Common Benchmark Functions for Metaheuristic Evaluation: A Review

Kashif Hussain (Universiti Tun Hussein Onn Malaysia, Johor, Malaysia)
Mohd Najib Mohd Salleh (Universiti Tun Hussein Onn Malaysia, Johor, Malaysia)
Shi Cheng (Shaanxi Normal University, Xi’an, China)
Rashid Naseem (City University of Science and Information Technology, Peshawar, Pakistan)

JOIV : International Journal on Informatics Visualization
Vol 1, No 4-2 (2017): The Advancement of System and Applications

In literature, benchmark test functions have been used for evaluating performance of metaheuristic algorithms. Algorithms that perform well on a set of numerical optimization problems are considered as effective methods for solving real-world problems. Different researchers choose different set of functions with varying configurations, as there exists no standard or universally agreed test-bed. This makes hard for researchers to select functions that can truly gauge the robustness of a metaheuristic algorithm which is being proposed. This review paper is an attempt to provide researchers with commonly used experimental settings, including selection of test functions with different modalities, dimensions, the number of experimental runs, and evaluation criteria. Hence, the proposed list of functions, based on existing literature, can be handily employed as an effective test-bed for evaluating either a new or modified variant of any existing metaheuristic algorithm. For embedding more complexity in the problems, these functions can be shifted or rotated for enhanced robustness.