A Benchmark Generator for Dynamic Permutation-Encoded Problems

Several general benchmark generators (BGs) are available for the dynamic continuous optimization domain, in which generators use functions with adjustable parameters to simulate shifting landscapes. In the combinatorial domain the work is still on early stages. Many attempts of dynamic BGs are limited to the range of algorithms and combinatorial optimization problems (COPs) they are compatible with, and usually the optimum is not known during the dynamic changes of the environment. In this paper, we propose a BG that can address the aforementioned limitations of existing BGs. The proposed generator allows full control over some important aspects of the dynamics, in which several test environments with different properties can be generated where the optimum is known, without re-optimization.