An archiving strategy based on the Convex Hull of Individual Minima for MOEAs

Diversity plays an important role in evolutionary multi-objective optimization. Because of this, a number of density estimators (i.e., mechanisms that help to maintain diversity) have been proposed since the early days of multi-objective evolutionary algorithms (MOEAs). Fitness sharing and niching were among the most popular density estimator used with non-elitist MOEAs, but their main drawback was their high dependence on the niche radius, which was normally difficult to set. In recent years, the use of external archives to store the nondominated solutions found by an elitist MOEA has become popular. This has motivated an important amount of research related to archiving techniques for MOEAs. In this paper, we contribute to such literature by introducing a new archiving strategy based on the Convex Hull of Individual Minima (CHIM). Our proposed approach is compared with respect to two competitive MOEAs (NSGA-II and SPEA2) using standard test problems and performance measures taken from the specialized literature.