Structural design of the danger model immune algorithm

The traditional immune algorithm (IA) is based on a self–nonself biological immunity mechanism. Recently, a novel immune theory called the danger model theory has provided more suitable biological information for data handling compared with the self–nonself mechanism. According to the danger model theory and based on past experiences of the genetic and artificial IA, we present the Danger Model Immune Algorithm (DMIA) that differs from the traditional IA in terms of the self–nonself biological immunity mechanism. We define a danger area and a danger signal in DMIA. We use the selection, mutation, and specific danger operators to update the population. The algorithm can achieve complex problem optimization. Simulation studies demonstrate that DMIA exhibits a higher efficiency than traditional genetic algorithms and other algorithms when considering a number of complicated functions.