A Novel Hardware Implementation of the Compact Genetic Algorithm

Moreno-Armendáriz, Marco A.; Cruz-Cortés, Nareli; León-Javier, Alejandro

doi:10.1109/reconfig.2010.54

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Other works employed cGA on various hardware devices [37,[233][234][235][236][237]. More recently, a GPU-enabled implementation of cGA was presented in [238], to solve a "seriously" large-scale (up to 10 million variables) Integer Linear Programming problem taken from [239], as well as continuous and discrete versions of the OneMax benchmark problem of up to one billion variables.…”

Section: Binary/discrete Compact Optimisation Algorithmsmentioning

confidence: 99%

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

Khalfi,

Caraffini,

Iacca

2023

International Journal of Intelligent Systems

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In the last three decades, the field of computational intelligence has seen a profusion of population-based metaheuristics applied to a variety of problems, where they achieved state-of-the-art results. This remarkable growth has been fuelled and, to some extent, exacerbated by various sources of inspiration and working philosophies, which have been thoroughly reviewed in several recent survey papers. However, the present survey addresses an important gap in the literature. Here, we reflect on a systematic categorisation of what we call “lightweight” metaheuristics, i.e., optimisation algorithms characterised by purposely limited memory and computational requirements. We focus mainly on two classes of lightweight algorithms: single-solution metaheuristics and “compact” optimisation algorithms. Our analysis is mostly focused on single-objective continuous optimisation. We provide an updated and unified view of the most important achievements in the field of lightweight metaheuristics, background concepts, and most important applications. We then discuss the implications of these algorithms and the main open questions and suggest future research directions.

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Section: Binary/discrete Compact Optimisation Algorithmsmentioning

confidence: 99%

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

Khalfi,

Caraffini,

Iacca

2023

International Journal of Intelligent Systems

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“…Furthermore, the simplicity and speed of altering programs related to the field programmable gate array (FPGA), besides decreasing implementation time, have solved a number of problems that are extra complex, computationally difficult, or very time consuming. Therefore, all these FPGA features along with the parallel asset of GAs make an attractive tool for GA hardware implementation, which can significantly reduce processing time and accelerate performance [16,[22][23][24]. In addition, the authors in [24] presented a GA simulation tool applicable in the context of an SDMA-OFDM system for determining an optimized solution.…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of Multi-User Detection Genetic Algorithm Tool for SDMA-OFDM Systems

Alansi

Elshafiey

Alsanie

et al. 2015

Wireless Pers Commun

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Robust multi-user detection (MUD) methods based on space division multiple access (SDMA) techniques are essential to efficiently exploit the electromagnetic spectrum. In this paper, an adaptive Genetic Algorithm-based tool for SDMA-OFDM Systems (GASOS) is developed to improve the performance and computational complexity in cases of fully-loaded and overloaded multi-user scenarios. The data flow in GASOS is appropriate in pipelining and parallelization to reduce operational time. A new GASOS-based MUD hardware design for SDMA-OFDM systems is proposed using FPGA architecture. The design details are presented together with their planned operational modules. Resource utilization is optimized, and the total number of clock cycles required is found to be 15 initially, in addition to one clock cycle per member of algorithm population. A clock frequency of 100 MHz is used and implementation is carried out on Xilinx Ò Virtex-6 FPGA, built in the development platform ML605 edition with JTAG Hardware Co-simulation. According to the results obtained from the developed algorithm and implementation tools, a high number of users can be physically possible and provided with support. Real-time based implementation of MUD systems has the potential to play a major role in next-generation communication systems.

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FPGA-Based Compact Differential Evolution for General-Purpose Optimization in Resource-Constrained Devices

Pan,

Song,

Chou

et al. 2024

IEEE Trans. Ind. Inf.

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A Novel Hardware Implementation of the Compact Genetic Algorithm

Cited by 3 publications

References 12 publications

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

FPGA Implementation of Multi-User Detection Genetic Algorithm Tool for SDMA-OFDM Systems

FPGA-Based Compact Differential Evolution for General-Purpose Optimization in Resource-Constrained Devices

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