The educational competition optimizer

• DOI: 10.1080/00207721.2024.2367079
• Publication Date: 2024-07-01T14:34:15Z
• Authors: Junbo Lian; Ting Zhu; Ling Ma; Xincan Wu; Ali Asghar Heidari; Yi Chen; Huiling Chen; Guohua Hui

In recent research, metaheuristic strategies stand out as powerful tools for complex optimization, capturing widespread attention. This study proposes the Educational Competition Optimizer (ECO), an algorithm created diverse optimization tasks. ECO draws inspiration from competitive dynamics observed in real-world educational resource allocation scenarios, harnessing this principle to refine its search process. To further boost efficiency, divides iterative process into three distinct phases: elementary, middle, and high school. Through stepwise approach, gradually narrows down pool of potential solutions, mirroring gradual competition witnessed within systems. strategic approach ensures a smooth resourceful transition between ECO's exploration exploitation phases. The results indicate that attains peak performance when configured with population size 40. Notably, algorithm's efficacy does not exhibit strictly linear correlation size. comprehensively evaluate effectiveness convergence characteristics, we conducted rigorous comparative analysis, comparing against nine state-of-the-art algorithms. remarkable success efficiently addressing problems underscores applicability across domains. additional resources open-source code proposed can be accessed at https://aliasgharheidari.com/ECO.html https://github.com/junbolian/ECO.