Handling Diversity in Evolutionary Multiobjective Optimistion

Abstract

In evolutionary multiobjective optimisation (EMO), the diversity of the set of non-dominated solutions used to be handled by the niching and fitness sharing technique. The main downside of this technique is the need to set the niche radius. Quite recently, new techniques have emerged and proved to be more successful. The grid-based density of the adaptive grid algorithm (AGA), the crowding-distance technique of the nondominated sorting genetic algorithm (NSGA-II), and the archive truncation procedure of the strength Pareto evolutionary algorithm (SPEA2) are the latest successful methods that ensure a better diversity than the traditional less effective and computationally expensive niching method. In this work, a crowding-dispersion technique which is based on the Pareto potential regions (PPR), is proposed and compared to three recent techniques.