Biomimetics and Bioinspired Artificial Intelligence Applications

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1153

Special Issue Editors


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Guest Editor
School of Computer Science and Technology, Shandong University of Finance and Economics, No. 7366, East Second Ring Road, Yaojia Sub-District, Jinan 250014, China
Interests: machine learning; data mining; multimedia processing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
Interests: machine learning; data mining; multimedia processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomimetics focuses on living systems and attempts to transfer their properties to engineering applications and has dramatically influenced human civilization. In recent decades, the integration of biomimetics and computing methods has achieved great results in a variety of artificial intelligence applications, including medical diagnosis, robotics, optimization, and pattern recognition. This Special Issue seeks to understand how to design biomimetic machinery and material models that mimic the properties and structures of organisms and report the latest advances in the area of bioinspired algorithms in artificial intelligence. We welcome the manuscripts devoted to the original research, meta-analysis, and review articles related to these directions. Potential topics include, but are not limited to:

  • Biomimetics of materials and structures;
  • Biomimetic design, construction, and devices;
  • Bioinspired robotics and autonomous systems;
  • Applications of bioinspired methods in computer vision and signal processing;
  • Brain-inspired computing methods, e.g., neural networks and deep learning;
  • Swarm intelligence and collective behaviour, e.g., particle swarm optimization and ant colony optimization;
  • Evolutionary algorithms and optimization, e.g., genetic algorithms;
  • Adaptive and self-learning systems.

Prof. Dr. Chaoran Cui
Dr. Xiaohui Han
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. Biomimetics 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 2200 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

  • biomimetics of materials and structures
  • biomimetic design, construction, and devices
  • bioinspired robotics and autonomous systems
  • applications of bioinspired methods in computer vision and signal processing
  • brain-inspired computing methods, e.g., neural networks and deep learning
  • swarm intelligence and collective behaviour, e.g., particle swarm optimization and ant colony optimization
  • evolutionary algorithms and optimization, e.g., genetic algorithms
  • adaptive and self-learning systems

Published Papers (1 paper)

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Research

22 pages, 5251 KiB  
Article
Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services
by Gabriel Ioan Arcas, Tudor Cioara and Ionut Anghel
Biomimetics 2024, 9(5), 302; https://doi.org/10.3390/biomimetics9050302 - 18 May 2024
Viewed by 696
Abstract
As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable [...] Read more.
As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services’ computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time. Full article
(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications)
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