Abstract
The interests in multi- and many-objective optimization have been rapidly increasing in the evolutionary computation community. However, most studies on multi- and many-objective optimization are limited to small-scale problems, despite the fact that many real-world multi- and many-objective optimization problems may involve a large number of decision variables. As has been evident in the history of evolutionary optimization, the development of evolutionary algorithms for solving a particular type of optimization problems has undergone a co-evolution with the development of test problems. To promote the research on large-scale multi- and many-objective optimization, we propose a set of generic test problems based on design principles widely used in the literature of multi- and many-objective optimization. In order for the test problems to be able to reflect challenges in real-world applications, we consider mixed separability between decision variables and non-uniform correlation between decision variables and objective functions. To assess the proposed test problems, six representative evolutionary multi- and many-objective evolutionary algorithms are tested on the proposed test problems. Our empirical results indicate that although the compared algorithms exhibit slightly different capabilities in dealing with the challenges in the test problems, none of them are able to efficiently solve these optimization problems, calling for the need for developing new evolutionary algorithms dedicated to large-scale multi- and many-objective optimization.