cuAPO: A CUDA-based Parallelization of Artificial Protozoa Optimizer
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917146301300736 |
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| author | Soliya, Henish Jain, Anugrah |
| author_facet | Soliya, Henish Jain, Anugrah |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_14982 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | cuAPO: A CUDA-based Parallelization of Artificial Protozoa Optimizer Soliya, Henish Jain, Anugrah Neural and Evolutionary Computing Artificial Intelligence Emerging Technologies 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. |
| title | cuAPO: A CUDA-based Parallelization of Artificial Protozoa Optimizer |
| topic | Neural and Evolutionary Computing Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2510.14982 |