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 |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447925003028 |
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| Summary: | 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. |
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| ISSN: | 2090-4479 |