Source Code for Chaotic Differential Evolution Algorithm

This resource provides self-developed source code implementation for a chaotic differential evolution algorithm complemented by a technical paper detailing its foundations. The chaotic differential evolution algorithm is an optimization method combining natural evolution principles with chaos theory, applicable across domains like image processing, control engineering, and artificial intelligence. The accompanying paper explains the algorithm's core mechanisms—including chaotic sequence initialization, mutation operations with chaos-based parameter adaptation, and crossover strategies—while demonstrating practical implementations through modular functions. Beginners can study the documentation and experiment with the provided MATLAB/Python code structure featuring key components such as population initialization using chaotic maps, fitness evaluation modules, and adaptive chaos-driven mutation operators to deepen their understanding of the algorithm's workflow and potential applications.