New Benchmark Functions for Single-Objective Optimization Based on a Zigzag Pattern

Benchmarking plays a crucial role in both development of new optimization methods, and conducting proper comparisons between already existing particularly the field evolutionary computation. In this paper, we develop benchmark functions for bound-constrained single-objective that are based on zigzag function. The proposed function has three parameters control its behaviour difficulty resulting problems. Utilizing function, introduce four conduct extensive computational experiments to evaluate their performance as benchmarks. comprise using newly 100 different parameter settings comparison eight algorithms, which mix canonical methods best performing from Congress Evolutionary Computation competitions. Using results comparison, choose some parametrization devise an ambiguous set each problems introduces statistically significant ranking among but entire is with no clear dominating relationship algorithms. We also exploratory landscape analysis compare used Black-Box-Optimization-Benchmarking suite. suggest well suited algorithmic comparisons.