Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices

Medeiros, Denis R. da S.; Torquato, Matheus F.; Fernandes, Marcelo A. C.

doi:10.1002/eng2.12231

Cited by 5 publications

(14 citation statements)

References 39 publications

(68 reference statements)

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“…The Algorithm 1 represents the pseudocode of the GA described above, the same presented in [13], and it is inspired by [3]. The vector x j (k) represents the j-th individual of the N-sized population X(k), on the k-th generation.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

“…This whole process is presented in Figure 6. As stated before, this implementation is a modification of the work presented in [13] and then it uses the same base structure and has the same constraints and limitations described there. To conform with those limitations, the number of nodes, Q, must be a power of 2, that is…”

Section: Algorithmmentioning

confidence: 99%

“…where H sel-cross represents the number of bytes transferred during the selection and crossover, including commands and acknowledge messages to collect fitness values, and commands and acknowledge messages to collect and send individuals; and H col represents the number of bytes transferred during the collection of the best individuals, which includes the commands and acknowledge messages to collect one individual from each node. The total number of bytes transferred is the sum of Equations (12) and (13). Using this result, the equation to calculate the total overhead in seconds is…”

Section: Communication Between Microcontrollersmentioning

confidence: 99%

“…Finally, using the results presented in [13] for the processing time for all sections of the GA, it is possible to estimate the total execution time of the DGA. The processing time for the standalone GA can be simplified as…”

Section: Communication Between Microcontrollersmentioning

confidence: 99%

“…Since the distributed genetic algorithm implementation is built on top of the same implementation proposed in [13], therefore, if the devices are running at the same clock speed, the total time for the DGA is the same expression of Equation 16but with the population divided between Q nodes plus the t overhead , that is,…”

Section: Communication Between Microcontrollersmentioning

confidence: 99%

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Distributed Genetic Algorithms for Low-Power, Low-Cost and Small-Sized Memory Devices

Medeiros

Fernandes

2020

Electronics

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This work presents a strategy to implement a distributed form of genetic algorithm (GA) on low power, low cost, and small-sized memory aiming for increased performance and reduction of energy consumption when compared to standalone GAs. This strategy focuses on making a distributed version of GA feasible to run as a low cost and a low power consumption embedded system utilizing devices such as 8-bit microcontrollers (µCs) and Serial Peripheral Interface (SPI) for data transmission between those devices. Details about how the distributed GA was designed from a previous standalone implementation made by the authors and how the project is structured are presented. Furthermore, this work investigates the implementation limitations and shows results about its proper operation, most of them collected with the Hardware-In-Loop (HIL) technique, and resource consumption such as memory and processing time. Finally, some scenarios are analyzed to identify where this distributed version can be utilized and how it is compared to the single-node standalone implementation in terms of performance and energy consumption.

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Section: Genetic Algorithmsmentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

Section: Communication Between Microcontrollersmentioning

confidence: 99%

Section: Communication Between Microcontrollersmentioning

confidence: 99%

Section: Communication Between Microcontrollersmentioning

confidence: 99%

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Distributed Genetic Algorithms for Low-Power, Low-Cost and Small-Sized Memory Devices

Medeiros

Fernandes

2020

Electronics

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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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Analyzing the Application of Two Main Microcontrollers in Engineering Education – A Case Study of three IEEE Conferences Focusing on Education

Swart¹

2021

Adv. sci. technol. eng. syst. j.

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Microcontrollers have revolutionized the field of Engineering Education. Their popularity and cost-effectiveness have opened a large door of activity for innovative projects at both the undergraduate and graduate levels. The purpose of this article is to review this activity in terms of where two of these microcontrollers have been used in Engineering Education so as to recommend further possible applications. Focus is limited to papers presented at three IEEE-based conferences over the past 10 years that mentioned the Arduino or Raspberry Pi. Documentary analysis is thus used where the abstracts of the conference papers were reviewed. Results indicate that EDUCON dominated the field of microcontroller education from 2013 to 2016, while the last three years have seen more papers dedicated to this topic being presented at the FIE series of conferences. A total of nine papers relating to microcontroller education has also been presented at TALE between 2012 and 2019. The main application of these microcontrollers has been in the field of Robotics, with general electronics and design-based learning following suit. At least 11 conference papers focusing on the use of these microcontrollers at school level were found. Overall, the Arduino outranks the Raspberry Pi by almost 4:1, with the most cited papers relating to Robotics education, to helping students at home to complete science and technology experiments and to programming. Further applications can extend to energy monitoring and academic development workshops.

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Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices

Cited by 5 publications

References 39 publications

Distributed Genetic Algorithms for Low-Power, Low-Cost and Small-Sized Memory Devices

Distributed Genetic Algorithms for Low-Power, Low-Cost and Small-Sized Memory Devices

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

Analyzing the Application of Two Main Microcontrollers in Engineering Education – A Case Study of three IEEE Conferences Focusing on Education

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