一种基于粒子群优化技术的信息检索模型

S. Surya, P. Sumitra
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引用次数: 0

摘要

互联网拥有巨大的信息,而且发展迅速。大量的数据给有效的信息检索带来了挑战。信息检索系统(IRS)的范围是从大型数据集中为用户查询提供最相关的数据。然而,当前的IR系统无法提供隐藏的和最新的数据。本文主要研究软计算技术来克服上述问题。粒子群优化算法(PSO)用于计算适应度函数,以优化检索结果。粒子群算法具有高效的全局搜索能力,并且易于开发。本研究的实现结果是可行的,提高了隐藏数据检索的效果和准确性。
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An Innovative Information Retrieval Model Implementing Particle Swarm Optimization Technique
The Internet has enormous information and it is growing rapidly. The vast amount of data creates challenges in relation to effective Information Retrieval (IR). The scope of the Information Retrieval System (IRS) is to provide the most relevant data for user query from large datasets. However the current IR system fails to provide the hidden and up to date data. This paper focused on soft computing techniques to overcome the above mentioned issues. Particle Swarm Optimization (PSO) is used to compute the fitness function to optimize the retrieval result. PSO has an efficient capability in global search and the implementation is easy to develop. The implementation result of the present study is feasible, that improves the retrieval effect and the accuracy of hidden data retrieval.
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来源期刊
Journal of Computational and Theoretical Nanoscience
Journal of Computational and Theoretical Nanoscience 工程技术-材料科学:综合
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审稿时长
3.9 months
期刊介绍: Information not localized
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