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Biomimetics
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Biomimicry for Optimization, Control, and Automation: 2nd Edition
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Biomimicry for Optimization, Control, and Automation: 2nd Edition

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

Deadline for manuscript submissions: 15 May 2024 | Viewed by 7106

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 the submission 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

Published Papers (7 papers)

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Research

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27 pages, 3235 KiB  
Article
Dynamic 3D Point-Cloud-Driven Autonomous Hierarchical Path Planning for Quadruped Robots
by Qi Zhang, Ruiya Li, Jubiao Sun, Li Wei, Jun Huang and Yuegang Tan
Biomimetics 2024, 9(5), 259; https://doi.org/10.3390/biomimetics9050259 - 24 Apr 2024
Viewed by 260
Abstract
Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierarchical path planning approach driven by dynamic 3D point clouds is proposed in this article. The developed path planning model is essentially constituted of two layers: a global path planning [...] Read more.
Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierarchical path planning approach driven by dynamic 3D point clouds is proposed in this article. The developed path planning model is essentially constituted of two layers: a global path planning layer, and a local path planning layer. At the global path planning layer, a new method is proposed for calculating the terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision areas and a strategy for temporary target point selection are developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experimental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maximum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is shortened by 20%, and more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA). Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 2nd Edition)
20 pages, 14244 KiB  
Article
A Novel Obstacle Traversal Method for Multiple Robotic Fish Based on Cross-Modal Variational Autoencoders and Imitation Learning
by Ruilong Wang, Ming Wang, Qianchuan Zhao, Yanling Gong, Lingchen Zuo, Xuehan Zheng and He Gao
Biomimetics 2024, 9(4), 221; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics9040221 - 08 Apr 2024
Viewed by 446
Abstract
Precision control of multiple robotic fish visual navigation in complex underwater environments has long been a challenging issue in the field of underwater robotics. To address this problem, this paper proposes a multi-robot fish obstacle traversal technique based on the combination of cross-modal [...] Read more.
Precision control of multiple robotic fish visual navigation in complex underwater environments has long been a challenging issue in the field of underwater robotics. To address this problem, this paper proposes a multi-robot fish obstacle traversal technique based on the combination of cross-modal variational autoencoder (CM-VAE) and imitation learning. Firstly, the overall framework of the robotic fish control system is introduced, where the first-person view of the robotic fish is encoded into a low-dimensional latent space using CM-VAE, and then different latent features in the space are mapped to the velocity commands of the robotic fish through imitation learning. Finally, to validate the effectiveness of the proposed method, experiments are conducted on linear, S-shaped, and circular gate frame trajectories with both single and multiple robotic fish. Analysis reveals that the visual navigation method proposed in this paper can stably traverse various types of gate frame trajectories. Compared to end-to-end learning and purely unsupervised image reconstruction, the proposed control strategy demonstrates superior performance, offering a new solution for the intelligent navigation of robotic fish in complex environments. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 2nd Edition)
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18 pages, 2508 KiB  
Article
An Agent-Based Model to Reproduce the Boolean Logic Behaviour of Neuronal Self-Organised Communities through Pulse Delay Modulation and Generation of Logic Gates
by Luis Irastorza-Valera, José María Benítez, Francisco J. Montáns and Luis Saucedo-Mora
Biomimetics 2024, 9(2), 101; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics9020101 - 09 Feb 2024
Viewed by 1127
Abstract
The human brain is arguably the most complex “machine” to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain’s structure, functioning and adaptation. Mathematical approaches [...] Read more.
The human brain is arguably the most complex “machine” to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain’s structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain’s logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced—under pertinent simplifications—via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 2nd Edition)
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35 pages, 6114 KiB  
Article
Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning
by Tao Tian, Zhiwei Liang, Yuanfei Wei, Qifang Luo and Yongquan Zhou
Biomimetics 2024, 9(1), 39; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics9010039 - 08 Jan 2024
Viewed by 1261
Abstract
With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, this [...] Read more.
With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, this paper utilizes the Whale Optimization Algorithm (WOA) to address the problem, aiming to improve the solution accuracy. Whale optimization algorithm (WOA) is an algorithm that imitates whale foraging behavior, and the firefly algorithm (FA) is an algorithm that imitates firefly behavior. This paper proposes a hybrid firefly-whale optimization algorithm (FWOA) based on multi-population and opposite-based learning using the above algorithms. This algorithm can quickly find the optimal path in the complex mobile robot working environment and can balance exploitation and exploration. In order to verify the FWOA’s performance, 23 benchmark functions have been used to test the FWOA, and they are used to optimize the MRPP. The FWOA is compared with ten other classical metaheuristic algorithms. The results clearly highlight the remarkable performance of the Whale Optimization Algorithm (WOA) in terms of convergence speed and exploration capability, surpassing other algorithms. Consequently, when compared to the most advanced metaheuristic algorithm, FWOA proves to be a strong competitor. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 2nd Edition)
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29 pages, 2900 KiB  
Article
Solving the Combined Heat and Power Economic Dispatch Problem in Different Scale Systems Using the Imperialist Competitive Harris Hawks Optimization Algorithm
by Amir Nazari and Hamdi Abdi
Biomimetics 2023, 8(8), 587; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics8080587 - 04 Dec 2023
Viewed by 1042
Abstract
The aim of electrical load dispatch (ELD) is to achieve the optimal planning of different power plants to supply the required power at the minimum operation cost. Using the combined heat and power (CHP) units in modern power systems, increases energy efficiency and, [...] Read more.
The aim of electrical load dispatch (ELD) is to achieve the optimal planning of different power plants to supply the required power at the minimum operation cost. Using the combined heat and power (CHP) units in modern power systems, increases energy efficiency and, produce less environmental pollution than conventional units, by producing electricity and heat, simultaneously. Consequently, the ELD problem in the presence of CHP units becomes a very non-linear and non-convex complex problem called the combined heat and power economic dispatch (CHPED), which supplies both electric and thermal loads at the minimum operational cost. In this work, at first, a brief review of optimization algorithms, in different categories of classical, or conventional, stochastic search-based, and hybrid optimization techniques for solving the CHPED problem is presented. Then the CHPED problem in large-scale power systems is investigated by applying the imperialist competitive Harris hawks optimization (ICHHO), as the combination of imperialist competitive algorithm (ICA), and Harris hawks optimizer (HHO), for the first time, to overcome the shortcomings of using the ICA and HHO in the exploitation, and exploration phases, respectively, to solve this complex optimization problem. The effectiveness of the combined algorithm on four standard case studies, including 24 units as a medium-scale, 48, 84, units as the large-scale, and 96-unit as a very large-scale heat and power system, is detailed. The obtained results are compared to those of different algorithms to demonstrate the performance of the ICHHO algorithm in terms of better solution quality and lower fuel cost. The simulation studies verify that the proposed algorithm decreases the minimum operation costs by at least 0.1870%, 0.342%, 0.05224%, and 0.07875% compared to the best results in the literature. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 2nd Edition)
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22 pages, 899 KiB  
Article
PhyEffector, the First Algorithm That Identifies Classical and Non-Classical Effectors in Phytoplasmas
by Karla Gisel Carreón-Anguiano, Sara Elena Vila-Luna, Luis Sáenz-Carbonell and Blondy Canto-Canche
Biomimetics 2023, 8(7), 550; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics8070550 - 17 Nov 2023
Viewed by 1240
Abstract
Phytoplasmas are the causal agents of more than 100 plant diseases in economically important crops. Eleven genomes have been fully sequenced and have allowed us to gain a better understanding of the biology and evolution of phytoplasmas. Effectors are key players in pathogenicity [...] Read more.
Phytoplasmas are the causal agents of more than 100 plant diseases in economically important crops. Eleven genomes have been fully sequenced and have allowed us to gain a better understanding of the biology and evolution of phytoplasmas. Effectors are key players in pathogenicity and virulence, and their identification and description are becoming an essential practice in the description of phytoplasma genomes. This is of particular importance because effectors are possible candidates for the development of new strategies for the control of plant diseases. To date, the prediction of effectors in phytoplasmas has been a great challenge; the reliable comparison of effectoromes has been hindered because research teams have used the combination of different programs in their predictions. This is not trivial since significant differences in the results can arise, depending on the predictive pipeline used. Here, we tested different predictive pipelines to create the PhyEffector algorithm; the average value of the F1 score for PhyEffector was 0.9761 when applied to different databases or genomes, demonstrating its robustness as a predictive tool. PhyEffector can recover both classical and non-classical phytoplasma effectors, making it an invaluable tool to accelerate effectoromics in phytoplasmas. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 2nd Edition)
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Review

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29 pages, 5591 KiB  
Review
Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review
by Yuwen Wu, Zhisen Wang, Yuyi Chu, Renyuan Peng, Haoran Peng, Hongbo Yang, Kai Guo and Juzhong Zhang
Biomimetics 2024, 9(3), 170; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics9030170 - 12 Mar 2024
Viewed by 1147
Abstract
Malignant tumors have become one of the serious public health problems in human safety and health, among which the chest and abdomen diseases account for the largest proportion. Early diagnosis and treatment can effectively improve the survival rate of patients. However, respiratory motion [...] Read more.
Malignant tumors have become one of the serious public health problems in human safety and health, among which the chest and abdomen diseases account for the largest proportion. Early diagnosis and treatment can effectively improve the survival rate of patients. However, respiratory motion in the chest and abdomen can lead to uncertainty in the shape, volume, and location of the tumor, making treatment of the chest and abdomen difficult. Therefore, compensation for respiratory motion is very important in clinical treatment. The purpose of this review was to discuss the research and development of respiratory movement monitoring and prediction in thoracic and abdominal surgery, as well as introduce the current research status. The integration of modern respiratory motion compensation technology with advanced sensor detection technology, medical-image-guided therapy, and artificial intelligence technology is discussed and analyzed. The future research direction of intraoperative thoracic and abdominal respiratory motion compensation should be non-invasive, non-contact, use a low dose, and involve intelligent development. The complexity of the surgical environment, the constraints on the accuracy of existing image guidance devices, and the latency of data transmission are all present technical challenges. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 2nd Edition)
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