SBTO

Savannah Bengal Tiger Optimization

The article proposes a novel meta-heuristic algorithm, named "Savannah Bengal Tiger Optimization" (SBTO), inspired by the hunting behavior evolved from the survival experiments of Bengal tigers, for solving constrained optimization problems and engineering applications. The iterative process of the algorithm mainly consists of three strategies: prey search, stealth approach, and hunting, which maintain the exploration capability of the algorithm by controlling the positional relationship between the prey and the Bengal tigers. The performance of SBTO is compared and evaluated against several popular algorithms and recently published algorithms on CEC2017, CEC2020, CEC2022 benchmark functions, and 9 real-world engineering problems. The final experimental results effectively demonstrate the outstanding optimization performance of SBTO across different problems.