Enhanced Elephant Herding Optimization for Large Scale Information Access on Social Media

TL;DR

This paper introduces an enhanced elephant herding optimization algorithm tailored for large-scale social media data, effectively improving information retrieval by integrating new operators and clustering techniques.

Contribution

The paper presents a novel large-scale adaptation of EHO with new operators and clustering, improving information access efficiency on social media datasets.

Findings

Enhanced EHO outperforms original EHO in large-scale scenarios.

The approach effectively finds relevant information in 1.4 million tweets.

Compared favorably against ant colony system and particle swarm optimization.

Abstract

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.