In this article, we present a novel information access approach inspired by the information foraging theory (IFT) and elephant herding optimization (EHO). First, we propose a model for information access on social media based on the IFT. We then elaborate an adaptation of the original EHO algorithm to apply it to the information access problem. The combination of the IFT and EHO constitutes a good opportunity to find relevant information on social media. However, when dealing with voluminous data, the performance undergoes a sharp drop. To overcome this issue, we developed an enhanced version of EHO for large scale information access. We introduce new operators to the algorithm, including territories delimitation and clan migration using clustering. To validate our work, we created a dataset of more than 1.4 million tweets, on which we carried out extensive experiments. The outcomes reveal the ability of our approach to find relevant information in an effective and efficient way. They also highlight the advantages of the improved version of EHO over the original algorithm regarding different aspects. Furthermore, we undertook a comparative study with two other metaheuristic-based information foraging approaches, namely ant colony system and particle swarm optimization. Overall, the results are very promising.

翻译：本文提出了一种受信息觅食理论和象群优化算法启发的新型信息访问方法。首先，我们基于信息觅食理论构建了社交媒体信息访问模型。随后，我们对原始EHO算法进行改进适配，使其适用于信息访问问题。IFT与EHO的结合为社交媒体相关信息的发现提供了良好途径。然而，当处理海量数据时，算法性能会出现急剧下降。为解决此问题，我们开发了面向大规模信息访问的增强型EHO算法。我们在算法中引入了新的操作机制，包括基于聚类的领域划分和族群迁移。为验证工作有效性，我们构建了包含超过140万条推文的数据集，并进行了大量实验。实验结果表明，我们的方法能够以高效且有效的方式发现相关信息，同时凸显了改进版EHO算法相较于原始版本在多方面的优势。此外，我们与另外两种基于元启发式的信息觅食方法（蚁群系统和粒子群优化）进行了对比研究。总体而言，实验结果展现出良好的应用前景。