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Emerging Research in Optimization Algorithms in the Era of Big...
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Emerging Research in Optimization Algorithms in the Era of Big Data

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 5437

Special Issue Editors

Dr. Marko Gulić

E-Mail Website
Guest Editor
Department of Electrical Engineering, Automation and Computing, Faculty of Maritime Studies, University of Rijeka, 51000 Rijeka, Croatia
Interests: metaheuristic algorithms; web semantics; ontology matching; ontology alignment; web development
Prof. Dr. Heming Jia

E-Mail Website
Guest Editor
School of Information Engineering, Sanming University, Sanming, China
Interests: Remora Optimization Algorithm (ROA); Crayfish Optimization Algorithm (COA); Catch Fish Optimization Algorithm (CFOA); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling and optimization algorithms; evolutionary computations; multilevel image segmentation; feature selection; combinatorial problems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antonio Jiménez-Martín

E-Mail Website
Guest Editor
Departamento de Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid (UPM), 28660 Madrid, Spain
Interests: multicriteria decision making; decision support systems; metaheuristic-based optimization; discret-event simulation; risk analysis and management; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of big data, where the volume, velocity, and variety of data are increasing exponentially, the development of efficient optimization algorithms is an important goal. This Special Issue explores the dynamic landscape of optimization algorithms tailored to the challenges of big data analytics and presents the latest advances and emerging trends. From traditional optimization techniques to cutting-edge machine learning algorithms and metaheuristic approaches, the range of contributions highlights the diversity and richness of current research efforts. The convergence of computing power, advanced algorithms, and huge datasets has led to a renaissance of optimization methods and produced innovative approaches for solving complex optimization problems in big data applications. This collection covers a wide range of topics, including evolutionary algorithms, genetic programming, swarm intelligence, nature-inspired optimization techniques, parallel and distributed optimization algorithms optimized for the Cloud, deep learning-based optimization strategies, hybrid optimization frameworks, and optimization algorithms for real-time processing of big data and streaming analytics. In addition, applications of optimization algorithms are explored in various areas, such as healthcare, finance, transportation, and cybersecurity, incorporating advances in generative AI to improve optimization capabilities in cloud-based environments. Through this compilation, researchers and practitioners will gain insights into the latest methodologies, challenges, and opportunities in the field of optimization algorithms for big data analytics that drive innovation and enable transformative breakthroughs in data-driven decision making. The interdisciplinary nature of these contributions emphasizes the collaboration between computer science, mathematics, engineering, and various domain-specific disciplines. This Special Issue is a testament to the vibrant research community dedicated to advancing optimization algorithms in the context of big data analytics.

Dr. Marko Gulić
Prof. Dr. Heming Jia
Prof. Dr. Antonio Jiménez-Martín
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optimization algorithms
  • big data analytics
  • machine learning algorithms
  • metaheuristic approaches
  • evolutionary algorithms
  • genetic programming
  • swarm intelligence
  • nature-inspired optimization techniques
  • parallel and distributed optimization algorithms

Benefits of Publishing in a Special Issue

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

Jump to: Review

20 pages, 10999 KiB  
Article
Stacking Ensemble Technique Using Optimized Machine Learning Models with Boruta–XGBoost Feature Selection for Landslide Susceptibility Mapping: A Case of Kermanshah Province, Iran
by Zeynab Yousefi, Ali Asghar Alesheikh, Ali Jafari, Sara Torktatari and Mohammad Sharif
Information 2024, 15(11), 689; https://doi.org/10.3390/info15110689 - 2 Nov 2024
Cited by 1 | Viewed by 1868
Abstract
Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibility map (LSM) is required to reduce the adverse effects of landslides. Machine learning (ML) is a robust tool for LSM creation. ML models require large amounts [...] Read more.
Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibility map (LSM) is required to reduce the adverse effects of landslides. Machine learning (ML) is a robust tool for LSM creation. ML models require large amounts of data to predict landslides accurately. This study has developed a stacking ensemble technique based on ML and optimization to enhance the accuracy of an LSM while considering small datasets. The Boruta–XGBoost feature selection was used to determine the optimal combination of features. Then, an intelligent and accurate analysis was performed to prepare the LSM using a dynamic and hybrid approach based on the Adaptive Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and new optimization algorithms (Ladybug Beetle Optimization [LBO] and Electric Eel Foraging Optimization [EEFO]). After model optimization, a stacking ensemble learning technique was used to weight the models and combine the model outputs to increase the accuracy and reliability of the LSM. The weight combinations of the models were optimized using LBO and EEFO. The Root Mean Square Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) parameters were used to assess the performance of these models. A landslide dataset from Kermanshah province, Iran, and 17 influencing factors were used to evaluate the proposed approach. Landslide inventory was 116 points, and the combined Voronoi and entropy method was applied for non-landslide point sampling. The results showed higher accuracy from the stacking ensemble technique with EEFO and LBO algorithms with AUC-ROC values of 94.81% and 94.84% and RMSE values of 0.3146 and 0.3142, respectively. The proposed approach can help managers and planners prepare accurate and reliable LSMs and, as a result, reduce the human and financial losses associated with landslide events. Full article
(This article belongs to the Special Issue Emerging Research in Optimization Algorithms in the Era of Big Data)
►▼ Show Figures

Figure 1

Figure 1
<p>Study area: Kermanshah Province, Iran, and historical landslide events.</p> Full article ">Figure 2
<p>Map of landslide conditioning factors: (<b>a</b>) elevation; (<b>b</b>) slope; (<b>c</b>) aspect; (<b>d</b>) valley depth; (<b>e</b>) profile curvature; (<b>f</b>) plan curvature; (<b>g</b>) lithology; (<b>h</b>) soil type; (<b>i</b>) soil texture; (<b>j</b>) distance to faults; (<b>k</b>) land use; (<b>l</b>) distance to roads; (<b>m</b>) SPI; (<b>n</b>) TWI; (<b>o</b>) distance to drainage; (<b>p</b>) drainage density; (<b>q</b>) rainfall.</p> Full article ">Figure 3
<p>Flowchart of the LSM using the stacking ensemble machine learning models.</p> Full article ">Figure 4
<p>Steps to create non-landslide points. (<b>a</b>) Candidate points from the Voronoi map. (<b>b</b>) Final non-landslide points from the combination of the Voronoi and entropy maps.</p> Full article ">Figure 5
<p>Factor ranks by importance extracted using the Boruta–XGBoost results.</p> Full article ">Figure 6
<p>Convergence diagram of the ML models using meta-heuristic algorithms.</p> Full article ">Figure 7
<p>Landslide susceptibility mapping derived from different models: (<b>a</b>) ANFIS–EEFO; (<b>b</b>) SVR–LBO; (<b>c</b>) ELM–LBO; (<b>d</b>) Stacking–EEFO; (<b>e</b>) Stacking–LBO.</p> Full article ">Figure 8
<p>Percentage of area for susceptibility classes in the ML models.</p> Full article ">
19 pages, 2994 KiB  
Article
Voltage Deviation Improvement in Microgrid Operation through Demand Response Using Imperialist Competitive and Genetic Algorithms
by Mahdi Ghaffari and Hamed H. Aly
Information 2024, 15(10), 638; https://doi.org/10.3390/info15100638 - 14 Oct 2024
Viewed by 608
Abstract
In recent decades, with the expansion of distributed energy generation technologies and the increasing need for more flexibility and efficiency in energy distribution systems, microgrids have been considered a promising innovative solution for local energy supply and enhancing resilience against network fluctuations. One [...] Read more.
In recent decades, with the expansion of distributed energy generation technologies and the increasing need for more flexibility and efficiency in energy distribution systems, microgrids have been considered a promising innovative solution for local energy supply and enhancing resilience against network fluctuations. One of the basic challenges in the operation of microgrids is the optimal management of voltage and frequency in the network, which has been the subject of extensive research in the field of microgrid operational optimization. The energy demand is considered a crucial element for energy management due to its fluctuating nature over the day. The use of demand response strategies for energy management is one of the most important factors in dealing with renewables. These strategies enable better energy management in microgrids, thereby improving system efficiency and stability. Given the complexity of optimization problems related to microgrid management, evolutionary optimization algorithms such as the Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) have gained great attention. These algorithms enable solving high-complexity optimization problems by considering various constraints and multiple objectives. In this paper, both ICA and GA, as well as their hybrid application, are used to significantly enhance the voltage regulation in microgrids. The integration of optimization techniques with demand response strategies improves the overall system efficiency and stability. The results proved that the hybrid method provides valuable insights for optimizing energy management systems. Full article
(This article belongs to the Special Issue Emerging Research in Optimization Algorithms in the Era of Big Data)
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Figure 1

Figure 1
<p>Demand response programs [<a href="#B11-information-15-00638" class="html-bibr">11</a>].</p> Full article ">Figure 2
<p>Flowchart of a genetic algorithm [<a href="#B9-information-15-00638" class="html-bibr">9</a>].</p> Full article ">Figure 3
<p>Flowchart of the Imperialist Competitive Algorithm [<a href="#B20-information-15-00638" class="html-bibr">20</a>].</p> Full article ">Figure 4
<p>Optimization process sequence.</p> Full article ">Figure 5
<p>IEEE 33-Bus network.</p> Full article ">Figure 6
<p>The voltage profile of the network is at different hours of the day in the initial state.</p> Full article ">Figure 7
<p>The voltage profile of the network is at different hours of the day in the first scenario.</p> Full article ">Figure 8
<p>The voltage profile of the network is at different hours of the day in the second scenario.</p> Full article ">Figure 9
<p>The voltage profile of the network is at different hours of the day in the third scenario.</p> Full article ">
19 pages, 1870 KiB  
Article
Bullying Detection Solution for GIFs Using a Deep Learning Approach
by Razvan Stoleriu, Andrei Nascu, Ana Magdalena Anghel and Florin Pop
Information 2024, 15(8), 446; https://doi.org/10.3390/info15080446 - 30 Jul 2024
Viewed by 1847
Abstract
Nowadays, technology allows people to connect and communicate with each other even from miles away, no matter the distance. With the increased use of social networks that were rapidly adopted in human beings’ lives, they can chat and share different media files. While [...] Read more.
Nowadays, technology allows people to connect and communicate with each other even from miles away, no matter the distance. With the increased use of social networks that were rapidly adopted in human beings’ lives, they can chat and share different media files. While the intent for which they have been created may be positive, they can be abused and utilized in a negative way. One form in which they can be maliciously used is represented by cyberbullying. This is a form of bullying where an aggressor shares, posts, or sends false, harmful, or negative content about someone else by electronic means. In this paper, we propose a solution for bullying detection in GIFs. We employ a hybrid architecture that comprises a Convolutional Neural Network (CNN) and three Recurrent Neural Networks (RNNs). For the feature extractor, we used the DenseNet-121 model that was pre-trained on the ImageNet-1k dataset. The obtained results give an accuracy of 99%. Full article
(This article belongs to the Special Issue Emerging Research in Optimization Algorithms in the Era of Big Data)
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Figure 1

Figure 1
<p>Non-bullying GIF removed from dataset.</p> Full article ">Figure 2
<p>GIF files with anti-bullying content (<a href="http://giphy.com" target="_blank">giphy.com</a>, accessed on 29 July 2024).</p> Full article ">Figure 3
<p>GIF files with bullying content (<a href="http://giphy.com" target="_blank">giphy.com</a>, accessed on 29 July 2024).</p> Full article ">Figure 4
<p>Cyberbullying GIF decomposed to frames.</p> Full article ">Figure 5
<p>Overview of the proposed approach.</p> Full article ">Figure 6
<p>Confusion matrix for all classification categories.</p> Full article ">Figure 7
<p>Confusion matrix.</p> Full article ">Figure 8
<p>Confusion matrix for the architecture that uses just one RNN model.</p> Full article ">Figure 9
<p>Test GIF file related to jumping.</p> Full article ">

Review

Jump to: Research

33 pages, 1407 KiB  
Review
An Exploratory Investigation of Chatbot Applications in Anxiety Management: A Focus on Personalized Interventions
by Alexia Manole, Răzvan Cârciumaru, Rodica Brînzaș and Felicia Manole
Information 2025, 16(1), 11; https://doi.org/10.3390/info16010011 - 29 Dec 2024
Viewed by 383
Abstract
Anxiety disorders are among the most prevalent mental health conditions globally, causing significant personal and societal burdens. Traditional therapies, while effective, often face barriers such as limited accessibility, high costs, and the stigma associated with seeking mental health care. The emergence of artificial [...] Read more.
Anxiety disorders are among the most prevalent mental health conditions globally, causing significant personal and societal burdens. Traditional therapies, while effective, often face barriers such as limited accessibility, high costs, and the stigma associated with seeking mental health care. The emergence of artificial intelligence (AI) chatbots offers a novel solution by providing accessible, cost-effective, and immediate support for individuals experiencing anxiety. This comprehensive review examines the evolution, efficacy, advantages, limitations, challenges, and future perspectives of AI chatbots in the treatment of anxiety disorders. A methodologically rigorous literature search was conducted across multiple databases, focusing on publications from 2010 to 2024 that evaluated AI chatbot interventions targeting anxiety symptoms. Empirical studies demonstrate that AI chatbots can effectively reduce anxiety symptoms by delivering therapeutic interventions like cognitive-behavioral therapy through interactive and personalized dialogues. The advantages include increased accessibility without geographical or temporal limitations, reduced costs, and an anonymity that encourages openness and reduces stigma. However, limitations persist, such as the lack of human empathy, ethical and privacy concerns related to data security, and technical challenges in understanding complex human emotions. The key challenges identified involve enhancing the emotional intelligence of chatbots, integrating them with traditional therapy, and establishing robust ethical frameworks to ensure user safety and data protection. Future research should focus on improving AI capabilities, personalization, cultural adaptation, and user engagement. In conclusion, AI chatbots represent a promising adjunct in treating anxiety disorders, offering scalable interventions that can complement traditional mental health services. Balancing technological innovation with ethical responsibility is crucial to maximize their potential benefits. Full article
(This article belongs to the Special Issue Emerging Research in Optimization Algorithms in the Era of Big Data)
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<p>Study selection process.</p> Full article ">Figure 2
<p>Research framework workflow.</p> Full article ">
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Analysis of Voltage Deviation Improvement in Microgrid Operation through Demand Response using Imperialist Competitive and Genetic Algorithms
Authors: Mahdi Ghaffari and Hamed H. Aly
Affiliation: Dalhousie University.
Abstract: In recent decades, with the expansion of distributed energy production technologies and increasing needs for more flexibility and efficiency in energy distribution systems, microgrids have a good innovative solution for local energy supply and enhancing resilience against network fluctuations. One of the basic challenges in the operation of microgrids is the optimal management of voltage and frequency in the network, which has been the subject to extensive research in the field of microgrid operational optimization. Additionally, since energy demand changes over time, the use of demand response strategies is of great importance. These strategies enable better energy management in microgrids, thereby improving system efficiency and stability. Given the complexity of optimization problems related to microgrid management, evolutionary optimization algorithms such as the Imperialist Competitive Algorithm (ICA) and Genetic Algorithm have gained attention. These algorithms enable solving high-complexity optimization problems by considering various constraints and multiple objectives. In this paper individual and hybrid of ICA and GA are used to check the effects of voltage division optimization in microgrids based on demand response strategies.

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