Novel Zigzag-based Benchmark Functions for Bound Constrained Single Objective Optimization

The development and comparison of new optimization methods in general, and evolutionary algorithms in particular, rely heavily on benchmarking. In this paper, the construction of novel zigzag-based benchmark functions for bound constrained single objective optimization is presented. The new benchmark functions are non-differentiable, highly multimodal, and have a built-in parameter that controls the complexity of the function. To investigate the properties of the new benchmark functions two of the best algorithms from the CEC'20 Competition on Single Objective Bound Constrained Optimization, as well as one standard evolutionary algorithm, were utilized in a computational study. The results of the study suggest that the new benchmark functions are very well suited for algorithmic comparison.

Automated summary

This paper introduces novel zigzag-based benchmark functions for bound constrained single objective optimization, which are non-differentiable, highly multimodal, and have a built-in parameter for controlling complexity. Computational study results show that these new benchmark functions are highly suitable for algorithmic comparison.