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, 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.