Test problems for multiple objective evolutionary algorithms

Multiple objective evolutionary algorithms (MOEAs) need to be tested on scalable test problems known as multiple objective optimization problems (MOPs). This study analyzes MOPs in terms of the constraint condition, the uniform representation of the Pareto-optimal front and the ability to reach the true Pareto-optimal front. For each item, test problems were developed for each aspect based on the fast elitist non-dominated sorting genetic algorithm II (NSGA-II). The results show that these problems effectively test the algorithm's performance in all three aspects, especially the constraint condition.