# cuAPO: A CUDA-based Parallelization of Artificial Protozoa Optimizer

arXiv: https://arxiv.org/abs/2510.14982 · HTML: https://mlsanity.com/paper/2510.14982/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 1 of 1)

- [Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization](https://mlsanity.com/paper/2505.03512/) (2025)

## Abstract (arXiv)

Metaheuristic algorithms are widely used for solving complex problems due to their ability to provide near-optimal solutions. But the execution time of these algorithms increases with the problem size and/or solution space. And, to get more promising results, we have to execute these algorithms for a large number of iterations, requiring a large amount of time and this is one of the main issues found with these algorithms. To handle the same, researchers are now-a-days working on design and development of parallel versions of state-of-the-art metaheuristic optimization algorithms. We, in this paper, present a CUDA-based parallelization of state-of-the-art Artificial Protozoa Optimizer leveraging GPU acceleration. We implement both the existing sequential version and the proposed parallel version of Artificial Protozoa Optimizer for a performance comparison. Our experimental results calculated over a set of CEC2022 benchmark functions demonstrate a significant performance gain i.e. up to 6.7 times speed up is achieved with proposed parallel version. We also use a real world application, i.e., Image Thresholding to compare both algorithms.