Olympic Champion Algorithm (OCA): A new human-inspired metaheuristic algorithm for solving optimization problems

The complexity and computational demands of many optimization challenges have led to the rise and advancement of metaheuristic algorithms. These methods offer intelligent approaches for optimization, utilizing a structured and iterative process to search the solution space effectively. In this paper, we introduce a novel metaheuristic approach known as the Olympic Champion Algorithm (OCA), inspired by human behavior. The fundamental idea behind OCA is inspired by the structured process of transforming an ordinary human into an Olympic champion athlete. This inspiration is drawn from the structure of talent discovery, Coaching and Training, Local Competitions, Global Ambitions and Olympic quota, Social Support, Close Competitions, Resilience and Persistence and Intelligent Analysis of Opponents in Finals employed to choose the champion of each iteration. The implementation stages of OCA are modeled mathematically in three phases: population generation and exploration, exploitation, and description. "The performance of the proposed OCA was evaluated on 140 challenging benchmark functions and 15 well-known engineering design problems. The convergence curves and statistical data were compared with 22 highly cited optimization algorithms. Statistical validation was carried out using the Wilcoxon and Friedman tests. The results demonstrate that the OCA is one of the most powerful and effective metaheuristic optimization algorithms for various problems and applications, highlighting its distinctiveness. The article clearly presents the OCA algorithm, which uses a novel and effective solution update formula to achieve impressive results, and emphasizes its potential to advance future metaheuristic algorithms.