The educational competition optimizer
Junbo Lian, Ting Zhu, Ling Ma, Xincan Wu, Ali Asghar Heidari, Yi Chen, Huiling Chen, Guohua Hui
International journal of systems science, Taylor & Francis Online
DOI, 2024
Junbo Lian, Ting Zhu, Ling Ma, Xincan Wu, Ali Asghar Heidari, Yi Chen, Huiling Chen, Guohua Hui
International journal of systems science, Taylor & Francis Online
DOI, 2024
Algorithm Design: "ECO employs a unique three-phase iterative process—elementary, middle, and high school—to refine its search and enhance efficiency."
We are happy to share the new Educational Competition Optimizer (ECO), and we invite you to experience its performance and innovative research results firsthand.
Abstract: The Educational Competition Optimizer (ECO) is a novel algorithm inspired by the competitive nature of the educational system. It simulates the dynamics of educational stages—primary, middle, and high school—to address optimization problems. ECO uses a balance of exploration and exploitation to find optimal solutions. The algorithm demonstrates effectiveness through comparison with various optimization methods and applications to real-world problems. .
Educational Competition Optimizer (ECO): The Educational Competition Optimizer (ECO) is an optimization algorithm inspired by the competitive aspects of the education system. It models different stages of education to address complex optimization problems efficiently. Below is an overview of the core components and mechanisms of ECO.
Conceptual Foundation: ECO emulates the competitive dynamics of various educational stages. Schools and students are simulated to balance exploration and exploitation within the optimization process, representing the different stages of primary, middle, and high school education.
Algorithm Structure: ECO operates through several distinct phases:
Initialization: The algorithm begins with a population initialized using logistic chaos mapping, simulating diverse initial positions within the search space.
Primary School Stage: Schools determine their locations based on the average position of the population, while students aim for the nearest schools.
Middle School Stage: Schools use a more sophisticated approach by considering both average and optimal population locations, and students adjust their positions based on their academic performance.
High School Stage: Schools select locations considering the best and worst positions, and students converge towards the best solution.
Termination: The algorithm concludes when a stopping criterion is met, such as convergence or reaching a maximum number of iterations.
Primary School Stage: At this stage, schools and students interact based on proximity, with schools adjusting locations to the average position of the student population and students aiming for nearby schools.
Middle School Stage: Schools refine their locations by considering both average and optimal positions, while students' learning motivation influences their position adjustments.
High School Stage: Schools adopt a comprehensive approach by evaluating the best and worst positions, and students work towards the best identified solution.
Summary of ECO Stages: ECO incorporates:
Primary School Stage: Focuses on proximity and average location adjustments.
Middle School Stage: Integrates average and optimal location considerations, with motivation influencing adjustments.
High School Stage: Emphasizes comprehensive evaluation and convergence towards the best solution.
The ECO algorithm simulates educational competition to balance exploration and exploitation. It starts with a diverse initial population and iteratively refines solutions across different educational stages, demonstrating its potential as an effective optimization tool.
The Educational Competition Optimizer, International journal of systems science, Taylor & Francis Online, 2024 DOI