On GA-AIS Hybrids for Constrained Optimization Problems in Engineering

Abstract

A genetic algorithm (GA) is hybridized with an artificial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired by the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The resulting GA-AIS hybrid is tested in a suite of constrained optimization problems with continuous variables, as well as structural and mixed integer reliability engineering optimization problems. In order to improve the diversity of the population, a variant of the algorithm is developed with the inclusion of a clearing procedure. The performance of the GA-AIS hybrids is compared with that of alternative techniques, such as the Adaptive Penalty Method, and the Stochastic Ranking technique, which represent two different types of constraint handling techniques that have been shown to provide good results in the literature.Keywordsgenetic algorithmartificial immune systemhydrid algorithm