Function Optimization Using Artificial Immune Clone Algorithm

This project implements function optimization using the Artificial Immune Clone Algorithm (AICA), which demonstrates excellent performance after execution. The artificial immune clone algorithm is an optimization technique based on immunological principles, simulating the cloning and mutation processes in biological immune systems to search for optimal solutions in function optimization problems. In typical implementations, the algorithm initializes a population of potential solutions (antibodies) and evaluates their fitness against the objective function. Key algorithmic components include: - Cloning Operation: High-fitness solutions undergo proportional cloning to increase population diversity - Mutation Mechanism: Introduces controlled variations through hypermutation operations to explore new regions of the solution space - Selection Process: Maintains population size by eliminating low-fitness solutions while preserving diversity The algorithm enhances exploration capabilities by cloning superior solutions to amplify their presence in the population, while mutation operations introduce novel solution candidates through strategic perturbations. Through experimental runs, we observed that the artificial immune clone algorithm performs effectively on function optimization problems, consistently locating optimal or near-optimal solutions with robust convergence characteristics. The implementation typically includes fitness evaluation functions, clone size calculation routines, and mutation rate controllers that adapt based on solution quality metrics.