Hybridization of Artificial Immune System Algorithms with Other AI Algorithms: A Review

Artificial Immune Systems (AIS) are a class of computationally intelligent systems inspired by the adaptive abilities of the natural immune system. However, AIS has limitations in computational efficiency, scalability, and handling of high-dimensional data. This paper reviews the fusion of AIS with other AI algorithms, including neural networks, evolutionary algorithms, and swarm intelligence, to enhance performance by leveraging their respective strengths. A comprehensive literature review was conducted analyzing over 12 papers on AIS hybridization published in the last decade. The works were categorized based on the integrated techniques and synthesized to identify benefits and challenges. The integration of AIS with the reviewed algorithms showed several benefits such as improved accuracy, faster processing, enhanced optimization and global search capabilities, and the ability to handle uncertainty and vagueness. However, challenges like increased complexity, parameter tuning, and algorithm selection were also noted. This review provides valuable insights into the fusion of AIS with diverse AI techniques for developing novel systems with enhanced adaptability, problem-solving capabilities, and real-world applicability. The identified trends pave the way for future innovations in AIS-based intelligent systems across security, medicine, finance, and other domains.