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Information Literacy Assessment with a Modified Hybrid Differential Evolution with Model-Based Reinitialization

Information literacy assessment is extremely important for the evaluation of the information literacy skills of college students. Intelligent optimization technique is an effective strategy to optimize the weight parameters of the information literacy assessment index system (ILAIS). In this paper,...

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Detalles Bibliográficos
Autores principales: Wang, Yuan, Li, Hui, Ding, Zhenguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6217889/
https://www.ncbi.nlm.nih.gov/pubmed/30425734
http://dx.doi.org/10.1155/2018/9745639
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author Wang, Yuan
Li, Hui
Ding, Zhenguo
author_facet Wang, Yuan
Li, Hui
Ding, Zhenguo
author_sort Wang, Yuan
collection PubMed
description Information literacy assessment is extremely important for the evaluation of the information literacy skills of college students. Intelligent optimization technique is an effective strategy to optimize the weight parameters of the information literacy assessment index system (ILAIS). In this paper, a new version of differential evolution algorithm (DE), named hybrid differential evolution with model-based reinitialization (HDEMR), is proposed to accurately fit the weight parameters of ILAIS. The main contributions of this paper are as follows: firstly, an improved contraction criterion which is based on the population entropy in objective space and the maximum distance in decision space is employed to decide when the local search starts. Secondly, a modified model-based population reinitialization strategy is designed to enhance the global search ability of HDEMR to handle complex problems. Two types of experiments are designed to assess the performance of HDEMR. In the first type of experiments, HDEMR is tested and compared with seven well-known DE variants on CEC2005 and CEC2014 benchmark functions. In the second type of experiments, HDEMR is compared with the well-known and widely used deterministic algorithm DIRECT on GKLS test classes. The experimental results demonstrate the effectiveness of HDEMR for global numerical optimization and show better performance. Furthermore, HDEMR is applied to optimize the weight parameters of ILAIS at China University of Geosciences (CUG), and satisfactory results are obtained.
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spelling pubmed-62178892018-11-13 Information Literacy Assessment with a Modified Hybrid Differential Evolution with Model-Based Reinitialization Wang, Yuan Li, Hui Ding, Zhenguo Comput Intell Neurosci Research Article Information literacy assessment is extremely important for the evaluation of the information literacy skills of college students. Intelligent optimization technique is an effective strategy to optimize the weight parameters of the information literacy assessment index system (ILAIS). In this paper, a new version of differential evolution algorithm (DE), named hybrid differential evolution with model-based reinitialization (HDEMR), is proposed to accurately fit the weight parameters of ILAIS. The main contributions of this paper are as follows: firstly, an improved contraction criterion which is based on the population entropy in objective space and the maximum distance in decision space is employed to decide when the local search starts. Secondly, a modified model-based population reinitialization strategy is designed to enhance the global search ability of HDEMR to handle complex problems. Two types of experiments are designed to assess the performance of HDEMR. In the first type of experiments, HDEMR is tested and compared with seven well-known DE variants on CEC2005 and CEC2014 benchmark functions. In the second type of experiments, HDEMR is compared with the well-known and widely used deterministic algorithm DIRECT on GKLS test classes. The experimental results demonstrate the effectiveness of HDEMR for global numerical optimization and show better performance. Furthermore, HDEMR is applied to optimize the weight parameters of ILAIS at China University of Geosciences (CUG), and satisfactory results are obtained. Hindawi 2018-10-22 /pmc/articles/PMC6217889/ /pubmed/30425734 http://dx.doi.org/10.1155/2018/9745639 Text en Copyright © 2018 Yuan Wang et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Yuan
Li, Hui
Ding, Zhenguo
Information Literacy Assessment with a Modified Hybrid Differential Evolution with Model-Based Reinitialization
title Information Literacy Assessment with a Modified Hybrid Differential Evolution with Model-Based Reinitialization
title_full Information Literacy Assessment with a Modified Hybrid Differential Evolution with Model-Based Reinitialization
title_fullStr Information Literacy Assessment with a Modified Hybrid Differential Evolution with Model-Based Reinitialization
title_full_unstemmed Information Literacy Assessment with a Modified Hybrid Differential Evolution with Model-Based Reinitialization
title_short Information Literacy Assessment with a Modified Hybrid Differential Evolution with Model-Based Reinitialization
title_sort information literacy assessment with a modified hybrid differential evolution with model-based reinitialization
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6217889/
https://www.ncbi.nlm.nih.gov/pubmed/30425734
http://dx.doi.org/10.1155/2018/9745639
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