Scalability Analysis of Entropy-Guided Optimization in High Dimensions

This paper presents the EGO-Optimizer, a Python library implementing Entropy-Guided Optimization (EGO) for large-scale continuous optimization tasks. The study introduces a Shannon-entropy feedback loop to quantify population diversity in real-time, allowing for adaptive control of the exploration-exploitation trade-off. Key Contributions: Entropy-Driven Mutation: Replaces fixed scaling with a real-time entropy-normalized factor to prevent premature convergence. High-Dimensional Scalability: Benchmarked on 30D, 50D, and 100D landscapes against standard metaheuristics (DE, PSO, GA). Performance Validation: Evaluated across 67 CEC-style benchmark instances (unimodal, multimodal, and composition functions). Source Code: The full implementation, documentation, and usage examples are available on GitHub:https://github.com/Draster2k/ego