Best practices for comparing optimization algorithms

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.

Optim Eng (2017)

References in corpus (5)

• ParamILS: An Automatic Algorithm Configuration Framework
• Nuclear Energy Density Optimization
• Deterministic approaches for solving practical black-box global optimization problems
• A deterministic global optimization using smooth diagonal auxiliary functions
• The NC-proximal average for multiple functions

Cited by in corpus (7)

• COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
• SMGO: A Set Membership Approach to Data-Driven Global Optimization
• Experimental evaluation of architectural software performance design patterns in microservices
• Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms
• Quantum Dynamical Hamiltonian Monte Carlo
• EISPY2D: An Open-Source Python Library for the Development and Comparison of Algorithms in Two-Dimensional Electromagnetic Inverse Scattering Problems
• A Statistical Analysis for Per-Instance Evaluation of Stochastic Optimizers: Avoiding Unreliable Conclusions