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.

我们提出了一种受信息采食理论（IFT）和大象群优化（EHO）启发的新型信息获取方法。通过将 IFT 应用于社交媒体等信息访问问题，结合 EHO 算法，并引入新的操作符，我们成功地增强了对大规模信息访问的能力，并与其他信息采食方法进行了比较研究，结果非常有希望。