FEMOEA: a fast and efficient multi-objective evolutionary algorithm

A new multi-objective evolutionary algorithm which can be applied to many nonlinear multi-objective optimization problems is proposed. Its aim is to obtain a discrete fixed size set approximating the complete Pareto-front quickly. It adapts ideas from different multiand single-objective optimization evolutionary algorithms, although it also incorporates new devices, namely, a new method to improve the efficiency of points (no gradient information is used) and a new stopping rule, which help to improve the quality of the obtained approximation of the Pareto-front and to reduce the computational requirements, respectively. In order to analyze its performace, it has been compared with the reference algorithms NSGA-II and SPEA2 on a set of twenty benchmark problems. Several quality indicators have been considered, namely, hypervolume, average distance, additive epsilon indicator, spread and spacing. According to the computational results and statistical analysis performed, the new algoritm, named FEMOEA, outperforms, in average, both NSGA-II and SPEA2 for all the quality indicators.