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Advances in Swarm Intelligence, Data Science and Their Applications

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 20650

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Special Issue Editors

Prof. Dr. Ying Tan

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Guest Editor

School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
Interests: swarm intelligence; swarm intelligence optimization algorithm; fireworks algorithm; swarm robotics; machine learning and data mining
Special Issues, Collections and Topics in MDPI journals

Dr. Yan Pei

E-Mail Website
Guest Editor

School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan
Interests: Computational Intelligence; Machine Learning; Optimization
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gaige Wang

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Guest Editor

Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
Interests: swarm intelligence; natural computing; evolutionary computation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Fernando Buarque

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Guest Editor

Graduate Program in Computer Engineering, Polytechnic School of Pernambuco, University of Pernambuco, Recife 50720-001, Brazil
Interests: artificial intelligence; swarm and evolutionary optimization; new algorithms; Net-Sci; semiotics and AI; AI for compliance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will cover the most recent discovery and development centered around two major topics: swarm intelligence and data science.

Swarm intelligence systems typically study the complex collective behavior that arises from decentralized simple agents with local and/or global interaction. The inspiration for swarm intelligence algorithms usually comes from natural behavior or phenomena, such as ant colonies, bird flocks, fireworks, etc. Typical subdomains of swarm intelligence are swarm-based optimization techniques and multi-agent cooperative systems. It has been proven that swarm intelligence is an effective way to tackle complex problems that arise in various domains such as power systems, robotics, information systems, image processing, computation chemistry, and so on. The importance of swarm intelligence in today’s society is gradually being brought to a whole new level.

On the other hand, data science has gained more and more momentum in the era of big data and artificial intelligence. It utilizes theories and techniques from machine learning, statistics, and information theory to help us extract valuable knowledge, patterns, and insights from data that are usually very large and complex. Some typical applications of data science are fraud detection, recommender systems, bioinformatics, stock market prediction, and so on. Our Special Issue is mainly concerned with advances in the field of data mining, machine learning, pattern recognition, automatic control, and their respective applications.

Prof. Dr. Ying Tan
Prof. Dr. Yan Pei
Prof. Dr. Gaige Wang
Prof. Dr. Fernando Buarque
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. Electronics 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.

Keywords

Swarm intelligence
Big data
Natural computing
Fireworks algorithms
Multi-agent theories
Optimization theories
Data mining
Machine learning
Pattern recognition
Automatic control

Related Special Issue

Advances in Swarm Intelligence, Data Science and Their Applications, 2nd Edition in Electronics (5 articles)

Published Papers (9 papers)

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Research

30 pages, 12499 KiB

Open AccessArticle

Distributed Multi-Mobile Robot Path Planning and Obstacle Avoidance Based on ACO–DWA in Unknown Complex Terrain

by Qian Wang, Junli Li, Liwei Yang, Zhen Yang, Ping Li and Guofeng Xia

Electronics 2022, 11(14), 2144; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11142144 - 08 Jul 2022

Cited by 13 | Viewed by 2756

Abstract

Multi-robot systems are popularly distributed in logistics, transportation, and other fields. We propose a distributed multi-mobile robot obstacle-avoidance algorithm to coordinate the path planning and motion navigation among multiple robots and between robots and unknown territories. This algorithm fuses the ant colony optimization (ACO) and the dynamic window approach (DWA) to coordinate a multi-robot system through a priority strategy. Firstly, to ensure the optimality of robot motion in complex terrains, we proposed the dual-population heuristic functions and a sort ant pheromone update strategy to enhance the search capability of ACO, and the globally optimal path is achieved by a redundant point deletion strategy. Considering the robot’s path-tracking accuracy and local target unreachability problems, an adaptive navigation strategy is presented. Furthermore, we propose the obstacle density evaluation function to improve the robot’s decision-making difficulty in high-density obstacle environments and modify the evaluation function coefficients adaptively by combining environmental characteristics. Finally, the robots’ motion conflict is resolved by combining our obstacle avoidance and multi-robot priority strategies. The experimental results show that this algorithm can realize the cooperative obstacle avoidance of AGVs in unknown environments with high safety and global optimality, which can provide a technical reference for distributed multi-robot in practical applications. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence, Data Science and Their Applications)

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19 pages, 885 KiB

Open AccessArticle

Hierarchical Collaborated Fireworks Algorithm

by Yifeng Li and Ying Tan

Electronics 2022, 11(6), 948; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11060948 - 18 Mar 2022

Cited by 4 | Viewed by 1390

Abstract

The fireworks algorithm (FWA) has achieved significant global optimization ability by organizing multiple simultaneous local searches. By dynamically decomposing the target problem and handling each one with a sub-population, it has presented distinct property and applicability compared with traditional evolutionary algorithms. In this paper, we extend the theoretical model of fireworks algorithm based on search space partition to obtain a hierarchical collaboration model. It maintains both multiple local fireworks for local exploitation and one global firework for overall population distribution control. The implemented hierarchical collaborated fireworks algorithm is able to combine the advantages of both classic evolutionary algorithms and fireworks algorithms. Several experiments are provided for in-depth analysis and discussion on the proposed algorithm. The effectiveness of proposed strategy is demonstrated on the benchmark test suite from CEC 2020. Experimental results validate that the hierarchical collaborated fireworks algorithm outperforms former fireworks algorithms significantly and achieves similar results compared with state-of-the-art evolutionary algorithms. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence, Data Science and Their Applications)

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25 pages, 933 KiB

Open AccessArticle

Simulation of Biochemical Reactions with ANN-Dependent Kinetic Parameter Extraction Method

by Fei Tan and Jin Xu

Electronics 2022, 11(2), 216; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11020216 - 11 Jan 2022

Viewed by 1563

Abstract

The measurement of thermodynamic properties of chemical or biological reactions were often confined to experimental means, which produced overall measurements of properties being investigated, but were usually susceptible to pitfalls of being too general. Among the thermodynamic properties that are of interest, reaction rates hold the greatest significance, as they play a critical role in reaction processes where speed is of essence, especially when fast association may enhance binding affinity of reaction molecules. Association reactions with high affinities often involve the formation of a intermediate state, which can be demonstrated by a hyperbolic reaction curve, but whose low abundance in reaction mixture often preclude the possibility of experimental measurement. Therefore, we resorted to computational methods using predefined reaction models that model the intermediate state as the reaction progresses. Here, we present a novel method called AKPE (ANN-Dependent Kinetic Parameter Extraction), our goal is to investigate the association/dissociation rate constants and the concentration dynamics of lowly-populated states (intermediate states) in the reaction landscape. To reach our goal, we simulated the chemical or biological reactions as system of differential equations, employed artificial neural networks (ANN) to model experimentally measured data, and utilized Particle Swarm Optimization (PSO) algorithm to obtain the globally optimum parameters in both the simulation and data fitting. In the Results section, we have successfully modeled a protein association reaction using AKPE, obtained the kinetic rate constants of the reaction, and constructed a full concentration versus reaction time curve of the intermediate state during the reaction. Furthermore, judging from the various validation methods that the method proposed in this paper has strong robustness and accuracy. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence, Data Science and Their Applications)

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17 pages, 5235 KiB

Open AccessArticle

Distributed Cooperative Jamming with Neighborhood Selection Strategy for Unmanned Aerial Vehicle Swarms

by Yongkun Zhou, Dan Song, Bowen Ding, Bin Rao, Man Su and Wei Wang

Electronics 2022, 11(2), 184; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11020184 - 07 Jan 2022

Cited by 3 | Viewed by 1328

Abstract

In system science, a swarm possesses certain characteristics which the isolated parts and the sum do not have. In order to explore emergence mechanism of a large–scale electromagnetic agents (EAs), a neighborhood selection (NS) strategy–based electromagnetic agent cellular automata (EA–CA) model is proposed in this paper. The model describes the process of agent state transition, in which a neighbor with the smallest state difference in each sector area is selected for state transition. Meanwhile, the evolution rules of the traditional CA are improved, and performance of different evolution strategies are compared. An application scenario in which the emergence of multi–jammers suppresses the radar radiation source is designed to demonstrate the effect of the EA–CA model. Experimental results show that the convergence speed of NS strategy is better than those of the traditional CA evolution rules, and the system achieves effective jamming with the target after emergence. It verifies the effectiveness and prospects of the proposed model in the application of electronic countermeasures (ECM). Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence, Data Science and Their Applications)

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31 pages, 3031 KiB

Open AccessArticle

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

by Souad Larabi-Marie-Sainte, Reham Alskireen and Sawsan Alhalawani

Electronics 2021, 10(24), 3116; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10243116 - 14 Dec 2021

Cited by 7 | Viewed by 2773

Abstract

Image processing is one example of digital media. It consists of a set of operations to handle an image. Image segmentation is among its main important operations. It involves dividing the image into several parts or regions to extract vital information or identify relevant objects. Many techniques of artificial intelligence, including bio-inspired algorithms, have been used in this regard. This article collected the state-of-the-art studies presenting image-segmentation techniques combined with four bio-inspired algorithms including particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), and artificial bee colonies (ABC). This research work aimed at showing the importance of image segmentation and its combination with these algorithms. This article provides insights on how these algorithms are adapted to image-segmentation combinatorial problems, which assist researchers to start the first hands-on application. It also discusses their setting parameters and the highly used algorithms such as PSO, GA, ACO, and ABC. The article presents new research directions in image segmentation based on bio-inspired algorithms. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence, Data Science and Their Applications)

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16 pages, 5252 KiB

Open AccessArticle

Speeding Up Velocity Consensus Control with Small World Communication Topology for Unmanned Aerial Vehicle Swarms

by Xiang Ji, Wanpeng Zhang, Shaofei Chen, Junren Luo, Lina Lu, Weilin Yuan, Zhenzhen Hu and Jing Chen

Electronics 2021, 10(20), 2547; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10202547 - 18 Oct 2021

Cited by 4 | Viewed by 1367

Abstract

This study addressed a problem of rapid velocity consensus within a swarm of unmanned aerial vehicles. Our analytical framework was based on tools using matrix theory and algebraic graph theory. We established connections between algebraic connectivity and the speed of converging on a velocity. The relationship between algebraic connectivity and communication cost was established. To deal with the trade-off among algebraic connectivity, convergence speed and communication cost, we propose a distributed small world network construction method. The small world network characteristics expedite the convergence speed toward consensus in the unmanned aerial vehicle swarm. Eventually, our method greatly sped up the consensus velocities in the unmanned aerial vehicle swarms at a lower communication cost than other methods required. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence, Data Science and Their Applications)

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16 pages, 1396 KiB

Open AccessArticle

DWSA: An Intelligent Document Structural Analysis Model for Information Extraction and Data Mining

by Tan Yue, Yong Li and Zonghai Hu

Electronics 2021, 10(19), 2443; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10192443 - 08 Oct 2021

Cited by 5 | Viewed by 1856

Abstract

The structure of a document contains rich information such as logical relations in context, hierarchy, affiliation, dependence, and applicability. It will greatly affect the accuracy of document information processing, particularly of legal documents and business contracts. Therefore, intelligent document structural analysis is important to information extraction and data mining. However, unlike the well-studied field of text semantic analysis, current work in document structural analysis is still scarce. In this paper, we propose an intelligent document structural analysis framework through data pre-processing, feature engineering, and structural classification with a dynamic sample weighting algorithm. As a typical application, we collect more than 11,000 insurance document content samples and carry out the machine learning experiments to check the efficiency of our framework. Meanwhile, to address the sample imbalance problem in the hierarchy classification task, a dynamic sample weighting algorithm is incorporated into our Dynamic Weighting Structural Analysis (DWSA) framework, in which the weights of different category tags according to the structural levels are iterated dynamically in training. Our results show that the DWSA has significantly improved the comprehensive accuracy and the classification F1-score of each category. The comprehensive accuracy is as high as 94.68% (3.36% absolute improvement) and the Macro F1-score is 88.29% (5.1% absolute improvement). Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence, Data Science and Their Applications)

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20 pages, 15874 KiB

Open AccessArticle

Identifying Communication Topologies on Twitter

by Mijat Kustudic, Bowen Xue, Huifen Zhong, Lijing Tan and Ben Niu

Electronics 2021, 10(17), 2151; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10172151 - 03 Sep 2021

Cited by 1 | Viewed by 1962

Abstract

Social networks are known for their decentralization and democracy. Each individual has a chance to participate and influence any discussion. Even with all the freedom, people’s behavior falls under patterns that are observed in numerous situations. In this paper, we propose a methodology that defines and searches for common communication patterns in topical networks on Twitter. We analyze clusters according to four traits: number of nodes the cluster has, their degree and betweenness centrality values, number of node types, and whether the cluster is open or closed. We find that cluster structures can be defined as (a) fixed, meaning that they are repeated across datasets/topics following uniform rules, or (b) variable if they follow an underlying rule regardless of their size. This approach allows us to classify 90% of all conversation clusters, with the number varying by topic. An increase in cluster size often results in difficulties finding topological shape rules; however, these types of clusters tend to exhibit rules regarding their node relationships in the form of centralization. Most individuals do not enter large-scale discussions on Twitter, meaning that the simplicity of communication clusters implies repetition. In general, power laws apply for the influencer connection distribution (degree centrality) even in topical networks. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence, Data Science and Their Applications)

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16 pages, 2708 KiB

Open AccessArticle

Predicting Fundraising Performance in Medical Crowdfunding Campaigns Using Machine Learning

by Nianjiao Peng, Xinlei Zhou, Ben Niu and Yuanyue Feng

Electronics 2021, 10(2), 143; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10020143 - 11 Jan 2021

Cited by 8 | Viewed by 3706

Abstract

The coronavirus disease (COVID-19) pandemic has flooded public health organizations around the world, highlighting the significance and responsibility of medical crowdfunding in filling a series of gaps and shortcomings in the publicly funded health system and providing a new fundraising solution for people that addresses health-related needs. However, the fact remains that medical fundraising from crowdfunding sources is relatively low and only a few studies have been conducted regarding this issue. Therefore, the performance predictions and multi-model comparisons of medical crowdfunding have important guiding significance to improve the fundraising rate and promote the sustainable development of medical crowdfunding. Based on the data of 11,771 medical crowdfunding campaigns from a leading donation-based platform called Weibo Philanthropy, machine-learning algorithms were applied. The results demonstrate the potential of ensemble-based machine-learning algorithms in the prediction of medical crowdfunding project fundraising amounts and leave some insights that can be taken into consideration by new researchers and help to produce new management practices. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence, Data Science and Their Applications)

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