  In the rapidly evolving optimization and metaheuristics domains, the efficacy
of algorithms is crucially determined by the benchmark (test) functions. While
several functions have been developed and derived over the past decades, little
information is available on the mathematical and visual description, range of
suitability, and applications of many such functions. To bridge this knowledge
gap, this review provides an exhaustive survey of more than 300 benchmark
functions used in the evaluation of optimization and metaheuristics algorithms.
This review first catalogs benchmark and test functions based on their
characteristics, complexity, properties, visuals, and domain implications to
offer a wide view that aids in selecting appropriate benchmarks for various
algorithmic challenges. This review also lists the 25 most commonly used
functions in the open literature and proposes two new, highly dimensional,
dynamic and challenging functions that could be used for testing new
algorithms. Finally, this review identifies gaps in current benchmarking
practices and suggests directions for future research.
