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

The Information Entropy-Based Immune Algorithm is an improved immune optimization approach that incorporates information entropy concepts into traditional immune algorithms, thereby enhancing algorithmic stability and convergence performance. This method proves particularly effective for solving specific types of optimization problems, including feature selection, classification tasks, and combinatorial optimization scenarios.

The core concept of this algorithm utilizes information entropy to measure the diversity and dissimilarity among antibodies (i.e., candidate solutions). By computing the information entropy of antibodies, the algorithm dynamically adjusts selection pressure to prevent premature convergence to local optima. Compared with conventional immune algorithms, this approach achieves a more effective balance between global exploration and local optimization capabilities, consequently improving the reliability of final solutions.

The implementation of Information Entropy-Based Immune Algorithm follows a structured workflow involving: initialization of antibody population, affinity (fitness) calculation, information entropy computation for selection strategy adjustment, clone and mutation operations, and final optimal solution selection. Key implementation aspects include using entropy calculations to maintain population diversity through diversity maintenance functions, and adaptive parameter tuning based on entropy measurements. Due to its self-adaptive search strategy adjustment capability, the algorithm demonstrates superior performance advantages in specific optimization domains such as high-dimensional feature selection and complex system optimization tasks. The algorithm typically employs entropy-based diversity measures in the selection operator and integrates entropy thresholds in the mutation probability adjustment mechanism.