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ESTVBO: a powerful meta-heuristic optimization algorithm for solving optimization problems
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DOI：10.1007/s10586-026-06346-9.png)
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Abstract
En 中文
In recent years, many meta-heuristic algorithms have faced challenges such as premature convergence and slow convergence rates. To address these limitations, this study introduces a nature-inspired optimization algorithm called the Spider-Tailed Viper and Bird Optimizer (STVBO), which is inspired by the hunting strategy of the Iranian spider-tailed viper. The STVBO algorithm demonstrates superior performance compared to rival algorithms. However, STVBO can be enhanced in terms of convergence rate and avoiding local optima in certain optimization problems, thus transforming it into a more powerful algorithm. In doing so, the paper employs the Enhanced Opposition-Based Learning (EOBL) technique to help the algorithm escape local optima and accelerate convergence. This technique is integrated with STVBO to propose the Enhanced Spider-Tailed Viper and Bird Optimizer (ESTVBO). To evaluate the performance of ESTVBO, benchmark functions, including CEC2005, CEC2017, and CEC2019, as well as ten real-world engineering problems, are utilized, and the results demonstrate the superior performance of ESTVBO. Moreover, the Wilcoxon rank-sum test and Friedman statistical test confirm that the superiority of ESTVBO is statistically significant.
Keywords:
Spider-Tailed Viper and Bird Optimizer (STVBO)
Meta-heuristic
Opposition-based learning
Enhanced opposition-based learning
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4.1
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5.0K
Citations:
7.5K
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