Relation between the Characteristics of Multiobjective Problems and the Search Performance of Evolutionary Multiobjective Optimization Algorithms

Abstract

Evolutionary multiobjective optimization (EMO) has attracted much attention over the last decade for solving multiobjective optimization problems. To examine the behavior of EMO algorithms, many test problems have been proposed so far. Each problem has different characteristics. One important feature is the relation between the solution space and the Pareto front size. For example, the distribution of randomly-generated initial solutions is much larger than the size of the Pareto front. In such a case, the population should shrink and converge towards the Pareto front. In this paper, we examine the characteristics of well-known multiobjective problems (e.g., knapsack problems and DTLZ problems) with two to ten objectives.