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Biomimetics
Special Issues
Biomimicry for Optimization, Control, and Automation: 3rd Edition
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Biomimicry for Optimization, Control, and Automation: 3rd Edition

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Biological Optimisation and Management".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 914

Special Issue Editors

Prof. Dr. Yongquan Zhou

E-Mail Website
Guest Editor
School of artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China
Interests: bio-Inspired computing; bionic optimization; computation intelligence; intelligence optimization; neural network
Special Issues, Collections and Topics in MDPI journals
Dr. Huajuan Huang

E-Mail Website
Guest Editor
College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006 China
Interests: bionic optimization; intelligence optimization; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Guo Zhou

E-Mail Website
Guest Editor
Department of Science and Technology Teaching, China University of Political Science and Law, Beijing 102249, China
Interests: bionic optimization; intelligence optimization; graphical visualization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bionic optimization is a relatively cutting-edge research direction in the field of intelligence optimization. There are many highly effective optimization, feedback control, and automation systems embedded in living organisms and nature. Evolution persistently seeks optimal robust designs for biological feedback control systems and decision-making processes. The advantages of intelligence optimization, such as global search and efficient parallelism, provide new ideas and means for solving complex control and automation optimization problems.

This Special Issue aims to collect the latest results regarding biomimicry for optimization, control, and automation applications. To this end, we encourage submissions of meta-heuristic theoretical algorithm papers and reviews, as well as experimental studies dealing with relevant questions in bionic optimization fields. 

Prof. Dr. Yongquan Zhou
Dr. Huajuan Huang
Dr. Guo Zhou
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

  • meta-heuristic
  • bio-inspired computing
  • bionic optimization
  • computation intelligence
  • intelligence control
  • intelligence design
  • automatic assembly

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Published Papers (2 papers)

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21 pages, 2895 KiB  
Article
White Shark Optimization for Solving Workshop Layout Optimization Problem
by Bin Guo, Yuanfei Wei, Qifang Luo and Yongquan Zhou
Biomimetics 2025, 10(5), 268; https://doi.org/10.3390/biomimetics10050268 - 27 Apr 2025
Viewed by 284
Abstract
The workshop is a crucial site for ensuring the smooth operation of production activities within an enterprise, playing a significant role in its long–term development. A well–designed workshop layout can reduce material–handling costs during production and enhance the overall efficiency of the enterprise. [...] Read more.
The workshop is a crucial site for ensuring the smooth operation of production activities within an enterprise, playing a significant role in its long–term development. A well–designed workshop layout can reduce material–handling costs during production and enhance the overall efficiency of the enterprise. This paper establishes a mathematical model for the workshop layout problem, aiming to minimize logistics transportation costs and maximize non–logistics relationships. Using a real–world case study, the White Shark Optimizer (WSO) algorithm is applied to solve the model. The results show that the transportation distance of the layout scheme obtained by the WSO algorithm is reduced by 381 m, 82 m, and 56 m, respectively, compared with the original layout, the Genetic Algorithm (GA), and the Sparrow Search Algorithm (SSA), and the non–logical relationship is increased by 24.84% and 1.6%, respectively. The layout scheme obtained by using the WSO algorithm is more excellent and can effectively improve the production efficiency of enterprises. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 3rd Edition)
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22 pages, 4865 KiB  
Article
An Unsupervised Fusion Strategy for Anomaly Detection via Chebyshev Graph Convolution and a Modified Adversarial Network
by Hamideh Manafi, Farnaz Mahan and Habib Izadkhah
Biomimetics 2025, 10(4), 245; https://doi.org/10.3390/biomimetics10040245 - 17 Apr 2025
Viewed by 372
Abstract
Anomalies refer to data inconsistent with the overall trend of the dataset and may indicate an error or an unusual event. Time series prediction can detect anomalies that happen unexpectedly in critical situations during the usage of a system or a network. Detecting [...] Read more.
Anomalies refer to data inconsistent with the overall trend of the dataset and may indicate an error or an unusual event. Time series prediction can detect anomalies that happen unexpectedly in critical situations during the usage of a system or a network. Detecting or predicting anomalies in the traditional way is time-consuming and error-prone. Accordingly, the automatic recognition of anomalies is applicable to reduce the cost of defects and will pave the way for companies to optimize their performance. This unsupervised technique is an efficient way of detecting abnormal samples during the fluctuations of time series. In this paper, an unsupervised deep network is proposed to predict temporal information. The correlations between the neighboring samples are acquired to construct a graph of neighboring fluctuations. The extricated features related to the temporal distribution of the time samples in the constructed graph representation are used to impose the Chebyshev graph convolution layers. The output is used to train an adversarial network for anomaly detection. A modification is performed for the generative adversarial network’s cost function to perfectly match our purpose. Thus, the proposed method is based on combining generative adversarial networks (GANs) and a Chebyshev graph, which has shown good results in various domains. Accordingly, the performance of the proposed fusion approach of a Chebyshev graph-based modified adversarial network (Cheb-MA) is evaluated on the Numenta dataset. The proposed model was evaluated based on various evaluation indices, including the average F1-score, and was able to reach a value of 82.09%, which is very promising compared to recent research. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 3rd Edition)
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