3RSTVBO: An improved meta-heuristic optimization algorithm for solving optimization problems

In this paper, a new meta-heuristic optimization algorithm called the Spider-Tailed Viper and Bird Optimizer (STVBO) is introduced, 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 for solving certain optimization problems, thus transforming it into a more powerful algorithm. In doing so, the paper employs the Random Opposition-Based Learning (ROBL) technique to help the algorithm escape local optima and accelerate convergence. This technique is integrated with STVBO to propose the Random opposition-based learning Spider-Tailed Viper and Bird Optimizer (RSTVBO). To evaluate the performance of RSTVBO, benchmark functions including CEC2017, and CEC2019 as well as four real-world engineering problems are utilized, and the results demonstrate the superior performance of RSTVBO. Moreover, the Wilcoxon rank sum test and Friedman statistical test confirm that the superiority of RSTVBO is statistically significant.