Benchmarking for Metaheuristic Black-Box Optimization: Perspectives and Open Challenges
This is an incremental analysis for researchers in optimization algorithms, highlighting limitations in current benchmarking practices.
The paper addresses the lack of methods to relate performance assessments from synthetic benchmark problems to real-world optimization challenges in metaheuristic black-box optimization, and provides a constructive perspective on open challenges and research directions based on a mini-review.
Research on new optimization algorithms is often funded based on the motivation that such algorithms might improve the capabilities to deal with real-world and industrially relevant optimization challenges. Besides a huge variety of different evolutionary and metaheuristic optimization algorithms, also a large number of test problems and benchmark suites have been developed and used for comparative assessments of algorithms, in the context of global, continuous, and black-box optimization. For many of the commonly used synthetic benchmark problems or artificial fitness landscapes, there are however, no methods available, to relate the resulting algorithm performance assessments to technologically relevant real-world optimization problems, or vice versa. Also, from a theoretical perspective, many of the commonly used benchmark problems and approaches have little to no generalization value. Based on a mini-review of publications with critical comments, advice, and new approaches, this communication aims to give a constructive perspective on several open challenges and prospective research directions related to systematic and generalizable benchmarking for black-box optimization.