FPGA Implementation of Metaheuristic Optimization Algorithm
Metaheuristic algorithms are gaining popularity amongst researchers due to their ability to solve nonlinear optimization problems as well as the ability to be adapted to solve a variety of problems. There is a surge of novel metaheuristics proposed recently, however it is uncertain whether they are...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671123002723 |
| _version_ | 1797388601295634432 |
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| author | Nurul Hazlina Noordin Phuah Soon Eu Zuwairie Ibrahim |
| author_facet | Nurul Hazlina Noordin Phuah Soon Eu Zuwairie Ibrahim |
| author_sort | Nurul Hazlina Noordin |
| collection | DOAJ |
| description | Metaheuristic algorithms are gaining popularity amongst researchers due to their ability to solve nonlinear optimization problems as well as the ability to be adapted to solve a variety of problems. There is a surge of novel metaheuristics proposed recently, however it is uncertain whether they are suitable for FPGA implementation. In addition, there exists a variety of design methodologies to implement metaheuristics upon FPGA which may improve the performance of the implementation. The project begins by researching and identifying metaheuristics which are suitable for FPGA implementation. The selected metaheuristic was the Simulated Kalman Filter (SKF) which proposed an algorithm that was low in complexity and used a small number of steps. Then the Discrete SKF was adapted from the original metaheuristic by rounding all floating-point values to integers as well as setting a fixed Kalman gain of 0.5. The Discrete SKF was then modeled using behavioral modeling to produce the Binary SKF which was then implemented onto FPGA. The design was made modular by producing separate modules that managed different parts of the metaheuristic and Parallel-In-Parallel-Out configuration of ports was also implemented. The Discrete SKF was then simulated on MATLAB meanwhile the Binary SKF was implemented onto FPGA and their performance were measured based on chip utilization, processing speed, and accuracy of results. The Binary SKF produced speed increment of up to 69 times faster than the Discrete SKF simulation. |
| first_indexed | 2024-03-08T22:43:07Z |
| format | Article |
| id | doaj.art-3bc44d2bff1748d5b14e8eeab1f5f76e |
| institution | Directory Open Access Journal |
| issn | 2772-6711 |
| language | English |
| last_indexed | 2024-03-08T22:43:07Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj.art-3bc44d2bff1748d5b14e8eeab1f5f76e2023-12-17T06:43:36ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112023-12-016100377FPGA Implementation of Metaheuristic Optimization AlgorithmNurul Hazlina Noordin0Phuah Soon Eu1Zuwairie Ibrahim2UMP STEM Lab, Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia; Corresponding author.UMP STEM Lab, Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, MalaysiaFaculty of Manufacturing & Mechatronic Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, MalaysiaMetaheuristic algorithms are gaining popularity amongst researchers due to their ability to solve nonlinear optimization problems as well as the ability to be adapted to solve a variety of problems. There is a surge of novel metaheuristics proposed recently, however it is uncertain whether they are suitable for FPGA implementation. In addition, there exists a variety of design methodologies to implement metaheuristics upon FPGA which may improve the performance of the implementation. The project begins by researching and identifying metaheuristics which are suitable for FPGA implementation. The selected metaheuristic was the Simulated Kalman Filter (SKF) which proposed an algorithm that was low in complexity and used a small number of steps. Then the Discrete SKF was adapted from the original metaheuristic by rounding all floating-point values to integers as well as setting a fixed Kalman gain of 0.5. The Discrete SKF was then modeled using behavioral modeling to produce the Binary SKF which was then implemented onto FPGA. The design was made modular by producing separate modules that managed different parts of the metaheuristic and Parallel-In-Parallel-Out configuration of ports was also implemented. The Discrete SKF was then simulated on MATLAB meanwhile the Binary SKF was implemented onto FPGA and their performance were measured based on chip utilization, processing speed, and accuracy of results. The Binary SKF produced speed increment of up to 69 times faster than the Discrete SKF simulation.http://www.sciencedirect.com/science/article/pii/S2772671123002723FPGA designBinary simulated Kalman filter |
| spellingShingle | Nurul Hazlina Noordin Phuah Soon Eu Zuwairie Ibrahim FPGA Implementation of Metaheuristic Optimization Algorithm e-Prime: Advances in Electrical Engineering, Electronics and Energy FPGA design Binary simulated Kalman filter |
| title | FPGA Implementation of Metaheuristic Optimization Algorithm |
| title_full | FPGA Implementation of Metaheuristic Optimization Algorithm |
| title_fullStr | FPGA Implementation of Metaheuristic Optimization Algorithm |
| title_full_unstemmed | FPGA Implementation of Metaheuristic Optimization Algorithm |
| title_short | FPGA Implementation of Metaheuristic Optimization Algorithm |
| title_sort | fpga implementation of metaheuristic optimization algorithm |
| topic | FPGA design Binary simulated Kalman filter |
| url | http://www.sciencedirect.com/science/article/pii/S2772671123002723 |
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