Benchmarking for Metaheuristic Black-Box Optimization: Perspectives and Open Challenges

TL;DR

This paper reviews current benchmarking practices in metaheuristic black-box optimization, highlighting challenges in relating synthetic benchmarks to real-world problems and proposing directions for more systematic and generalizable evaluation methods.

Contribution

It provides a critical overview of existing benchmarking approaches and discusses open challenges and future research directions for improving black-box optimization assessments.

Findings

Many benchmarks lack relevance to real-world problems

Current methods have limited generalization capabilities

Open challenges include developing more systematic benchmarking approaches

Abstract

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.