Information Entropy-Based Immune Algorithm: A Modified Immune Algorithm Approach

The information entropy-based immune algorithm discussed in this article represents a modified version of traditional immune algorithms. While the methodology maintains relative simplicity, it demonstrates superior effectiveness when applied to specific problem types. By incorporating information entropy concepts, this algorithm achieves improved handling of problem complexity and uncertainty, thereby enhancing both solution accuracy and computational efficiency. Key implementation aspects typically include entropy calculation functions to measure antibody diversity and mechanisms for dynamic adjustment of immune cell concentrations. The algorithm's strength lies in its ability to maintain population diversity through entropy-based regulation, preventing premature convergence. Consequently, the information entropy-based immune algorithm presents a valuable approach worthy of research and practical application. These supplemental details aim to provide a more comprehensive understanding of the algorithm's advantages and domain suitability, including its code implementation structure involving antigen recognition, antibody generation, and entropy-driven selection processes.