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Biomimetics
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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 2933

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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Related Special Issues

Published Papers (5 papers)

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Research

23 pages, 1998 KB  
Article
Hybrid Cuckoo Search–Bees Algorithm with Memristive Chaotic Initialization for Cryptographically Strong S-Box Generation
by Sinem Akyol
Biomimetics 2025, 10(9), 610; https://doi.org/10.3390/biomimetics10090610 - 10 Sep 2025
Viewed by 147
Abstract
One of the essential parts of contemporary cryptographic systems is s-boxes (Substitution Boxes), which give encryption algorithms more complexity and resilience due to their nonlinear structure. In this study, we propose CSBA (Cuckoo Search–Bees Algorithm), a hybrid evolutionary method that combines the strengths [...] Read more.
One of the essential parts of contemporary cryptographic systems is s-boxes (Substitution Boxes), which give encryption algorithms more complexity and resilience due to their nonlinear structure. In this study, we propose CSBA (Cuckoo Search–Bees Algorithm), a hybrid evolutionary method that combines the strengths of Cuckoo Search and Bees algorithms, to generate s-box structures with strong cryptographic properties. The initial population is generated with a high-diversity four-dimensional Memristive Lu chaotic map, taking advantage of the random yet deterministic nature of chaotic systems. This proposed method was designed with inspiration from biological systems. It was developed based on the foraging strategies of bees and the reproductive strategies of cuckoos. This nature-inspired structure enables an efficient scanning of the solution space. The resultant s-boxes’ fitness was assessed using the nonlinearity value. These s-boxes were then optimized using the hybrid CSBA algorithm suggested in this paper as well as the Bees algorithm. The performance of the proposed approaches was measured using SAC, nonlinearity, BIC-SAC, BIC-NL, maximum difference distribution, and linear uniformity (LU) metrics. Compared to other studies in the literature that used metaheuristic algorithms to generate s-boxes, the proposed approach demonstrates good performance. In particular, the average value of 109.75 obtained for the nonlinearity metric demonstrates high success. Therefore, this study demonstrates that robust and reliable s-boxes can be generated for symmetric encryption algorithms using the developed metaheuristic algorithms. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 3rd Edition)
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23 pages, 1377 KB  
Article
High-Value Patents Recognition with Random Forest and Enhanced Fire Hawk Optimization Algorithm
by Xiaona Yao, Huijia Li and Sili Wang
Biomimetics 2025, 10(9), 561; https://doi.org/10.3390/biomimetics10090561 - 23 Aug 2025
Viewed by 463
Abstract
High-value patents are a key indicator of new product development, the emergence of innovative technology, and a source of innovation incentives. Multiple studies have shown that patent value exhibits a significantly skewed distribution, with only about 10% of patents having high value. Identifying [...] Read more.
High-value patents are a key indicator of new product development, the emergence of innovative technology, and a source of innovation incentives. Multiple studies have shown that patent value exhibits a significantly skewed distribution, with only about 10% of patents having high value. Identifying high-value patents from a large volume of patent data in advance has become a crucial problem that needs to be addressed urgently. However, current machine learning methods often rely on manual hyperparameter tuning, which is time-consuming and prone to suboptimal results. Existing optimization algorithms also suffer from slow convergence and local optima issues, limiting their effectiveness on complex patent datasets. In this paper, machine learning and intelligent optimization algorithms are combined to process and analyze the patent data. The Fire Hawk Optimization Algorithm (FHO) is a novel intelligence algorithm suggested in recent years, inspired by the process in nature where Fire Hawks capture prey by setting fires. This paper firstly proposes the Enhanced Fire Hawk Optimizer (EFHO), which combines four strategies, namely adaptive tent chaotic mapping, hunting prey, adding the inertial weight, and enhanced flee strategy to address the weakness of FHO development. Benchmark tests demonstrate EFHO’s superior convergence speed, accuracy, and robustness across standard optimization benchmarks. As a representative real-world application, EFHO is employed to optimize Random Forest hyperparameters for high-value patent recognition. While other intelligent optimizers could be applied, EFHO effectively overcomes common issues like slow convergence and local optima trapping. Compared to other classification methods, the EFHO-optimized Random Forest achieves superior accuracy and classification stability. This study fills a research gap in effective hyperparameter tuning for patent recognition and demonstrates EFHO’s practical value on real-world patent datasets. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 3rd Edition)
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26 pages, 14849 KB  
Article
EAB-BES: A Global Optimization Approach for Efficient UAV Path Planning in High-Density Urban Environments
by Yunhui Zhang, Wenhong Xiao and Shihong Yin
Biomimetics 2025, 10(8), 499; https://doi.org/10.3390/biomimetics10080499 - 31 Jul 2025
Viewed by 488
Abstract
This paper presents a multi-strategy enhanced bald eagle search algorithm (EAB-BES) for 3D UAV path planning in urban environments. EAB-BES addresses key limitations of the traditional bald eagle search (BES) algorithm, including slow convergence, susceptibility to local optima, and poor adaptability in complex [...] Read more.
This paper presents a multi-strategy enhanced bald eagle search algorithm (EAB-BES) for 3D UAV path planning in urban environments. EAB-BES addresses key limitations of the traditional bald eagle search (BES) algorithm, including slow convergence, susceptibility to local optima, and poor adaptability in complex urban scenarios. The algorithm enhances solution space exploration through elite opposition-based learning, balances global search and local exploitation via an adaptive weight mechanism, and refines local search directions using block-based elite-guided differential mutation. These innovations significantly improve BES’s convergence speed, path accuracy, and adaptability to urban constraints. To validate its effectiveness, six high-density urban environments with varied obstacles were used for comparative experiments against nine advanced algorithms. The results demonstrate that EAB-BES achieves the fastest convergence speed and lowest stable fitness values and generates the shortest, smoothest collision-free 3D paths. Statistical tests and box plot analysis further confirm its superior performance in multiple performance metrics. EAB-BES has greater competitiveness compared with the comparative algorithms and can provide an efficient, reliable and robust solution for UAV autonomous navigation in complex urban environments. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 3rd Edition)
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21 pages, 2895 KB  
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 657
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 KB  
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 674
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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