# Review of Parameter Tuning Methods for Nature-Inspired Algorithms

arXiv: https://arxiv.org/abs/2308.15965 · HTML: https://mlsanity.com/paper/2308.15965/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 46 references that are in the map.
- First posted on arXiv 30 Aug 2023.

## Built on by (most cited first, 2 of 2)

- [Parameter Tuning of the Firefly Algorithm by Standard Monte Carlo and Quasi-Monte Carlo Methods](https://mlsanity.com/paper/2407.02537/) (2024)
- [Parameter Tuning of the Firefly Algorithm by Three Tuning Methods: Standard Monte Carlo, Quasi-Monte Carlo and Latin Hypercube Sampling Methods](https://mlsanity.com/paper/2504.18545/) (2025)

## Abstract (arXiv)

Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can largely influence the behaviour of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ensure the algorithm used for optimization may perform well and can be sufficiently robust for solving different types of optimization problems. This chapter reviews some of the main methods for parameter tuning and then highlights the important issues concerning the latest development in parameter tuning. A few open problems are also discussed with some recommendations for future research.