The Educational Competition Optimizer

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:

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:

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.

Applications to machine learning models: In this module, we showcase how ECO can enhance the optimization of three key machine learning models: Support Vector Machines (SVM), Back-Propagation Neural Networks (BP), and Long Short-Term Memory Networks (LSTM).

Our implementation offers flexible integration of ECO with these models, providing users with powerful tools to achieve superior training efficiency and performance. The code is easy to use and can be adapted to other machine learning tasks. - SVM Optimization: Achieves better hyperparameter tuning for classification tasks. - BP Optimization: Enhances convergence rates and generalization for neural network training. - LSTM Optimization: Improves sequence learning for time-series forecasting and other applications. The module includes the source code for all three models, fully commented and customizable for your needs. You are welcome to explore and modify the implementation to fit specific research or application contexts.