A Greener Meta-Heuristics Scheduling Algorithm With Energized Optimization Dynamics by Deeper Intelligence Fusion
As is known to all, the heterogeneous green scheduling objects have the intelligent feedback related to the efficiencies corresponding to the schemes, which has been largely ignored in most existing studies. That is why the existing optimization dynamics in green meta-heuristics scheduling algorithm...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9314121/ |
| _version_ | 1831526726948093952 |
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| author | Jinglian Wang Bin Gong Hong Liu Shaohui Li |
| author_facet | Jinglian Wang Bin Gong Hong Liu Shaohui Li |
| author_sort | Jinglian Wang |
| collection | DOAJ |
| description | As is known to all, the heterogeneous green scheduling objects have the intelligent feedback related to the efficiencies corresponding to the schemes, which has been largely ignored in most existing studies. That is why the existing optimization dynamics in green meta-heuristics scheduling algorithms, generally appear underpowered and vulnerable in the face of the rapid extension from homogeneity to heterogeneity of scheduling objects. Then, with respecting and ingeniously leveraging hardware (i.e., heterogeneous scheduling objects) intelligence, an efficient meta-heuristics algorithm with re-energized majorization dynamics for heterogeneous greener scheduling (i.e., CA<sup>r</sup>_FI(HS)), is proposed. The experimental results show that compared with the other meta-heuristics scheduling algorithms, CA<sup>r</sup>_FI(HS) has obvious advantages in the overall performance and the solution quality, for both data intensive and computing intensive instances. |
| first_indexed | 2024-12-16T16:15:53Z |
| format | Article |
| id | doaj.art-9b73d3f1fd234098b6a4b461178835dc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T16:15:53Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9b73d3f1fd234098b6a4b461178835dc2022-12-21T22:25:06ZengIEEEIEEE Access2169-35362021-01-019183731838210.1109/ACCESS.2021.30492379314121A Greener Meta-Heuristics Scheduling Algorithm With Energized Optimization Dynamics by Deeper Intelligence FusionJinglian Wang0https://orcid.org/0000-0001-5120-6883Bin Gong1Hong Liu2Shaohui Li3https://orcid.org/0000-0003-2932-9793Department of Information and Electrical Engineering, Ludong University, Yantai, ChinaDepartment of Software, Shandong University, Jinan, ChinaDepartment of Information Science and Engineering, Shandong Normal University, Jinan, ChinaDepartment of Information Science and Engineering, Shandong Normal University, Jinan, ChinaAs is known to all, the heterogeneous green scheduling objects have the intelligent feedback related to the efficiencies corresponding to the schemes, which has been largely ignored in most existing studies. That is why the existing optimization dynamics in green meta-heuristics scheduling algorithms, generally appear underpowered and vulnerable in the face of the rapid extension from homogeneity to heterogeneity of scheduling objects. Then, with respecting and ingeniously leveraging hardware (i.e., heterogeneous scheduling objects) intelligence, an efficient meta-heuristics algorithm with re-energized majorization dynamics for heterogeneous greener scheduling (i.e., CA<sup>r</sup>_FI(HS)), is proposed. The experimental results show that compared with the other meta-heuristics scheduling algorithms, CA<sup>r</sup>_FI(HS) has obvious advantages in the overall performance and the solution quality, for both data intensive and computing intensive instances.https://ieeexplore.ieee.org/document/9314121/Greenerenergized optimization dynamicsfusion intelligencemeta-heuristics algorithmnonlinear heterogeneous scheduling |
| spellingShingle | Jinglian Wang Bin Gong Hong Liu Shaohui Li A Greener Meta-Heuristics Scheduling Algorithm With Energized Optimization Dynamics by Deeper Intelligence Fusion IEEE Access Greener energized optimization dynamics fusion intelligence meta-heuristics algorithm nonlinear heterogeneous scheduling |
| title | A Greener Meta-Heuristics Scheduling Algorithm With Energized Optimization Dynamics by Deeper Intelligence Fusion |
| title_full | A Greener Meta-Heuristics Scheduling Algorithm With Energized Optimization Dynamics by Deeper Intelligence Fusion |
| title_fullStr | A Greener Meta-Heuristics Scheduling Algorithm With Energized Optimization Dynamics by Deeper Intelligence Fusion |
| title_full_unstemmed | A Greener Meta-Heuristics Scheduling Algorithm With Energized Optimization Dynamics by Deeper Intelligence Fusion |
| title_short | A Greener Meta-Heuristics Scheduling Algorithm With Energized Optimization Dynamics by Deeper Intelligence Fusion |
| title_sort | greener meta heuristics scheduling algorithm with energized optimization dynamics by deeper intelligence fusion |
| topic | Greener energized optimization dynamics fusion intelligence meta-heuristics algorithm nonlinear heterogeneous scheduling |
| url | https://ieeexplore.ieee.org/document/9314121/ |
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