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使用带有交叉算子算法的动态自适应布谷鸟搜索算法反演折射率参数

Inversion for Refractivity Parameters Using a Dynamic Adaptive Cuckoo Search with Crossover Operator Algorithm.

作者信息

Zhang Zhihua, Sheng Zheng, Shi Hanqing, Fan Zhiqiang

机构信息

Department of Space and Remote Sensing, College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China.

出版信息

Comput Intell Neurosci. 2016;2016:3208724. doi: 10.1155/2016/3208724. Epub 2016 Apr 26.

DOI:10.1155/2016/3208724
PMID:27212938
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4861794/
Abstract

Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter.

摘要

使用RFC技术估计折射率参数是一个复杂的非线性优化问题。本文提出了一种改进的布谷鸟搜索（CS）算法来处理这个问题。为了提高CS算法的性能，将参数动态自适应操作和交叉操作集成到标准CS算法中（DACS-CO）。采用Rechenberg的1/5准则结合学习因子来控制参数动态自适应调整过程。利用遗传算法的交叉操作来保证种群多样性。新的混合算法具有更好的局部搜索能力，性能更优。为了验证DACS-CO算法估计大气折射率参数的能力，对模拟数据和实际雷达杂波数据都进行了实验。数值实验表明，DACS-CO算法能够为从雷达杂波中近实时估计大气折射率剖面提供一种有效的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/7fb18dbabbc0/CIN2016-3208724.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/5417d752e95d/CIN2016-3208724.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/c0dc91f5a531/CIN2016-3208724.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/3481bd90fde2/CIN2016-3208724.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/f5da49ab1b0d/CIN2016-3208724.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/9c64e51bf565/CIN2016-3208724.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/2709a3fb0bd9/CIN2016-3208724.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/665919deab4e/CIN2016-3208724.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/926b54c7b673/CIN2016-3208724.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/7fb18dbabbc0/CIN2016-3208724.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/5417d752e95d/CIN2016-3208724.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/c0dc91f5a531/CIN2016-3208724.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/3481bd90fde2/CIN2016-3208724.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/f5da49ab1b0d/CIN2016-3208724.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/9c64e51bf565/CIN2016-3208724.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/2709a3fb0bd9/CIN2016-3208724.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/665919deab4e/CIN2016-3208724.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/926b54c7b673/CIN2016-3208724.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0d1/4861794/7fb18dbabbc0/CIN2016-3208724.009.jpg

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本文引用的文献

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Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm.基于混合自适应布谷鸟搜索与模拟退火算法的混沌系统参数估计
Chaos. 2014 Mar;24(1):013133. doi: 10.1063/1.4867989.
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Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search.果蝇自由飞行中的气味追踪与最优间歇无标度搜索一致。
PLoS One. 2007 Apr 4;2(4):e354. doi: 10.1371/journal.pone.0000354.
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Optimizing the success of random searches.优化随机搜索的成功率。
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