issues, they frequently struggle to find a balance between exploitation and exploration. Ineffective trade-offs may cause optimization to proceed slowly or to converge too soon. We suggest the Survivor Algorithm,...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447925003028 |
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| _version_ | 1849316299975426048 |
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| author | Arif Yelği |
| author_facet | Arif Yelği |
| author_sort | Arif Yelği |
| collection | DOAJ |
| description | In recent years, although optimization algorithms are essential for solving complicated issues, they frequently struggle to find a balance between exploitation and exploration. Ineffective trade-offs may cause optimization to proceed slowly or to converge too soon. We suggest the Survivor Algorithm, a cutting-edge method that improves search robustness and efficiency, to address this. It ensures a more efficient search procedure across various optimization landscapes by constantly adjusting its exploration and exploitation tactics. The Survivor Optimizer’s primary features and contributions include a process that draws inspiration from survival-based reality shows and balances exploration and exploitation through team-based competition, eliminations, and rewards. Together with the best-set selection approach, this competitive feature seeks to preserve diversity and efficiently identify the best answers. It consistently outperforms current approaches in extensive assessments on five real-world optimization problems and the CEC2017 benchmark functions. The algorithm achieves better results, confirmed by Wilcoxon test. |
| format | Article |
| id | doaj-art-0783bc921ded447d9adc2faa2fbe8082 |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-0783bc921ded447d9adc2faa2fbe80822025-08-20T03:51:53ZengElsevierAin Shams Engineering Journal2090-44792025-09-0116910356110.1016/j.asej.2025.103561Survivor optimizer: A competitive strategy for enhanced search efficiencyArif Yelği0Corresponding author.; Department of Computer Engineering, Istanbul Topkapi University, Istanbul, TurkeyIn recent years, although optimization algorithms are essential for solving complicated issues, they frequently struggle to find a balance between exploitation and exploration. Ineffective trade-offs may cause optimization to proceed slowly or to converge too soon. We suggest the Survivor Algorithm, a cutting-edge method that improves search robustness and efficiency, to address this. It ensures a more efficient search procedure across various optimization landscapes by constantly adjusting its exploration and exploitation tactics. The Survivor Optimizer’s primary features and contributions include a process that draws inspiration from survival-based reality shows and balances exploration and exploitation through team-based competition, eliminations, and rewards. Together with the best-set selection approach, this competitive feature seeks to preserve diversity and efficiently identify the best answers. It consistently outperforms current approaches in extensive assessments on five real-world optimization problems and the CEC2017 benchmark functions. The algorithm achieves better results, confirmed by Wilcoxon test.http://www.sciencedirect.com/science/article/pii/S2090447925003028Survivor algorithmOptimizationMetaheuristicSwarm intelligence |
| spellingShingle | Arif Yelği Survivor optimizer: A competitive strategy for enhanced search efficiency Ain Shams Engineering Journal Survivor algorithm Optimization Metaheuristic Swarm intelligence |
| title | Survivor optimizer: A competitive strategy for enhanced search efficiency |
| title_full | Survivor optimizer: A competitive strategy for enhanced search efficiency |
| title_fullStr | Survivor optimizer: A competitive strategy for enhanced search efficiency |
| title_full_unstemmed | Survivor optimizer: A competitive strategy for enhanced search efficiency |
| title_short | Survivor optimizer: A competitive strategy for enhanced search efficiency |
| title_sort | survivor optimizer a competitive strategy for enhanced search efficiency |
| topic | Survivor algorithm Optimization Metaheuristic Swarm intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2090447925003028 |
| work_keys_str_mv | AT arifyelgi survivoroptimizeracompetitivestrategyforenhancedsearchefficiency |