Artificial Immune Systems as a Bio-Inspired Optimization Technique and Its Engineering Applications

Abstract

The primary objective of this chapter is to introduce Artificial Immune Systems (AIS) as a relatively new bio-inspired optimization technique and to show its appeal to engineering applications. The advantages and disadvantages of the new computing paradigm, compared to other bio-inspired optimization techniques, such as Genetic Algorithms and other evolution computing strategies, are highlighted. Responding to some aforementioned disadvantages, a population adaptive based immune algorithm (PAIA) and its modified version for multi-objective optimization are put forward and discussed. A multi-stage optimization procedure is also proposed in which the first stage can be regarded as a vaccination process. It is argued that PAIA and its variations are the embodiments of some new characteristics which are recognized nowadays as the key to success for any stochastic algorithms dealing with continuous optimization problems, thus breathing new blood into the existing AIS family. The proposed algorithm is compared with the previously established evolutionary based optimization algorithms on ZDT and DTLZ test suites. The promising results encourage us to further extract a general framework from the PAIA as the guild to design immune algorithms. Finally, a real-world engineering problem relating to the building of a transparent fuzzy model for alloy steel is presented to show the merits of the algorithm.