Particle Swarm Optimization Algorithm with Immune Function

The algorithm employed in this project is the Particle Swarm Optimization with Immune Function, implemented within the MATLAB environment. This hybrid optimization technique proves highly effective for solving complex problems by integrating particle swarm optimization principles with immunological concepts to deliver superior solutions. The immune function introduces adaptive adjustment capabilities, enabling the algorithm to dynamically respond to varying optimization objectives. Key implementation aspects include parameter tuning through MATLAB's optimization工具箱 to enhance algorithm performance. Critical adjustable parameters encompass: - Population size (swarm initialization using `particleswarm` function) - Maximum iterations (controlled via `MaxIterations` option) - Immune function weights (implemented through affinity calculation and antibody selection mechanisms) The algorithm's architecture involves: 1. Particle velocity updates using cognitive and social components 2. Antibody diversity maintenance through vaccination operations 3. Memory cell preservation for elite solutions 4. Dynamic parameter adaptation via immune feedback mechanisms This robust and flexible optimization tool demonstrates particular strength in handling multimodal and dynamic optimization problems, with MATLAB implementations typically leveraging matrix operations for efficient swarm management and fitness evaluation through objective function handles.