2020/07/01 by Ramses Sala, Ralf Müller, Sala, Ramses +1 · 2 citations
Computer Science · Engineering · #65K99 #68-02 #68W50 (primary) #90-02 #90C26 #90C59 (secondary) #A.1 #Advanced Multi-Objective Optimization Algorithms #B.8.0 #FOS: Computer and information sciences #FOS: Mathematics #G.1.6 #G.4 #I.2.8 #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Optimization and Control (math.OC) #Performance (cs.PF) #Vehicle Routing Optimization Methods
paper · doi:10.48550/arxiv.2007.00541
openalex publication_date 2020/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.