A new self-adaptive approach for evolutionary multiobjective optimization

We propose in this paper a new strategy for self-adaptation in multiobjective evolutionary algorithms, which is based on information obtained from the implicit distribution created by a chaotic differential mutation operator. This technique is used to develop a self-adaptive evolutionary algorithm for multiobjective optimisation, and its efficiency is evaluated by means of a comparative study using well-known benchmark problems. The statistical analysis of the results shows that the proposed algorithm was able to outperform the NSGA-II in fourteen of the seventeen problems used. These results represent evidence for the adequacy of the proposed technique in solving the classes of multiobjective optimisation problems represented in the benchmark suites used.