  The development and identification of effective optimization algorithms for
non-convex real-world problems is a challenge in global optimization. Because
theoretical performance analysis is difficult, and problems based on models of
real-world systems are often computationally expensive, several artificial
performance test problems and test function generators have been proposed for
empirical comparative assessment and analysis of metaheuristic optimization
algorithms. These test problems however often lack the complex function
structures and forthcoming difficulties that can appear in real-world problems.
This communication presents a method to systematically build test problems with
various types and degrees of difficulty. By weighted composition of
parameterized random fields, challenging test functions with tunable function
features such as, variance contribution distribution, interaction order, and
nonlinearity can be constructed. The method is described, and its applicability
to optimization performance analysis is described by means of a few basic
examples. The method aims to set a step forward in the systematic generation of
global optimization test problems, which could lead to a better understanding
of the performance of optimization algorithms on problem types with particular
characteristics. On request an introductive MATLAB implementation of a test
function generator based on the presented method is available.
