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Applied Sciences
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Anomaly Detection, Optimization and Control with Swarm Intelligence
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Anomaly Detection, Optimization and Control with Swarm Intelligence

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 4883

Special Issue Editors

Prof. Dr. Pan Wang

E-Mail Website
Guest Editor
School of Automation, Wuhan University of Technology, Wuhan, China
Interests: intelligent optimization and control; industrial information integration; decision analysis; multi-objective optimization; machine learning
Dr. Xiaomin Zhu

E-Mail Website
Guest Editor
College of Systems Engineering, National University of Defense Technology, Changsha, China
Interests: swarm intelligence; distributed collaboration; intelligent decision-making
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhun Fan

E-Mail Website
Guest Editor
College of Engineering, Shantou University, Shantou 515063, China
Interests: artificial intelligence and robotics; swarm intelligence; computational intelligence; design automation; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to collect high-quality original research and review papers from the fields of anomaly detection, optimization and control with swarm intelligence. We encourage researchers from various fields within the journal’s scope to submit papers highlighting the latest developments in the fields of anomaly detection, optimization and control with swarm intelligence, or to invite relevant experts and colleagues to do so. Topics of interest for this Special Issue include, but are not limited to:

  • Anomaly detection with swarm intelligence;
  • Optimization with swarm intelligence;
  • Control with swarm intelligence;
  • Anomaly detection with causal learning;
  • Theories and applications of swarm intelligence;
  • Anomaly detection, optimization and control of multiple agents;
  • Constrained multi-objective optimization and its application with swarm intelligence.

Prof. Dr. Pan Wang
Dr. Xiaomin Zhu
Prof. Dr. Zhun Fan
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.

Published Papers (4 papers)

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Research

31 pages, 3341 KiB  
Article
Anomaly Detection for IOT Systems Using Active Learning
by Mohammed Zakariah and Abdulaziz S. Almazyad
Appl. Sci. 2023, 13(21), 12029; https://doi.org/10.3390/app132112029 - 04 Nov 2023
Viewed by 1735
Abstract
The prevalence of Internet of Things (IoT) technologies is on the rise, making the identification of anomalies in IoT systems crucial for ensuring their security and reliability. However, many existing approaches rely on static classifiers and immutable datasets, limiting their effectiveness. In this [...] Read more.
The prevalence of Internet of Things (IoT) technologies is on the rise, making the identification of anomalies in IoT systems crucial for ensuring their security and reliability. However, many existing approaches rely on static classifiers and immutable datasets, limiting their effectiveness. In this paper, we have utilized the UNSW-NB15 dataset, which contains 45 variables including multi- and binary-target variables, to determine the most relevant properties for detecting abnormalities in IoT systems. To address this issue, our research has investigated the use of active learning-based algorithms for anomaly detection in IoT systems. Active learning is a powerful technique that improves precision and productivity by eliminating the need for labeling and adapting to dynamic IoT environments. Additionally, our study has combined feature engineering methods, active learning approaches, and a random forest classifier to construct a resilient anomaly detection model for IoT devices. The proposed model has outperformed several state-of-the-art techniques, achieving an impressive accuracy rate of 99.7%. By implementing a rigorous sampling procedure and leveraging the collaborative nature of the random forest technique, our model has demonstrated a notable level of precision with a weighted average accuracy of 0.995. The findings of the study offered empirical evidence, supporting the efficacy of our active learning methodology in identifying abnormalities in IoT systems. Moreover, our study provides valuable insights and recommendations for future research and development activities in this field. Overall, this research contributes to the advancement of anomaly detection techniques in IoT systems, further enhancing their security and reliability. Full article
(This article belongs to the Special Issue Anomaly Detection, Optimization and Control with Swarm Intelligence)
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16 pages, 2423 KiB  
Article
Effective Connectivity Changes among Brain Hierarchical Architecture of Pre-Supplementary Motor Area in Taxi Drivers
by Huilin Wei, Lubin Wang, Limin Peng, Chenming Li, Tian Ma and Dewen Hu
Appl. Sci. 2023, 13(20), 11471; https://doi.org/10.3390/app132011471 - 19 Oct 2023
Viewed by 691
Abstract
Much effort has been devoted towards the identification of brain areas recruited during driving—as one of the most common motor skills of human beings. However, how driving experience impacts on the brain’s intrinsic functional architecture has not been fully investigated. Using resting-state fMRI [...] Read more.
Much effort has been devoted towards the identification of brain areas recruited during driving—as one of the most common motor skills of human beings. However, how driving experience impacts on the brain’s intrinsic functional architecture has not been fully investigated. Using resting-state fMRI data collected from 20 taxi drivers and 20 nondrivers, this paper asks whether there exists specific brain network integration encoding driving behavior. First, to address this, we proposed a general framework combining whole-brain functional connectivity analysis with effective connectivity analysis based on spectral Dynamic Causal Modeling. The validation results indicated that the application of this framework could effectively discover the brain network that best explained the observed BOLD fluctuations. Second, by segmenting supplementary motor area (SMA) into pre-SMA and SMA proper sub-regions, we used the above framework and discovered a hierarchical architecture with pre-SMA located at the higher level in both driver and control groups. Third, we further evaluated the possibility that driving behavior could be encoded by directed connections among the hierarchy, and found that the effective connectivity from pre-SMA to left superior frontal gyrus could distinguish drivers from nondrivers with a sensitivity of 80%. Our findings provide a new paradigm for analyzing the brain’s intrinsic functional integration, and may shed new light on the theory of neuroplasticity that training and experience can remodel the patterns of correlated spontaneous brain activity between specific processing regions. Meanwhile, from a methodological advantage perspective, our proposed framework takes the functional connectivity results as a prior, enabling subsequent spectral DCM to efficiently assess functional integration at a whole-brain scale, which is not available by only using other DCM methods, such as stochastic DCM or the State-of-the-Art multimodal DCM. Full article
(This article belongs to the Special Issue Anomaly Detection, Optimization and Control with Swarm Intelligence)
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21 pages, 1625 KiB  
Article
Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool
by Yingui Huang, Ying Yu, Jinglei Guo and Yong Wu
Appl. Sci. 2023, 13(18), 10445; https://doi.org/10.3390/app131810445 - 19 Sep 2023
Viewed by 591
Abstract
As a newly developed metaheuristic algorithm, the artificial bee colony (ABC) has garnered a lot of interest because of its strong exploration ability and easy implementation. However, its exploitation ability is poor and dramatically deteriorates for high-dimension and/or non-separable functions. To fix this [...] Read more.
As a newly developed metaheuristic algorithm, the artificial bee colony (ABC) has garnered a lot of interest because of its strong exploration ability and easy implementation. However, its exploitation ability is poor and dramatically deteriorates for high-dimension and/or non-separable functions. To fix this defect, a self-adaptive ABC with a candidate strategy pool (SAABC-CS) is proposed. First, several search strategies with different features are assembled in the strategy pool. The top 10% of the bees make up the elite bee group. Then, we choose an appropriate strategy and implement this strategy for the present population according to the success rate learning information. Finally, we simultaneously implement some improved neighborhood search strategies in the scout bee phase. A total of 22 basic benchmark functions and the CEC2013 set of tests were employed to prove the usefulness of SAABC-CS. The impact of combining the five methods and the self-adaptive mechanism inside the SAABC-CS framework was examined in an experiment with 22 fundamental benchmark problems. In the CEC2013 set of tests, the comparison of SAABC-CS with a number of state-of-the-art algorithms showed that SAABC-CS outperformed these widely-used algorithms. Moreover, despite the increasing dimensions of CEC2013, SAABC-CS was robust and offered a higher solution quality. Full article
(This article belongs to the Special Issue Anomaly Detection, Optimization and Control with Swarm Intelligence)
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12 pages, 2950 KiB  
Article
Speaker Recognition Based on Dung Beetle Optimized CNN
by Xinhua Guo, Xiao Qin, Qing Zhang, Yuanhuai Zhang, Pan Wang and Zhun Fan
Appl. Sci. 2023, 13(17), 9787; https://doi.org/10.3390/app13179787 - 30 Aug 2023
Cited by 3 | Viewed by 1092
Abstract
Speaker recognition methods based on convolutional neural networks (CNN) have been widely used in the security field and smart wearable devices. However, the traditional CNN has many hyperparameters that are difficult to determine, making the model easily fall into local optimum or even [...] Read more.
Speaker recognition methods based on convolutional neural networks (CNN) have been widely used in the security field and smart wearable devices. However, the traditional CNN has many hyperparameters that are difficult to determine, making the model easily fall into local optimum or even fail to converge during the training process. Intelligent algorithms such as particle swarm optimization and genetic algorithms are used to solve the above problems. However, these algorithms perform poorly compared to the current emerging meta-heuristic algorithms. In this study, the dung beetle optimized convolution neural network (DBO-CNN) is proposed to identify the speakers for the first time, which is helpful in finding suitable hyperparameters for training. By testing the dataset of 50 people, it was demonstrated that the accuracy of the model was significantly improved by using this approach. Compared with the traditional CNN and CNN optimized by other intelligent algorithms, the average accuracy of DBO-CNN has increased by 1.22~4.39% and reached 97.93%. Full article
(This article belongs to the Special Issue Anomaly Detection, Optimization and Control with Swarm Intelligence)
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