Best practices for comparing optimization algorithms

TL;DR

This paper reviews the complexities of benchmarking optimization algorithms, offering guidelines and highlighting pitfalls to ensure fair, unbiased, and effective comparisons, along with suggestions for improving future benchmarking practices.

Contribution

It provides a systematic review of optimization algorithm benchmarking, offering practical recommendations and identifying common pitfalls to enhance fairness and reliability in evaluations.

Findings

Identifies key challenges in fair comparison of algorithms

Provides step-by-step guidelines for benchmarking

Highlights common pitfalls and reporting issues

Abstract

Comparing, or benchmarking, of optimization algorithms is a complicated task that involves many subtle considerations to yield a fair and unbiased evaluation. In this paper, we systematically review the benchmarking process of optimization algorithms, and discuss the challenges of fair comparison. We provide suggestions for each step of the comparison process and highlight the pitfalls to avoid when evaluating the performance of optimization algorithms. We also discuss various methods of reporting the benchmarking results. Finally, some suggestions for future research are presented to improve the current benchmarking process.