Chaotic Optimization Algorithm with Comprehensive Code Comments

I would like to elaborate on my self-developed chaotic optimization algorithm implementation. While the algorithm appears straightforward at first glance, it encapsulates sophisticated concepts that facilitate deeper understanding of chaotic system characteristics. The implementation includes comprehensive code comments that break down each computational step, making it particularly accessible for beginners studying optimization algorithms. The algorithm leverages chaotic sequences for global exploration in the search space, typically using logistic maps or similar chaotic systems to generate pseudo-random numbers with ergodic properties. Key functions include chaos initialization, iterative mapping operations, and fitness evaluation mechanisms. The code structure demonstrates how chaotic variables are transformed into solution space parameters through appropriate mapping techniques. For those wishing to delve deeper into chaotic optimization methods, I strongly recommend hands-on experimentation with the code. Modifying parameters like chaotic maps, iteration strategies, or constraint handling approaches will provide practical insights into algorithmic essence and real-world application potential. The implementation serves as an excellent foundation for developing more advanced hybrid optimization techniques combining chaos theory with other metaheuristic methods.