  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.
