Multi-areas Outstanding Covering Optimization Method of HF Network Based on Preference Ranking Elimination NSGAII Algorithm

LI Xinchao; HE Qianhua; LI Yanxiong; ZHU Zhengyu

doi:10.11999/JEIT161172

Volume 39 Issue 8

Aug. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(8): 1779-1787

LI Xinchao, HE Qianhua, LI Yanxiong, ZHU Zhengyu. Multi-areas Outstanding Covering Optimization Method of HF Network Based on Preference Ranking Elimination NSGAII Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1779-1787. doi: 10.11999/JEIT161172

Citation:

LI Xinchao, HE Qianhua, LI Yanxiong, ZHU Zhengyu. Multi-areas Outstanding Covering Optimization Method of HF Network Based on Preference Ranking Elimination NSGAII Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1779-1787. doi: 10.11999/JEIT161172

Citation:

LI Xinchao, HE Qianhua, LI Yanxiong, ZHU Zhengyu. Multi-areas Outstanding Covering Optimization Method of HF Network Based on Preference Ranking Elimination NSGAII Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1779-1787. doi: 10.11999/JEIT161172

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Multi-areas Outstanding Covering Optimization Method of HF Network Based on Preference Ranking Elimination NSGAII Algorithm

doi: 10.11999/JEIT161172

Funds:

The National Natural Science Foundation of China (61571192), The Public Welfare Research Project of Guangdong Province (2015A010103003)

Received Date: 2016-11-02
Rev Recd Date: 2017-03-01
Publish Date: 2017-08-19

Abstract

Abstract

This paper proposes a preference ranking elimination NSGAII algorithm to deal with the time-consuming issue of the preference NSGAII algorithm in optimizing HF network frequency assignment in multi-areas outstanding coverage. The proposed algorithm sorts and eliminates solutions according to their preference evaluation priori to the non-dominate sorting. By eliminating solutions with low ranking, the number of solutions participates in non-dominate sorting is reduced. The calculation time and the probability of selecting low ranking individuals for crossover or mutation are both decreased. The proposed algorithm simultaneously achieves the best performance and least calculation time in 38 of 48 sets experiments. Constrained with the same iteration number, the proposed algorithm saves 27% of computation time against the preference NSGAII algorithm. Experimental results show that by adopting preference evaluation sorting, the proposed algorithm takes less time and obtains a better solution.
- High Frequency (HF) network,
- Frequency assignment,
- Multi-objective optimization,
- Non-dominate sorting,
- Preference ranking elimination

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References(26)

References

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