An Improved Differential Evolution Algorithm Based on Statistical Log-linear Model
Differential evolution (DE) algorithm is a good optimization technique based on population which has been successfully applied in many research and application areas. Log-linear model is a statistical model which can easily blend multiple features, a variety of knowledge sources can be added to the...
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IFSA Publishing, S.L.
2013-11-01
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doaj-bcc7c9ee1364404fa02d686919267b0b2020-11-25T02:11:19ZengIFSA Publishing, S.L.Sensors & Transducers2306-85151726-54792013-11-0115911277281An Improved Differential Evolution Algorithm Based on Statistical Log-linear ModelZhehuang Huang0School of Mathematics Sciences, Huaqiao University, Quanzhou, 362021, China Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen, 361005Differential evolution (DE) algorithm is a good optimization technique based on population which has been successfully applied in many research and application areas. Log-linear model is a statistical model which can easily blend multiple features, a variety of knowledge sources can be added to the model in the form of feature functions. Traditional differential evolution algorithm is easy to fall into local optimum value and the convergence rate is slow. To solve these problems, an improved differential evolution algorithm based on log-linear model is proposed and implemented in this paper. There are two mainly works in this paper. Firstly, we introduce log-linear model to differential evolution algorithm which can enhance decision making ability. Secondly, some operations are presented to improve global optimization capability. Experiments showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.http://www.sensorsportal.com/HTML/DIGEST/november_2013/PDF_vol_159/P_1572.pdfDifferential evolutionLog-linear modelStatistical modelFunction optimization. |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhehuang Huang |
spellingShingle |
Zhehuang Huang An Improved Differential Evolution Algorithm Based on Statistical Log-linear Model Sensors & Transducers Differential evolution Log-linear model Statistical model Function optimization. |
author_facet |
Zhehuang Huang |
author_sort |
Zhehuang Huang |
title |
An Improved Differential Evolution Algorithm Based on Statistical Log-linear Model |
title_short |
An Improved Differential Evolution Algorithm Based on Statistical Log-linear Model |
title_full |
An Improved Differential Evolution Algorithm Based on Statistical Log-linear Model |
title_fullStr |
An Improved Differential Evolution Algorithm Based on Statistical Log-linear Model |
title_full_unstemmed |
An Improved Differential Evolution Algorithm Based on Statistical Log-linear Model |
title_sort |
improved differential evolution algorithm based on statistical log-linear model |
publisher |
IFSA Publishing, S.L. |
series |
Sensors & Transducers |
issn |
2306-8515 1726-5479 |
publishDate |
2013-11-01 |
description |
Differential evolution (DE) algorithm is a good optimization technique based on population which has been successfully applied in many research and application areas. Log-linear model is a statistical model which can easily blend multiple features, a variety of knowledge sources can be added to the model in the form of feature functions. Traditional differential evolution algorithm is easy to fall into local optimum value and the convergence rate is slow. To solve these problems, an improved differential evolution algorithm based on log-linear model is proposed and implemented in this paper. There are two mainly works in this paper. Firstly, we introduce log-linear model to differential evolution algorithm which can enhance decision making ability. Secondly, some operations are presented to improve global optimization capability. Experiments showed that the improved algorithm has more powerful global exploration ability and faster convergence speed. |
topic |
Differential evolution Log-linear model Statistical model Function optimization. |
url |
http://www.sensorsportal.com/HTML/DIGEST/november_2013/PDF_vol_159/P_1572.pdf |
work_keys_str_mv |
AT zhehuanghuang animproveddifferentialevolutionalgorithmbasedonstatisticalloglinearmodel AT zhehuanghuang improveddifferentialevolutionalgorithmbasedonstatisticalloglinearmodel |
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1724915054091960320 |