Automated Algorithm Configuration and Systematic Benchmarking for Heterogeneous MNK-Landscapes

MNK-landscapes are a class of multi-objective combinatorial optimisation problems that simulate interactions between system components with adjustable parameters. Recently, heterogeneous MNK-landscapes were introduced, which feature objectives with varying interdependencies, offering a new direction in multi-objective (multi-modal) landscape research. This study benchmarks various evolutionary multi-objective optimisation algorithms and a local search algorithm on such landscapes by means of automated algorithm configuration. Our systematic analysis yields various insights into the behaviour and competitiveness of these algorithms and reveals that, particularly, the omni-optimizer algorithm and iterated Pareto local search yield strong, complementary, performance. These findings facilitate the case for automated algorithm selection, which we also investigate in this paper.