@article {pub3077,
	title = {Test Problems for Large-Scale Multi- and Many-Objective Optimization},
	author = {Ran Cheng AND Yaochu Jin AND Markus Olhofer AND Bernhard Sendhoff},
	year = {2016},
	abstract = { The interests in multi- and many-objective optimization have been rapidly increasing in the evolutionary computation community. However, in the literature, the majority of the study on multi- and many-objective optimization is limited to small-scale decision variables, notwithstanding real-world multi- and many-objective optimization problems may involve large-scale decision variables as well. One factor that limits the development of large-scale multi- and many-objective optimization is the lack of proper benchmark test problems, as it is believed that the multi- and many- objective evolutionary algorithms undergo a coevolutionary development with the test problems. To this end, we propose some test problems for large-scale multi- and many-objective optimization in this paper based on some general design principles. Specifically, the test problems are incorporated with some important characteristics of real-world problems, such as mixed separability among the decision variables, non-uniform correlations between decision variables and objective functions. To assess the proposed test problems, six representative algorithms are tested on them, for large-scale multi-objective optimization and large-scale many-objective optimization, respectively.
},
	publisher = {IEEE },
	journal = {IEEE Transactions on Cybernetics},
	volume = {47},
	number = {12},
	pages = {4108-4121}
}