Supercell thunderstorm algorithm (STA): a nature-inspired metaheuristic algorithm for engineering optimization

Abstract

In this paper, an optimization algorithm called supercell thunderstorm algorithm (STA) is proposed. STA draws inspiration from the strategies employed by storms, such as spiral motion, tornado formation, and the jet stream. It is a computational algorithm specifically designed to simulate and model the behavior of supercell thunderstorms. These storms are known for their rotating updrafts, strong wind shear, and potential for generating tornadoes. The optimization procedures of the STA algorithm are based on three distinct approaches: exploring a divergent search space using spiral motion, exploiting a convergent search space through tornado formation, and navigating through the search space with the aid of the jet stream. To evaluate the effectiveness of the proposed STA algorithm in achieving optimal solutions for various optimization problems, a series of test sequences were conducted. Initially, the algorithm was tested on a set of 23 well-established functions. Subsequently, the algorithm’s performance was assessed on more complex problems, including ten CEC2019 test functions, in the second experimental sequence. Finally, the algorithm was applied to five real-world engineering problems to validate its effectiveness. The experimental results of the STA algorithm were compared to those of contemporary metaheuristic methods. The analysis clearly demonstrates that the developed STA algorithm outperforms other methods in terms of performance.