In this paper, we carefully select (or modify) 15 test problems with diverse properties to construct a benchmark test suite, aiming to promote the research of evolutionary many-objective optimization (EMaO) via suggesting a set of test problems with a good representation of various real-world scenarios.
Several test problem suites which were designed for multi-objective optimization have still been dominantly used in many-objective optimization.
However, in contrast to the rapid development of algorithm design, performance investigation and comparison of algorithms have received little attention. The failure of conventional Pareto-based multi-objective evolutionary algorithms in dealing with MaOPs motivates various new approaches. Such problems, called many-objective optimization problems (MaOPs), pose great challenges to the area of evolutionary computation. In the real world, it is not uncommon to face an optimization problem with more than three objectives.
