Matches in SemOpenAlex for { <https://semopenalex.org/work/W2888502023> ?p ?o ?g. }
Showing items 1 to 72 of
72
with 100 items per page.
- W2888502023 abstract "Background: Patents suggest that efficient hybrid information scheduling algorithm is critical to achieve high performance for heterogeneous multi-core processors. Because the commonly used list scheduling algorithm obtains the approximate optimal solution, and the genetic algorithm is easy to converge to the local optimal solution and the convergence rate is slow. Methods: To solve the above two problems, the thesis proposes a hybrid algorithm integrating list scheduling and genetic algorithm. Firstly, in the task priority calculation phase of the list scheduling algorithm, the total cost of the current task node to the exit node and the differences of its execution cost on different processor cores are taken into account when constructing the task scheduling list, then the task insertion method is used in the task allocation phase, thus obtaining a better scheduling sequence. Secondly, the pre-acquired scheduling sequence is added to the initial population of the genetic algorithm, and then a dynamic selection strategy based on fitness value is adopted in the phase of evolution. Finally, the cross and mutation probability in the genetic algorithm is improved to avoid premature phenomenon. Results: With a series of simulation experiments, the proposed algorithm is proved to have a faster convergence rate and a higher optimal solution quality. Conclusion: The experimental results show that the ICLGA has the highest quality of the optimal solution than CPOP and GA, and the convergence rate of ICLGA is faster than that of GA." @default.
- W2888502023 created "2018-08-31" @default.
- W2888502023 creator A5000318376 @default.
- W2888502023 creator A5018710096 @default.
- W2888502023 creator A5018786162 @default.
- W2888502023 creator A5071684691 @default.
- W2888502023 date "2019-12-27" @default.
- W2888502023 modified "2023-09-27" @default.
- W2888502023 title "An Improved Genetic Algorithm on Hybrid Information Scheduling" @default.
- W2888502023 cites W1141949320 @default.
- W2888502023 cites W1271724408 @default.
- W2888502023 cites W1750422355 @default.
- W2888502023 cites W1966351223 @default.
- W2888502023 cites W1977251985 @default.
- W2888502023 cites W2050591253 @default.
- W2888502023 cites W2060240123 @default.
- W2888502023 cites W2076406848 @default.
- W2888502023 cites W2495349330 @default.
- W2888502023 doi "https://doi.org/10.2174/1872212112666180817130152" @default.
- W2888502023 hasPublicationYear "2019" @default.
- W2888502023 type Work @default.
- W2888502023 sameAs 2888502023 @default.
- W2888502023 citedByCount "1" @default.
- W2888502023 countsByYear W28885020232020 @default.
- W2888502023 crossrefType "journal-article" @default.
- W2888502023 hasAuthorship W2888502023A5000318376 @default.
- W2888502023 hasAuthorship W2888502023A5018710096 @default.
- W2888502023 hasAuthorship W2888502023A5018786162 @default.
- W2888502023 hasAuthorship W2888502023A5071684691 @default.
- W2888502023 hasConcept C107568181 @default.
- W2888502023 hasConcept C11413529 @default.
- W2888502023 hasConcept C119857082 @default.
- W2888502023 hasConcept C126255220 @default.
- W2888502023 hasConcept C127456818 @default.
- W2888502023 hasConcept C184497298 @default.
- W2888502023 hasConcept C206729178 @default.
- W2888502023 hasConcept C31258907 @default.
- W2888502023 hasConcept C31689143 @default.
- W2888502023 hasConcept C33923547 @default.
- W2888502023 hasConcept C41008148 @default.
- W2888502023 hasConcept C5119721 @default.
- W2888502023 hasConcept C8880873 @default.
- W2888502023 hasConceptScore W2888502023C107568181 @default.
- W2888502023 hasConceptScore W2888502023C11413529 @default.
- W2888502023 hasConceptScore W2888502023C119857082 @default.
- W2888502023 hasConceptScore W2888502023C126255220 @default.
- W2888502023 hasConceptScore W2888502023C127456818 @default.
- W2888502023 hasConceptScore W2888502023C184497298 @default.
- W2888502023 hasConceptScore W2888502023C206729178 @default.
- W2888502023 hasConceptScore W2888502023C31258907 @default.
- W2888502023 hasConceptScore W2888502023C31689143 @default.
- W2888502023 hasConceptScore W2888502023C33923547 @default.
- W2888502023 hasConceptScore W2888502023C41008148 @default.
- W2888502023 hasConceptScore W2888502023C5119721 @default.
- W2888502023 hasConceptScore W2888502023C8880873 @default.
- W2888502023 hasLocation W28885020231 @default.
- W2888502023 hasOpenAccess W2888502023 @default.
- W2888502023 hasPrimaryLocation W28885020231 @default.
- W2888502023 hasRelatedWork W10750703 @default.
- W2888502023 hasRelatedWork W14441464 @default.
- W2888502023 hasRelatedWork W14518443 @default.
- W2888502023 hasRelatedWork W14584334 @default.
- W2888502023 hasRelatedWork W2694335 @default.
- W2888502023 hasRelatedWork W6221533 @default.
- W2888502023 hasRelatedWork W6881284 @default.
- W2888502023 hasRelatedWork W7563926 @default.
- W2888502023 hasRelatedWork W8447824 @default.
- W2888502023 hasRelatedWork W9385438 @default.
- W2888502023 isParatext "false" @default.
- W2888502023 isRetracted "false" @default.
- W2888502023 magId "2888502023" @default.
- W2888502023 workType "article" @default.