Parallel skeleton for multi-objective optimization

Many real-world problems are based on the optimization of more than one objective function. This work presents a tool for the resolution of multi-objective optimization problems based on the cooperation of a set of algorithms. The invested time in the resolution is decreased by means of a parallel implementation of an evolutionary team algorithm. This model keeps the advantages of heterogeneous island models but also allows to assign more computational resources to the algorithms with better expectations. The elitist scheme applied aims to improve the results obtained with single executions of independent evolutionary algorithms. The user solves the problem without the need of knowing the internal operation details of the used evolutionary algorithms. The computational results obtained on a cluster of PCs for some tests available in the literature are presented.