Optimization Algorithm and Application of Hybrid NSGA-II and DE
Due to the introduction of fast non-dominated sorting algorithms，crowding operators and elite strategy，the probability of repetition individual increased significantly in every population of NSGA-II algorithm， reducing the Pareto efficiency. It has been improved for this defect，removed the repeating...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2018-10-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1587 |
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| Summary: | Due to the introduction of fast non-dominated sorting algorithms，crowding operators and elite strategy，the probability of repetition individual increased significantly in every population of NSGA-II algorithm， reducing the Pareto efficiency. It has been improved for this defect，removed the repeating individual and maintained the number of populations unchanged. According to the genetic algorithm crossover and mutation method and differential evolution algorithm DE，the improved NSGA-II and DE are combined to construct a new multi objective optimization algorithm. The algorithm takes DE as the main optimization method，and uses the basic idea and crossover and mutation method of genetic algorithm. The optimization algorithm was verified by MATLAB. The results show that the optimized algorithm has been improved in both distribution and convergence，and the capacity of search solution has also been improved. At last ，the optimization algorithm is used to complete the hardware software partitioning of task management part in μC /OS-II. |
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| ISSN: | 1007-2683 |