New Tunable Test Problems for Benchmarking Niching Methods for Multimodal Optimization
Ali Ahrari, Jonathan Edward Fieldsend, Mike Preuß, Xiaodong Li, Michael G. Epitropakis · Proceedings of the Genetic and Evolutionary Computation Conference · 2024
This study introduces novel tunable benchmark test problems for continuous box-constrained multimodal optimization (MMO). It first introduces a new approach to control the non-uniformity of distribution of global minima, a notable challenge in MMO. Then, it builds upon an existing procedure to create composite functions in which the severity of two distinguishable groups of MMO challenges can be controlled: i) challenges shared with global optimization (GO), such as ill-conditioning, and ii) challenges specific to MMO, such as non-uniform distribution of global minima. Eight new scalable and tunable MMO functions are then proposed, based on which a test suite of 16 continuous MMO test problems is suggested. This test suite is designed to be i) comprehensive, which means they simulate most, if not all, prominent challenges associated with MMO, ii) discriminating, which means test problems can disclose the gap between the performance of diverse MMO methods, and iii) illuminating, which means test problems can reveal and compare strengths and weaknesses of MMO methods. The code of these problems is made available in two different programming languages to encourage its adoption by the research community.