A Concentration-based Artificial Immune Network for continuous optimization

Metaheuristics based on the Artificial Immune System (AIS) framework, especially those inspired by the Immune Network theory, are known to be capable of stimulating the generation of diverse sets of solutions for a given problem, even though they generally implement very simple mechanisms to control the dynamics of the network. In the AIS literature, several studies propose different models that try to explain the behavior of immune networks, which are generally based on the concentration of antibodies and tend to better mimic some aspects of such complex systems. Therefore, in this work we propose a novel immune-inspired algorithm for optimization, named cob-aiNet (Concentration-based Artificial Immune Network), that intends to explore such network models and introduce new mechanisms to better control the dynamics of the network, so that a broader coverage of promising regions of the search space can be achieved. This property of cob-aiNet was verified in experimental analyses, in which the algorithm was compared to two other AIS proposals and also to all the competitors from the 2005 CEC Special Session on RealParameter Optimization.