Artificial Immune Systems for Optimization

Artificial Immune Systems (AISs) are computational methods inspired by the biological immune system and thus classified as a nature-inspired meta-heuristic along with genetic algorithms, ant colony optimization, particle swarm optimization, and others. This chapter is focused on the application of AISs to solve optimization problems. Optimization is a mathematical principle largely applied to design and operational problems in all types of engineering, as well as a tool for formulating and solving inverse problems such as parameter identification in scientific and engineering situations. This chapter provides a survey of the applications of AISs in optimization. The main contributions are studied and contrasted with respect to the different concepts of the biological immune system, such as clonal selection, hypermutation, immune network, affinity, etc. that have inspired such techniques. The main types of optimization problems are considered, namely, (i) unconstrained problems, (ii) constrained problems, (iii) multimodal problems where several optima are to be obtained in a single run, and, finally (iv) multi-objective problems, where an approximation to the Pareto set is to be obtained as the result of a single run of the algorithm. Although AISs are good for solving optimization problems, useful features from other techniques have been combined with a “pure” AIS in order to generate hybrid AIS methods with improved performance.