# Nature-Inspired Optimization Algorithms: Challenges and Open Problems

arXiv: https://arxiv.org/abs/2003.03776 · HTML: https://mlsanity.com/paper/2003.03776/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2020.

## Built on by (most cited first, 11 of 11)

- [Hybrid Whale-Mud-Ring Optimization for Precise Color Skin Cancer Image Segmentation](https://mlsanity.com/paper/2311.13512/) (2023)
- [Optimizing Hospital Room Layout to Reduce the Risk of Patient Falls](https://mlsanity.com/paper/2101.03210/) (2021)
- [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)
- [Golden Tortoise Beetle Optimizer: A Novel Nature-Inspired Meta-heuristic Algorithm for Engineering Problems](https://mlsanity.com/paper/2104.01521/) (2021)
- [Dynamic Cat Swarm Optimization Algorithm for Backboard Wiring Problem](https://mlsanity.com/paper/2107.08908/) (2021)
- [Hybrid ACO-CI Algorithm for Beam Design problems](https://mlsanity.com/paper/2303.16908/) (2023)
- [Ten New Benchmarks for Optimization](https://mlsanity.com/paper/2309.00644/) (2023)
- [Variational Inference Using Material Point Method](https://mlsanity.com/paper/2407.20287/) (2024)
- [A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needs](https://mlsanity.com/paper/2506.04238/) (2025)
- [SALPA: Spaceborne LiDAR Point Adjustment for Enhanced GEDI Footprint Geolocation](https://mlsanity.com/paper/2511.17600/) (2025)
- [Analysis of Parameter Settings for the Bat Algorithm Using Variance Evolution](https://mlsanity.com/paper/2606.28644/) (2026)

## Abstract (arXiv)

Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness. However, there are some key issues concerning nature-inspired computation and swarm intelligence. This paper provides an in-depth review of some recent nature-inspired algorithms with the emphasis on their search mechanisms and mathematical foundations. Some challenging issues are identified and five open problems are highlighted, concerning the analysis of algorithmic convergence and stability, parameter tuning, mathematical framework, role of benchmarking and scalability. These problems are discussed with the directions for future research.