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Towards the Novel Reasoning among Particles in PSO by the Use of RDF and SPARQL

机译:利用RDF和SPARQL探索PSO中粒子之间的新颖推理

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摘要

The significant development of the Internet has posed some new challenges and many new programming tools have been developed to address such challenges. Today, semantic web is a modern paradigm for representing and accessing knowledge data on the Internet. This paper tries to use the semantic tools such as resource definition framework (RDF) and RDF query language (SPARQL) for the optimization purpose. These tools are combined with particle swarm optimization (PSO) and the selection of the best solutions depends on its fitness. Instead of the local best solution, a neighborhood of solutions for each particle can be defined and used for the calculation of the new position, based on the key ideas from semantic web domain. The preliminary results by optimizing ten benchmark functions showed the promising results and thus this method should be investigated further.
机译:互联网的巨大发展带来了一些新的挑战,并且已经开发出许多新的编程工具来应对这些挑战。如今,语义网已成为代表和访问Internet上知识数据的现代范例。本文尝试使用诸如资源定义框架(RDF)和RDF查询语言(SPARQL)之类的语义工具进行优化。这些工具与粒子群优化(PSO)相结合,最佳解决方案的选择取决于其适用性。代替局部最佳解决方案,可以基于语义Web领域的关键思想,为每个粒子定义一个解决方案邻域,并将其用于新位置的计算。通过优化十个基准函数得出的初步结果显示了可喜的结果,因此,应进一步研究此方法。

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