Efficiency analysis of parallel swarm intelligence using rapid range search in Euclidean space

• Autorzy: Michalski Łukasz , Sołtysik Andrzej , Woda Marek

Identyfikatory

DOI

10.24425/ijet.2025.153541

• Języki publikacji: EN

Abstrakty

EN

Swarm intelligence algorithms are widely recognized for their efficiency in solving complex optimization problems. However, their scalability poses challenges, particularly with large problem instances. This study investigates the time performance of swarm intelligence algorithms by leveraging parallel computing on both central processing units (CPUs) and graphics processing units (GPUs). The focus is on optimizing algorithms designed for range search in Euclidean space to enhance GPU execution. Additionally, the study explores swarm-inspired solutions specifically tailored for GPU implementations, emphasising improving efficiency in video rendering and computer simulations. The findings highlight the potential of GPU-accelerated swarm intelligence solutions to address scalability challenges in large-scale optimization, offering promising advancements in the field.

Słowa kluczowe

EN

swarm intelligence; parallel computing; range search; CUDA

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2025
• Tom: Vol. 71, No. 1
• Strony: 31--37
• Opis fizyczny: Bibliogr. 7 poz., tab., rys.

Twórcy

autor

Michalski Łukasz

• Wroclaw University of Technology

autor

Sołtysik Andrzej

• Wroclaw University of Technology

• https://orcid.org/0009-0003-3821-9462

autor

Woda Marek

• Wroclaw University of Technology

• https://orcid.org/0000-0002-3956-9427

Bibliografia

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• [4] C. Delgado-Mata, J. Ib´a˜nez-Mart´ınez, S. Bee, R. Ruiz-Rodarte, and R. Aylett, “On the Use of Virtual Animals with Artificial Fear in Virtual Environments,” New Generation Comput., vol. 25, pp. 145–169, 02 2007.
• [5] C. Hartman and B. Benes, “Autonomous boids.” Journal of Visualization and Computer Animation, vol. 17, pp. 199–206, 01 2006.
• [6] D. R. Karger, “Advanced Algorithms,” in Advanced Algorithms MIT Course No.6.5210/18.415. MIT OpenCourseWare, 2022. [Online]. Available: https://6.5210.csail.mit.edu/
• [7] X. Li, W. Cai, and S. J. Turner, “Efficient Neighbor Searching for Agent-Based Simulation on GPU,” ser. DS-RT ’14. USA: IEEE Computer Society, 2014, p. 87–96. [Online]. Available: https://doi.org/10.1109/DS-RT.2014.19

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).