Multi-objective Genetic Algorithms : Problem Difficulties and Construction of Test Problems

Abstract

In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty to converge to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult features of single-objective problems (such as multi-modality or deception) to be directly transferred to the corresponding multi-objective problem. In addition, test problems having features specific to multi-objective optimization are also constructed. The construction methodology allows a simpler way to develop test problems having other difficult and interesting problem features. More importantly, these difficult test problems will enable researchers to test their algorithms for specific aspects of multi-objective optimization in the coming years.