Artificial Immune Algorithm Based on Clonal Selection Principle

In this paper, we discuss an artificial immune algorithm grounded in clonal selection principles. The algorithm architecture comprises three segments: the initial segment implements traditional genetic algorithms, while the subsequent segment incorporates the Clonal Selection Algorithm (CSA). Both methodologies can be applied to extremum-seeking for mathematical functions, each demonstrating unique characteristics in their optimization approaches.

Notably, genetic algorithms represent optimization techniques suitable for complex problem-solving. Traditional genetic algorithms mimic natural selection processes through coded operations including selection, crossover, and mutation to converge toward optimal solutions. Conversely, the CSA algorithm leverages immune mechanism principles inspired by human immunological systems. The CSA implementation employs clonal selection principles featuring antigen recognition, antibody cloning, and affinity maturation cycles to refine solutions. While both algorithms serve diverse problem domains, they exhibit distinct advantages and limitations in convergence speed, solution diversity, and computational efficiency.

This paper elaborates on algorithmic foundations, application scenarios, and experimental outcomes. We further analyze algorithmic strengths and constraints, alongside proposing future research directions. The discussion aims to provide valuable insights for researchers in related computational intelligence fields.