Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning
Today, more and more complex tasks are emerging. To finish these tasks within a reasonable time, using the complex embedded system which has multiple processing units is necessary. Hardware/software partitioning is one of the key technologies in designing complex embedded systems, it is usually take...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/5/866 |
| _version_ | 1818995048699658240 |
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| author | Tao Zhang Changfu Yang Xin Zhao |
| author_facet | Tao Zhang Changfu Yang Xin Zhao |
| author_sort | Tao Zhang |
| collection | DOAJ |
| description | Today, more and more complex tasks are emerging. To finish these tasks within a reasonable time, using the complex embedded system which has multiple processing units is necessary. Hardware/software partitioning is one of the key technologies in designing complex embedded systems, it is usually taken as an optimization problem and be solved with different optimization methods. Among the optimization methods, swarm intelligent (SI) algorithms are easily applied and have the advantages of strong robustness and excellent global search ability. Due to the high complexity of hardware/software partitioning problems, the SI algorithms are ideal methods to solve the problems. In this paper, a new SI algorithm, called brainstorm optimization (BSO), is applied to hardware/software partitioning. In order to improve the performance of the BSO, we analyzed its optimization process when solving the hardware/software partitioning problem and found the disadvantages in terms of the clustering method and the updating strategy. Then we proposed the improved brainstorm optimization (IBSO) which ameliorated the original clustering method by setting the cluster points and improved the updating strategy by decreasing the number of updated individuals in each iteration. Based on the simulation methods which are usually used to evaluate the performance of the hardware/software partitioning algorithms, we generated eight benchmarks which represent tasks with different scales to test the performance of IBSO, BSO, four original heuristic algorithms and two improved BSO. Simulation results show that the IBSO algorithm can achieve the solutions with the highest quality within the shortest running time among these algorithms. |
| first_indexed | 2024-12-20T21:07:39Z |
| format | Article |
| id | doaj.art-2be85e39d4d748e394f5a9b06139ba5e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-20T21:07:39Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2be85e39d4d748e394f5a9b06139ba5e2022-12-21T19:26:35ZengMDPI AGApplied Sciences2076-34172019-02-019586610.3390/app9050866app9050866Using Improved Brainstorm Optimization Algorithm for Hardware/Software PartitioningTao Zhang0Changfu Yang1Xin Zhao2School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaToday, more and more complex tasks are emerging. To finish these tasks within a reasonable time, using the complex embedded system which has multiple processing units is necessary. Hardware/software partitioning is one of the key technologies in designing complex embedded systems, it is usually taken as an optimization problem and be solved with different optimization methods. Among the optimization methods, swarm intelligent (SI) algorithms are easily applied and have the advantages of strong robustness and excellent global search ability. Due to the high complexity of hardware/software partitioning problems, the SI algorithms are ideal methods to solve the problems. In this paper, a new SI algorithm, called brainstorm optimization (BSO), is applied to hardware/software partitioning. In order to improve the performance of the BSO, we analyzed its optimization process when solving the hardware/software partitioning problem and found the disadvantages in terms of the clustering method and the updating strategy. Then we proposed the improved brainstorm optimization (IBSO) which ameliorated the original clustering method by setting the cluster points and improved the updating strategy by decreasing the number of updated individuals in each iteration. Based on the simulation methods which are usually used to evaluate the performance of the hardware/software partitioning algorithms, we generated eight benchmarks which represent tasks with different scales to test the performance of IBSO, BSO, four original heuristic algorithms and two improved BSO. Simulation results show that the IBSO algorithm can achieve the solutions with the highest quality within the shortest running time among these algorithms.https://www.mdpi.com/2076-3417/9/5/866brainstorm optimizationhardware/software partitioningswarm intelligence |
| spellingShingle | Tao Zhang Changfu Yang Xin Zhao Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning Applied Sciences brainstorm optimization hardware/software partitioning swarm intelligence |
| title | Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning |
| title_full | Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning |
| title_fullStr | Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning |
| title_full_unstemmed | Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning |
| title_short | Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning |
| title_sort | using improved brainstorm optimization algorithm for hardware software partitioning |
| topic | brainstorm optimization hardware/software partitioning swarm intelligence |
| url | https://www.mdpi.com/2076-3417/9/5/866 |
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