A novel partition strategy for efficient implementation of 3D Cellular Genetic Algorithms

Letras M, Morales-Reyes A, Cumplido R, Martínez-Peñaloza MG, Feregrino-Uribe C (2024)


Publication Type: Journal article

Publication year: 2024

Journal

Book Volume: 104

Article Number: 104986

DOI: 10.1016/j.micpro.2023.104986

Abstract

Solving optimization problems while fulfilling real-time constraints requires high algorithmic and processing performance. Cellular Genetic Algorithms (cGAs) have been competitive at difficult single objective combinatorial and continuous domain problems. Moreover, it has been demonstrated that structural properties in cGAs, such as population topology dimension, local neighborhood configuration and ad-hoc selection mechanisms, allow not only further algorithmic improvement but also, these characteristics can be combined at hardware level for acceleration. In this article, a novel partition strategy to exploit 3D cGAs population dynamics on a 2D processing array using Field Programmable Gate Arrays (FPGAs) as the target processing platform is presented. The proposed architecture fits as an optimization module within an embedded system where real-time constraints must be fulfilled. Therefore, it is important to find an optimal trade-off between hardware resources usage and searching time. Overall results demonstrate that the proposed architecture can run up to 90 MHz when tackling continuous benchmark functions. Moreover, speed-up of up to three and two orders of magnitude are achieved in comparison to a single CPU and a parallel GPU respectively.

Involved external institutions

How to cite

APA:

Letras, M., Morales-Reyes, A., Cumplido, R., Martínez-Peñaloza, M.G., & Feregrino-Uribe, C. (2024). A novel partition strategy for efficient implementation of 3D Cellular Genetic Algorithms. Microprocessors and Microsystems, 104. https://doi.org/10.1016/j.micpro.2023.104986

MLA:

Letras, Martin, et al. "A novel partition strategy for efficient implementation of 3D Cellular Genetic Algorithms." Microprocessors and Microsystems 104 (2024).

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