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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier Ltd
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40635/1/FPGA%20implementation%20of%20metaheuristic%20optimization%20algorithm.pdf |
| _version_ | 1796996281929826304 |
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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 | UMP |
| 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 | 2024-04-09T03:52:00Z |
| format | Article |
| id | UMPir40635 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-04-09T03:52:00Z |
| publishDate | 2023 |
| publisher | Elsevier Ltd |
| record_format | dspace |
| spelling | UMPir406352024-03-07T08:05:17Z http://umpir.ump.edu.my/id/eprint/40635/ FPGA implementation of metaheuristic optimization algorithm Nurul Hazlina, Noordin Phuah, Soon Eu Zuwairie, Ibrahim TK Electrical engineering. Electronics Nuclear engineering 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. Elsevier Ltd 2023-12 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/40635/1/FPGA%20implementation%20of%20metaheuristic%20optimization%20algorithm.pdf Nurul Hazlina, Noordin and Phuah, Soon Eu and Zuwairie, Ibrahim (2023) FPGA implementation of metaheuristic optimization algorithm. e-Prime - Advances in Electrical Engineering, Electronics and Energy, 6 (100377). pp. 1-14. ISSN 2772-6711. (Published) https://doi.org/10.1016/j.prime.2023.100377 https://doi.org/10.1016/j.prime.2023.100377 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Nurul Hazlina, Noordin Phuah, Soon Eu Zuwairie, Ibrahim FPGA implementation of metaheuristic optimization algorithm |
| 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 | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/40635/1/FPGA%20implementation%20of%20metaheuristic%20optimization%20algorithm.pdf |
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