Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator
Whale optimization algorithm (WOA), known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8810759 |
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| _version_ | 1832567739752906752 |
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| author | Qiangqiang Jiang Yuanjun Guo Zhile Yang Zheng Wang Dongsheng Yang Xianyu Zhou |
| author_facet | Qiangqiang Jiang Yuanjun Guo Zhile Yang Zheng Wang Dongsheng Yang Xianyu Zhou |
| author_sort | Qiangqiang Jiang |
| collection | DOAJ |
| description | Whale optimization algorithm (WOA), known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array (FPGA) is used as an accelerator, of which the high-level synthesis utilizes open computing language (OpenCL) as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization (PSO) and competitive swarm optimizer (CSO) are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems. |
| format | Article |
| id | doaj-art-89b57fa045ef4eacbd322d639c7eb84a |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-89b57fa045ef4eacbd322d639c7eb84a2025-02-03T01:00:41ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88107598810759Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA AcceleratorQiangqiang Jiang0Yuanjun Guo1Zhile Yang2Zheng Wang3Dongsheng Yang4Xianyu Zhou5Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, ChinaIntelligent Electrical Science and Technology Research Institute, Northeastern University, Shenyang 110819, ChinaIntelligent Electrical Science and Technology Research Institute, Northeastern University, Shenyang 110819, ChinaWhale optimization algorithm (WOA), known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array (FPGA) is used as an accelerator, of which the high-level synthesis utilizes open computing language (OpenCL) as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization (PSO) and competitive swarm optimizer (CSO) are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems.http://dx.doi.org/10.1155/2020/8810759 |
| spellingShingle | Qiangqiang Jiang Yuanjun Guo Zhile Yang Zheng Wang Dongsheng Yang Xianyu Zhou Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator Complexity |
| title | Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator |
| title_full | Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator |
| title_fullStr | Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator |
| title_full_unstemmed | Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator |
| title_short | Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator |
| title_sort | improving the performance of whale optimization algorithm through opencl based fpga accelerator |
| url | http://dx.doi.org/10.1155/2020/8810759 |
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