Immune Algorithm Implementation for Multi-peak, Multi-extremum Function Surface Fitting

The immune algorithm provides an effective approach for fitting complex planar surfaces containing multiple peaks and extreme values. This biologically-inspired optimization technique mimics the immune system's mechanism of antibody-antigen interactions, employing evolutionary processes and selection mechanisms to identify optimal solutions. Key implementation aspects include: - Antibody representation of candidate solutions using real-valued or binary encoding - Affinity calculation between antibodies and antigens (objective function evaluation) - Clonal selection and hypermutation operators for local refinement - Memory cell maintenance for preserving diverse optimal solutions When applied to surface fitting problems, the algorithm can effectively locate multiple peaks and extremum points on planar surfaces, enabling comprehensive analysis of function characteristics. The code implementation typically involves population initialization, fitness evaluation, cloning with mutation rates proportional to solution quality, and diversity preservation through suppression mechanisms. This makes immune algorithms particularly suitable for solving complex multimodal optimization problems where traditional methods might converge to local optima.