Multiobjective optimization by a modified artificial immune system algorithm

The aim of this work is to propose and validate a new multiobjective optimization algorithm based on the emulation of the immune system behavior. The rationale of this work is that the artificial immune system has, in its elementary structure, the main features required by other multiobjective evolutionary algorithms described in literature. The proposed approach is compared with the NSGA2 algorithm, that is representative of the state-of-the-art in multiobjective optimization. Algorithms are tested versus three standard problems (unconstrained and constrained), and comparisons are carried out using three different metrics. Results show that the proposed approach have performances similar or better than those produced by NSGA2, and it can become a valid alternative to standard algorithms.