Special Issue "Swarm Information Acquisition and Swarm Intelligence in Engineering"
A special issue of Information (ISSN 2078-2489).
Deadline for manuscript submissions: closed (1 June 2015)
Interests: artificial intelligence; urban logistics; public transportation
Interests: deep learning; convolutional neural network; graph convolutional network; attention network; explainable AI; biomedical image analysis; bio-inspired computing; pattern recognition; transfer learning; medical sensor
Special Issues, Collections and Topics in MDPI journals
Special Issue in Information: Computational Intelligence Technique in Medical Image Analysis
Special Issue in Technologies: Medical Imaging & Image Processing Ⅱ
Special Issue in Applied Sciences: Fractal Based Information Processing and Recognition
Special Issue in Journal of Imaging: Deep Learning in Medical Image Analysis
Special Issue in Sensors: Explainable AI in Medical Sensors
Special Issue in Electronics: Data-Driven Processing from Complex Systems Perspective
Special Issue in Journal of Imaging: Deep Learning in Medical Image Analysis, Volume II
Special Issue in Methods and Protocols: Methods and Protocols in Recent Artificial Intelligence
Special Issue in Applied Sciences: Selected Papers from FCPAE2021 and 3rd International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2021)
Special Issue in Technologies: Medical Imaging & Image Processing III
Special Issue in Multimodal Technologies and Interaction: Systems Simulation and Modelling for IoT Data Processing Applications
Special Issue in Sensors: Biomedical Sensors-Recent Advances and Future Challenges 2022
Special Issue in Methods and Protocols: Methods and Protocols of AI in Biomedicine
Topics: Big Data in Healthcare, Bioinformatics and Precision Medicine
Topics: Fighting against COVID-19: Latest Advances, Challenges and Methodologies
Swarm intelligence (SI) is an artificial intelligence technique based on the study of the behavior of simple individuals (e.g., ant colonies, bird flocking, animal herding and honey bees) in various decentralized systems. The population, which consists of simple individuals, can usually solve complex tasks in nature by individuals interacting locally with one another and with their environment. Although their behaviors are primarily characterized by autonomy, distributed functioning and self-organizing capacities, local interactions among the individuals often cause a global optimal.
Recently, SI algorithms have attracted much attention from researchers and have also been applied successfully to solve optimization problems in engineering. However, for large and complex problems, SI algorithms consume often much computation time due to stochastic feature of the search approaches. Therefore, there is a potential requirement to develop an efficient algorithm to find solutions under limited time and financial resources in real-world applications.
The aim of this special issue is to highlight the most significant recent developments in the topics of SI and to apply SI algorithms in a real-life scenario. Contributions containing new insights and findings in this field are welcome.
Dr. Baozhen Yao
Prof. Dr. Yudong Zhang
Manuscript Submission Information
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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 1400 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.
- benchmarking and evaluation of new si algorithms
- convergence proof for si algorithms
- comparative theoretical and empirical studies on si algorithms (e.g., ant colony optimization, particle swarm optimization, artificial bee swarm algorithm, bacterial foraging optimization, artificial fish algorithm, …)
- si algorithms for real-world applications