Dynamic Environment Optimization Algorithm Based on Dynamic Particle Swarm Optimization

I believe the code explanations are comprehensive, but we can further enhance their value by expanding certain aspects to better demonstrate the algorithm's applications and operational mechanisms. For instance, we could add inline comments describing each function's purpose—such as the particle initialization routine, fitness evaluation method, and velocity update mechanism—along with guidance on parameter tuning for different optimization scenarios. Additionally, creating structured documentation or tutorials covering the dynamic particle swarm algorithm's convergence behavior and constraint handling would help users understand and implement the code more effectively. While the code has been validated through testing, incorporating unit tests for core components like neighborhood topology updates and dynamic environment adaptation would ensure long-term reliability. Ultimately, by preserving the algorithm's core principles while augmenting it with practical implementation details, we can create a more accessible and robust optimization tool for the research community.