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

Yassine Drias, Habiba Drias, Ilyes Khennak·June 16, 2024

Summary

The paper presents Enhanced Elephant Herding Optimization (EHO) and its enhanced version (EEHOLSIF) for large-scale information access on social media, drawing from the information foraging theory. The authors propose a model that combines IFT with EHO, using social graphs to represent users, posts, and interactions. They improve the algorithm by introducing new operators like territories delimitation, clustering-based clan migration, and k-means clustering. Experiments with a large dataset of 1.4 million tweets demonstrate EEHOLSIF's effectiveness, outperforming other metaheuristics like ant colony system and particle swarm optimization in relevance and response time. The study highlights the importance of tailored techniques for navigating the vast social media data and suggests potential future directions for optimization and integration with deep learning.

Key findings

17

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the problem of large-scale information access on social media by proposing a novel approach that combines the information foraging theory (IFT) with an enhanced version of the Elephant Herding Optimization (EHO) algorithm . This approach is designed to help users efficiently navigate and access information on social media platforms, which have become crucial sources of information for many individuals .

The problem of large-scale information access on social media is not new, as the rapid growth of social media platforms has led to an exponential increase in the volume of online public data . Users rely on social media not only for entertainment but also for seeking information on various topics, including serious issues like health information and current events .

The paper introduces a detailed formal model for information foraging on social media and presents an enhanced version of the EHO algorithm with new operators tailored for large-scale information access . These new contributions aim to improve the efficiency and effectiveness of information retrieval on social media platforms, making it easier for users to find relevant information amidst the vast amount of data available .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate a scientific hypothesis related to large-scale information access on social media by proposing a novel bio-inspired approach that combines the information foraging theory with an enhanced elephant herding optimization . The main contributions of this work include:

  • Introducing a detailed formal model for information foraging on social media.
  • Developing a new enhanced version of elephant herding optimization with new operators tailored for large-scale information access.
  • Utilizing k-means clustering to implement new operators in the enhanced elephant herding optimization.
  • Conducting a performance evaluation on a dataset of over 1.4 million tweets.
  • Performing a comparative study with other metaheuristic-based information access approaches .

What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Enhanced Elephant Herding Optimization for Large Scale Information Access on Social Media" proposes several new ideas, methods, and models in the field of information foraging and optimization . Here are the key contributions outlined in the paper:

  1. Information Foraging Model for Social Media: The paper introduces a detailed formal model for information foraging on social media based on the Information Foraging Theory (IFT) . This model focuses on how people seek and access information on social media platforms, drawing parallels between animal food foraging and information foraging .

  2. Enhanced Elephant Herding Optimization (EEHO): The paper presents an enhanced version of the Elephant Herding Optimization (EHO) algorithm tailored for large-scale information foraging on social media . The EEHO algorithm incorporates new operators, such as territories delimitation and clan migration using clustering, to improve the efficiency and effectiveness of information access .

  3. Territories Concept and Migration Mechanism: The EEHO algorithm introduces the concept of territories and a migration mechanism to enhance information foraging on social media . By defining territories and incorporating a migration mechanism, the algorithm can better target relevant information aligned with users' interests, leading to improved relevance scores and faster convergence rates .

  4. Performance Evaluation: The paper conducts extensive experiments on a dataset of over 1.4 million tweets to validate the proposed EEHO approach . The outcomes of the experiments demonstrate the effectiveness and efficiency of the EEHO algorithm in finding relevant information on social media platforms .

  5. Comparative Study: A comparative study is conducted to evaluate the EEHO algorithm against other metaheuristic-based information foraging approaches, such as ant colony system and particle swarm optimization . The study highlights the advantages of the EEHO algorithm in terms of relevance scores, surfing depth, convergence rates, and response times compared to existing approaches .

Overall, the paper introduces a novel approach that combines the Information Foraging Theory with an enhanced Elephant Herding Optimization algorithm to address large-scale information access on social media, offering significant advancements in the field of information retrieval and optimization . The "Enhanced Elephant Herding Optimization for Large Scale Information Access on Social Media" paper introduces several key characteristics and advantages compared to previous methods, as detailed in the document:

  1. Detailed Formal Model for Information Foraging: The paper presents a detailed formal model for information foraging on social media, which is a significant contribution to the field. This model is based on the Information Foraging Theory and focuses on how individuals seek and access information on social media platforms .

  2. Enhanced Elephant Herding Optimization (EEHO): The paper introduces an enhanced version of the Elephant Herding Optimization (EHO) algorithm specifically tailored for large-scale information access on social media. The EEHO algorithm incorporates new operators and mechanisms, such as territories delimitation and clan migration using clustering, to improve efficiency and effectiveness in information retrieval .

  3. Incorporation of Territories Concept and Migration Mechanism: One of the key advantages of the EEHO algorithm is the incorporation of the territories concept and a migration mechanism. By defining territories and implementing a migration mechanism, the algorithm can better target relevant information aligned with users' interests, leading to improved relevance scores, faster convergence rates, and more efficient information access on social media platforms .

  4. Performance Evaluation and Comparative Study: The paper conducts extensive experiments on a dataset of over 1.4 million tweets to evaluate the performance of the EEHO algorithm. The results demonstrate the effectiveness and efficiency of the EEHO approach in locating relevant information on social media platforms. Additionally, a comparative study is conducted to compare the EEHO algorithm with other metaheuristic-based information foraging approaches, highlighting its advantages in terms of relevance scores, surfing depth, convergence rates, and response times .

  5. Novelty and Contributions: The main contributions of the paper include the introduction of a new enhanced version of the EHO algorithm, the utilization of k-means clustering to implement new operators, a detailed formal model for information foraging on social media, and a comprehensive performance evaluation on a large dataset of tweets. These contributions collectively enhance the efficiency and effectiveness of large-scale information access on social media platforms .

Overall, the EEHO algorithm stands out for its innovative approach, incorporation of territories and migration mechanisms, performance improvements, and the ability to address the challenges of large-scale information foraging on social media platforms, making it a valuable advancement in the field of information retrieval and optimization .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of information foraging and elephant herding optimization. Noteworthy researchers in this field include Yassine Drias, S. Kechid, G. Pasi, V. Nguyen, G. Rabby, V. Sv´atek, O. Corcho, A. Dalton, B. Dorr, L. Liang, K. Hollingshead, A. Jaiswal, H. Liu, I. Frommholz, H. Estiri, B. Abounia Omran, S. Murphy, and Ilyes Khennak .

The key solution mentioned in the paper is the development of an Enhanced Elephant Herding Optimization (EEHO) approach for large-scale information access on social media. This approach combines the information foraging theory with an enhanced version of elephant herding optimization, introducing new concepts such as territories delimitation, clan migration, and k-means clustering to improve the algorithm's performance in handling large-scale information foraging . The EEHO approach aims to provide better relevance scores, faster response times, improved convergence, and increased surfing depth compared to traditional information foraging methods .


How were the experiments in the paper designed?

The experiments in the paper were designed to propose a novel bio-inspired approach to large scale information foraging on social media using enhanced elephant herding optimization and clustering. The experiments involved adapting the Elephant Herding Optimization (EHO) algorithm to address information foraging on social media, which required modifications to key aspects such as the elephants' positions implementation, solution construction, and solution evaluation . The study aimed to enhance the EHO algorithm to work on combinatorial problems, specifically information foraging, by introducing new operators adapted to large scale information access and incorporating k-means clustering to implement these new operators . The experiments included a performance evaluation on a dataset of more than 1.4 million tweets and a comparative study with other metaheuristic-based information access approaches . The experiments focused on maximizing the system's performance by setting empirical parameters, such as the number of territories (clusters), through testing different values and computing the total Within Cluster Sums of Squares (WSS) to measure the average distance between posts and their centroids for each cluster .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided contexts. However, the study focuses on large-scale information foraging on social media using the Enhanced Elephant Herding Optimization algorithm . The code used in the study is not specified to be open source or publicly available in the provided contexts. For more specific details regarding the dataset used for quantitative evaluation and the availability of the code, it would be advisable to refer directly to the study or contact the authors for clarification.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted a performance evaluation on a dataset of over 1.4 million tweets, which demonstrates a robust empirical analysis . The research introduced a novel bio-inspired approach for large-scale information access on social media, combining the information foraging theory with an enhanced elephant herding optimization . This innovative approach included a detailed formal model for information foraging on social media, new operators adapted for large-scale information access, and the incorporation of k-means clustering to enhance the optimization process . Additionally, the study included a comparative analysis with other metaheuristic-based information access approaches, further strengthening the validity of the findings .


What are the contributions of this paper?

The contributions of the paper "Enhanced Elephant Herding Optimization for Large Scale Information Access on Social Media" include:

  • Detailed formal model for information foraging on social media.
  • Introduction of a new enhanced version of elephant herding optimization tailored for large scale information access.
  • Utilization of k-means clustering to implement new operators in the enhanced elephant herding optimization.
  • Performance evaluation conducted on a dataset comprising over 1.4 million tweets.
  • Comparative study with other metaheuristic-based information access approaches .

What work can be continued in depth?

To delve deeper into the research on large-scale information access on social media, a potential avenue for further exploration could be the continuation of the study on the migration mechanism introduced in the Enhanced Elephant Herding Optimization (EEHO) algorithm . This migration parameter controls the maximum number of generations a clan can spend without improving its best solution and influences the movement towards new territories to find better solutions . Investigating the impact of different migration thresholds and strategies on the algorithm's performance could provide valuable insights into optimizing information foraging on social media platforms.

Furthermore, exploring the effectiveness of the territories concept in the EEHO algorithm could be a promising direction for future research. By dividing the search space into multiple regions based on problem-related features, the algorithm aims to explore these areas more efficiently, potentially enhancing its performance, especially in dealing with large-scale problems . Analyzing the impact of varying the number and characteristics of these territories on the algorithm's efficiency and effectiveness in information retrieval tasks could offer valuable contributions to the field.

Moreover, conducting a comparative study with other metaheuristic-based information foraging approaches from the literature could provide valuable insights into the strengths and weaknesses of different optimization techniques. By comparing the EEHO algorithm with existing approaches, researchers can evaluate its performance metrics such as relevance score, surfing depth, convergence rate, and response time . This comparative analysis can help validate the effectiveness of the EEHO algorithm and identify areas for further improvement or optimization in large-scale information access on social media platforms.

Tables

2

Introduction
Background
Information Foraging Theory (IFT) in social media context
Challenges of large-scale data in social media platforms
Objective
To develop EHO and EEHOLSIF algorithms
Improve information access and retrieval efficiency
Outperform existing metaheuristics
Enhanced Elephant Herding Optimization (EHO)
Model Description
Social graph representation of users, posts, and interactions
Integration of IFT principles
Operators
1. Territories Delimitation
Division of social space for efficient search
2. Clustering-Based Clan Migration
Organizing herds into clusters for collective exploration
3. K-means Clustering
Dynamic grouping for targeted information gathering
EEHOLSIF: Enhanced EHO with Social Influence Factor
Integration of social influence in the optimization process
Improvement over EHO
Experimental Evaluation
Dataset
1.4 million tweets dataset for benchmarking
Performance Metrics
Relevance (accuracy or precision)
Response time
Comparison with Metaheuristics
Ant Colony System (ACS)
Particle Swarm Optimization (PSO)
Outperformance results
Results and Discussion
EEHOLSIF's effectiveness in navigating social media data
Advantages over traditional methods
Real-world implications
Future Directions
Optimization Techniques
Integration with deep learning
Adaptive strategies for dynamic social media environments
Applications
Sentiment analysis
Trend prediction
Recommendation systems
Conclusion
The significance of tailored algorithms for social media data
The potential of EHO and EEHOLSIF for large-scale information access
Limitations and suggestions for further research
Basic info
papers
neural and evolutionary computing
artificial intelligence
Advanced features
Insights
Which optimization algorithms does the paper compare EEHOLSIF with?
How does the model utilize information foraging theory in social media information access?
What is the primary focus of the paper discussed?
What improvement techniques are introduced in the Enhanced Elephant Herding Optimization (EHO) model?

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

Yassine Drias, Habiba Drias, Ilyes Khennak·June 16, 2024

Summary

The paper presents Enhanced Elephant Herding Optimization (EHO) and its enhanced version (EEHOLSIF) for large-scale information access on social media, drawing from the information foraging theory. The authors propose a model that combines IFT with EHO, using social graphs to represent users, posts, and interactions. They improve the algorithm by introducing new operators like territories delimitation, clustering-based clan migration, and k-means clustering. Experiments with a large dataset of 1.4 million tweets demonstrate EEHOLSIF's effectiveness, outperforming other metaheuristics like ant colony system and particle swarm optimization in relevance and response time. The study highlights the importance of tailored techniques for navigating the vast social media data and suggests potential future directions for optimization and integration with deep learning.
Mind map
Dynamic grouping for targeted information gathering
Organizing herds into clusters for collective exploration
Division of social space for efficient search
Recommendation systems
Trend prediction
Sentiment analysis
Adaptive strategies for dynamic social media environments
Integration with deep learning
Outperformance results
Particle Swarm Optimization (PSO)
Ant Colony System (ACS)
Response time
Relevance (accuracy or precision)
1.4 million tweets dataset for benchmarking
3. K-means Clustering
2. Clustering-Based Clan Migration
1. Territories Delimitation
Integration of IFT principles
Social graph representation of users, posts, and interactions
Outperform existing metaheuristics
Improve information access and retrieval efficiency
To develop EHO and EEHOLSIF algorithms
Challenges of large-scale data in social media platforms
Information Foraging Theory (IFT) in social media context
Limitations and suggestions for further research
The potential of EHO and EEHOLSIF for large-scale information access
The significance of tailored algorithms for social media data
Applications
Optimization Techniques
Real-world implications
Advantages over traditional methods
EEHOLSIF's effectiveness in navigating social media data
Comparison with Metaheuristics
Performance Metrics
Dataset
Improvement over EHO
Integration of social influence in the optimization process
Operators
Model Description
Objective
Background
Conclusion
Future Directions
Results and Discussion
Experimental Evaluation
EEHOLSIF: Enhanced EHO with Social Influence Factor
Enhanced Elephant Herding Optimization (EHO)
Introduction
Outline
Introduction
Background
Information Foraging Theory (IFT) in social media context
Challenges of large-scale data in social media platforms
Objective
To develop EHO and EEHOLSIF algorithms
Improve information access and retrieval efficiency
Outperform existing metaheuristics
Enhanced Elephant Herding Optimization (EHO)
Model Description
Social graph representation of users, posts, and interactions
Integration of IFT principles
Operators
1. Territories Delimitation
Division of social space for efficient search
2. Clustering-Based Clan Migration
Organizing herds into clusters for collective exploration
3. K-means Clustering
Dynamic grouping for targeted information gathering
EEHOLSIF: Enhanced EHO with Social Influence Factor
Integration of social influence in the optimization process
Improvement over EHO
Experimental Evaluation
Dataset
1.4 million tweets dataset for benchmarking
Performance Metrics
Relevance (accuracy or precision)
Response time
Comparison with Metaheuristics
Ant Colony System (ACS)
Particle Swarm Optimization (PSO)
Outperformance results
Results and Discussion
EEHOLSIF's effectiveness in navigating social media data
Advantages over traditional methods
Real-world implications
Future Directions
Optimization Techniques
Integration with deep learning
Adaptive strategies for dynamic social media environments
Applications
Sentiment analysis
Trend prediction
Recommendation systems
Conclusion
The significance of tailored algorithms for social media data
The potential of EHO and EEHOLSIF for large-scale information access
Limitations and suggestions for further research
Key findings
17

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the problem of large-scale information access on social media by proposing a novel approach that combines the information foraging theory (IFT) with an enhanced version of the Elephant Herding Optimization (EHO) algorithm . This approach is designed to help users efficiently navigate and access information on social media platforms, which have become crucial sources of information for many individuals .

The problem of large-scale information access on social media is not new, as the rapid growth of social media platforms has led to an exponential increase in the volume of online public data . Users rely on social media not only for entertainment but also for seeking information on various topics, including serious issues like health information and current events .

The paper introduces a detailed formal model for information foraging on social media and presents an enhanced version of the EHO algorithm with new operators tailored for large-scale information access . These new contributions aim to improve the efficiency and effectiveness of information retrieval on social media platforms, making it easier for users to find relevant information amidst the vast amount of data available .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate a scientific hypothesis related to large-scale information access on social media by proposing a novel bio-inspired approach that combines the information foraging theory with an enhanced elephant herding optimization . The main contributions of this work include:

  • Introducing a detailed formal model for information foraging on social media.
  • Developing a new enhanced version of elephant herding optimization with new operators tailored for large-scale information access.
  • Utilizing k-means clustering to implement new operators in the enhanced elephant herding optimization.
  • Conducting a performance evaluation on a dataset of over 1.4 million tweets.
  • Performing a comparative study with other metaheuristic-based information access approaches .

What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Enhanced Elephant Herding Optimization for Large Scale Information Access on Social Media" proposes several new ideas, methods, and models in the field of information foraging and optimization . Here are the key contributions outlined in the paper:

  1. Information Foraging Model for Social Media: The paper introduces a detailed formal model for information foraging on social media based on the Information Foraging Theory (IFT) . This model focuses on how people seek and access information on social media platforms, drawing parallels between animal food foraging and information foraging .

  2. Enhanced Elephant Herding Optimization (EEHO): The paper presents an enhanced version of the Elephant Herding Optimization (EHO) algorithm tailored for large-scale information foraging on social media . The EEHO algorithm incorporates new operators, such as territories delimitation and clan migration using clustering, to improve the efficiency and effectiveness of information access .

  3. Territories Concept and Migration Mechanism: The EEHO algorithm introduces the concept of territories and a migration mechanism to enhance information foraging on social media . By defining territories and incorporating a migration mechanism, the algorithm can better target relevant information aligned with users' interests, leading to improved relevance scores and faster convergence rates .

  4. Performance Evaluation: The paper conducts extensive experiments on a dataset of over 1.4 million tweets to validate the proposed EEHO approach . The outcomes of the experiments demonstrate the effectiveness and efficiency of the EEHO algorithm in finding relevant information on social media platforms .

  5. Comparative Study: A comparative study is conducted to evaluate the EEHO algorithm against other metaheuristic-based information foraging approaches, such as ant colony system and particle swarm optimization . The study highlights the advantages of the EEHO algorithm in terms of relevance scores, surfing depth, convergence rates, and response times compared to existing approaches .

Overall, the paper introduces a novel approach that combines the Information Foraging Theory with an enhanced Elephant Herding Optimization algorithm to address large-scale information access on social media, offering significant advancements in the field of information retrieval and optimization . The "Enhanced Elephant Herding Optimization for Large Scale Information Access on Social Media" paper introduces several key characteristics and advantages compared to previous methods, as detailed in the document:

  1. Detailed Formal Model for Information Foraging: The paper presents a detailed formal model for information foraging on social media, which is a significant contribution to the field. This model is based on the Information Foraging Theory and focuses on how individuals seek and access information on social media platforms .

  2. Enhanced Elephant Herding Optimization (EEHO): The paper introduces an enhanced version of the Elephant Herding Optimization (EHO) algorithm specifically tailored for large-scale information access on social media. The EEHO algorithm incorporates new operators and mechanisms, such as territories delimitation and clan migration using clustering, to improve efficiency and effectiveness in information retrieval .

  3. Incorporation of Territories Concept and Migration Mechanism: One of the key advantages of the EEHO algorithm is the incorporation of the territories concept and a migration mechanism. By defining territories and implementing a migration mechanism, the algorithm can better target relevant information aligned with users' interests, leading to improved relevance scores, faster convergence rates, and more efficient information access on social media platforms .

  4. Performance Evaluation and Comparative Study: The paper conducts extensive experiments on a dataset of over 1.4 million tweets to evaluate the performance of the EEHO algorithm. The results demonstrate the effectiveness and efficiency of the EEHO approach in locating relevant information on social media platforms. Additionally, a comparative study is conducted to compare the EEHO algorithm with other metaheuristic-based information foraging approaches, highlighting its advantages in terms of relevance scores, surfing depth, convergence rates, and response times .

  5. Novelty and Contributions: The main contributions of the paper include the introduction of a new enhanced version of the EHO algorithm, the utilization of k-means clustering to implement new operators, a detailed formal model for information foraging on social media, and a comprehensive performance evaluation on a large dataset of tweets. These contributions collectively enhance the efficiency and effectiveness of large-scale information access on social media platforms .

Overall, the EEHO algorithm stands out for its innovative approach, incorporation of territories and migration mechanisms, performance improvements, and the ability to address the challenges of large-scale information foraging on social media platforms, making it a valuable advancement in the field of information retrieval and optimization .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of information foraging and elephant herding optimization. Noteworthy researchers in this field include Yassine Drias, S. Kechid, G. Pasi, V. Nguyen, G. Rabby, V. Sv´atek, O. Corcho, A. Dalton, B. Dorr, L. Liang, K. Hollingshead, A. Jaiswal, H. Liu, I. Frommholz, H. Estiri, B. Abounia Omran, S. Murphy, and Ilyes Khennak .

The key solution mentioned in the paper is the development of an Enhanced Elephant Herding Optimization (EEHO) approach for large-scale information access on social media. This approach combines the information foraging theory with an enhanced version of elephant herding optimization, introducing new concepts such as territories delimitation, clan migration, and k-means clustering to improve the algorithm's performance in handling large-scale information foraging . The EEHO approach aims to provide better relevance scores, faster response times, improved convergence, and increased surfing depth compared to traditional information foraging methods .


How were the experiments in the paper designed?

The experiments in the paper were designed to propose a novel bio-inspired approach to large scale information foraging on social media using enhanced elephant herding optimization and clustering. The experiments involved adapting the Elephant Herding Optimization (EHO) algorithm to address information foraging on social media, which required modifications to key aspects such as the elephants' positions implementation, solution construction, and solution evaluation . The study aimed to enhance the EHO algorithm to work on combinatorial problems, specifically information foraging, by introducing new operators adapted to large scale information access and incorporating k-means clustering to implement these new operators . The experiments included a performance evaluation on a dataset of more than 1.4 million tweets and a comparative study with other metaheuristic-based information access approaches . The experiments focused on maximizing the system's performance by setting empirical parameters, such as the number of territories (clusters), through testing different values and computing the total Within Cluster Sums of Squares (WSS) to measure the average distance between posts and their centroids for each cluster .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided contexts. However, the study focuses on large-scale information foraging on social media using the Enhanced Elephant Herding Optimization algorithm . The code used in the study is not specified to be open source or publicly available in the provided contexts. For more specific details regarding the dataset used for quantitative evaluation and the availability of the code, it would be advisable to refer directly to the study or contact the authors for clarification.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted a performance evaluation on a dataset of over 1.4 million tweets, which demonstrates a robust empirical analysis . The research introduced a novel bio-inspired approach for large-scale information access on social media, combining the information foraging theory with an enhanced elephant herding optimization . This innovative approach included a detailed formal model for information foraging on social media, new operators adapted for large-scale information access, and the incorporation of k-means clustering to enhance the optimization process . Additionally, the study included a comparative analysis with other metaheuristic-based information access approaches, further strengthening the validity of the findings .


What are the contributions of this paper?

The contributions of the paper "Enhanced Elephant Herding Optimization for Large Scale Information Access on Social Media" include:

  • Detailed formal model for information foraging on social media.
  • Introduction of a new enhanced version of elephant herding optimization tailored for large scale information access.
  • Utilization of k-means clustering to implement new operators in the enhanced elephant herding optimization.
  • Performance evaluation conducted on a dataset comprising over 1.4 million tweets.
  • Comparative study with other metaheuristic-based information access approaches .

What work can be continued in depth?

To delve deeper into the research on large-scale information access on social media, a potential avenue for further exploration could be the continuation of the study on the migration mechanism introduced in the Enhanced Elephant Herding Optimization (EEHO) algorithm . This migration parameter controls the maximum number of generations a clan can spend without improving its best solution and influences the movement towards new territories to find better solutions . Investigating the impact of different migration thresholds and strategies on the algorithm's performance could provide valuable insights into optimizing information foraging on social media platforms.

Furthermore, exploring the effectiveness of the territories concept in the EEHO algorithm could be a promising direction for future research. By dividing the search space into multiple regions based on problem-related features, the algorithm aims to explore these areas more efficiently, potentially enhancing its performance, especially in dealing with large-scale problems . Analyzing the impact of varying the number and characteristics of these territories on the algorithm's efficiency and effectiveness in information retrieval tasks could offer valuable contributions to the field.

Moreover, conducting a comparative study with other metaheuristic-based information foraging approaches from the literature could provide valuable insights into the strengths and weaknesses of different optimization techniques. By comparing the EEHO algorithm with existing approaches, researchers can evaluate its performance metrics such as relevance score, surfing depth, convergence rate, and response time . This comparative analysis can help validate the effectiveness of the EEHO algorithm and identify areas for further improvement or optimization in large-scale information access on social media platforms.

Tables
2
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