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

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

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 18360

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Prof. Dr. Yongquan Zhou

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School of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China
Interests: bio-inspired computing; bionic optimization; computation intelligence; intelligence optimization; neural network
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Dr. Huajuan Huang

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College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China
Interests: bionic optimization; intelligence optimization; machine learning
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Dr. Guo Zhou

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Department of Science and Technology Teaching, China University of Political Science and Law, Beijing 102249, China
Interests: bionic optimization; intelligence optimization; graphical visualization
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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

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Published Papers (11 papers)

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Research

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19 pages, 4179 KiB

Open AccessArticle

Design and Experimental of the Soil Removal Device for Root-Soil Complex of Gentian Imitating the Percussion of Woodpeckers

by Hongguang Cui, Li Du, Zhanqiu Xie, Wei Zhong, Dehui Xu, Weiming Bian, Long Jiang, Tiejun Wang and Liyan Wu

Biomimetics 2024, 9(8), 479; https://doi.org/10.3390/biomimetics9080479 - 8 Aug 2024

Viewed by 775

Abstract

A soil removal device for the root-soil complex of Gentian imitating the percussion function of a woodpecker was designed to improve the soil removal efficiency of harvesting devices for rhizome-type traditional Chinese herbal medicines. Based on the physical parameters of roots and the root-soil complex of Gentian, the structure parameters of the striking arm and the actual profile of the cam are determined according to the physical parameters when the woodpecker knocks on the tree. The key parameters that affect the working performance of the soil removal device and their suitable value ranges have been identified through the impact test and analysis of the root-soil complex of Gentian. The mass of the striking hammer, the swing angle of the striking arm, and the rotation speed of the cam were taken as the experimental factors and the soil removal rate and the energy consumption per hammer percussion were taken as the experimental indicators. The ternary quadratic orthogonal regression combination experiment was carried out using Design-Expert. The regression model of the influence factors and evaluation indicators was established through the analysis of variance. The interaction effects of the influence factors on the indicators were analyzed using the response surface method. Using multiobjective optimization method, the optimal parameter combination was obtained as that of the mass of the striking hammer of 0.9 kg, the swing angle of the striking arm of 47°, and the rotation speed of the cam of 100 r/min, then the soil removal rate was the maximum and the energy consumption of single-hammer knocking was the minimum, with the values of 89.12% and 31.21 J, respectively. This study can provide a reference for the design and optimization of soil removal devices for rhizome-type traditional Chinese herbal medicines. Full article

(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 2nd Edition)

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17 pages, 8439 KiB

Open AccessArticle

A Study on the Design of Knee Exoskeleton Rehabilitation Based on the RFPBS Model

by Qiujian Xu, Junrui Li, Nan Jiang, Xinran Yuan, Siqi Liu, Dan Yang, Xiubo Ren, Xiaoyu Wang, Mingyi Yang, Yintong Liu and Peng Zhang

Biomimetics 2024, 9(7), 410; https://doi.org/10.3390/biomimetics9070410 - 5 Jul 2024

Viewed by 1414

Abstract

The gait rehabilitation knee exoskeleton is an advanced rehabilitative assistive device designed to help patients with knee joint dysfunction regain normal gait through training and activity support. This paper introduces a design framework based on the process knowledge representation method to optimize the design and control efficiency of the knee exoskeleton. This framework integrates knowledge of design objects and processes, specifically including requirements, functions, principle work areas, and the representation and multi-dimensional dynamic mapping of the Behavior–Structure (RFPBS) matrix, achieving multi-dimensional dynamic mapping of the knee exoskeleton. This method incorporates biomechanical and physiological knowledge from the rehabilitation process to more effectively simulate and support gait movements during rehabilitation. Research results indicate that the knee rehabilitation exoskeleton design, based on the RFPBS process knowledge representation model, accomplishes multi-dimensional dynamic mapping, providing a scientific basis and effective support for the rehabilitation of patients with knee joint dysfunction. Full article

(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 2nd Edition)

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15 pages, 950 KiB

Open AccessArticle

MOBCA: Multi-Objective Besiege and Conquer Algorithm

by Jianhua Jiang, Jiaqi Wu, Jinmeng Luo, Xi Yang and Zulu Huang

Biomimetics 2024, 9(6), 316; https://doi.org/10.3390/biomimetics9060316 - 24 May 2024

Cited by 2 | Viewed by 1323

Abstract

The besiege and conquer algorithm has shown excellent performance in single-objective optimization problems. However, there is no literature on the research of the BCA algorithm on multi-objective optimization problems. Therefore, this paper proposes a new multi-objective besiege and conquer algorithm to solve multi-objective optimization problems. The grid mechanism, archiving mechanism, and leader selection mechanism are integrated into the BCA to estimate the Pareto optimal solution and approach the Pareto optimal frontier. The proposed algorithm is tested with MOPSO, MOEA/D, and NSGAIII on the benchmark function IMOP and ZDT. The experiment results show that the proposed algorithm can obtain competitive results in terms of the accuracy of the Pareto optimal solution. Full article

(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 2nd Edition)

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18 pages, 7742 KiB

Open AccessArticle

Research on Economic Load Dispatch Problem of Microgrid Based on an Improved Pelican Optimization Algorithm

by Yi Zhang and Haoxue Li

Biomimetics 2024, 9(5), 277; https://doi.org/10.3390/biomimetics9050277 - 4 May 2024

Cited by 4 | Viewed by 1512

Abstract

This paper presents an improved pelican optimization algorithm (IPOA) to solve the economic load dispatch problem. The vertical crossover operator in the crisscross optimization algorithm is integrated to expand the diversity of the population in the local search phase. The optimal individual is also introduced to enhance its ability to guide the whole population and add disturbance factors to enhance its ability to jump out of the local optimal. The dimensional variation strategy is adopted to improve the optimal individual and speed up the algorithm’s convergence. The results of the IPOA showed that coal consumption was reduced by 0.0292%, 2.7273%, and 3.6739%, respectively, when tested on 10, 40, and 80-dimensional thermal power plant units compared to POA. The IPOA can significantly reduce the fuel cost of power plants. Full article

(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 2nd Edition)

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27 pages, 53631 KiB

Open AccessArticle

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

Cited by 2 | Viewed by 1533

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 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)

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20 pages, 14244 KiB

Open AccessArticle

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://doi.org/10.3390/biomimetics9040221 - 8 Apr 2024

Cited by 1 | Viewed by 1160

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 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

Open AccessArticle

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://doi.org/10.3390/biomimetics9020101 - 9 Feb 2024

Cited by 1 | Viewed by 1821

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 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

Open AccessArticle

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://doi.org/10.3390/biomimetics9010039 - 8 Jan 2024

Cited by 1 | Viewed by 2242

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 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

Open AccessArticle

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://doi.org/10.3390/biomimetics8080587 - 4 Dec 2023

Cited by 2 | Viewed by 1479

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, 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

Open AccessArticle

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://doi.org/10.3390/biomimetics8070550 - 17 Nov 2023

Cited by 3 | Viewed by 1730

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 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

Open AccessReview

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://doi.org/10.3390/biomimetics9030170 - 12 Mar 2024

Cited by 2 | Viewed by 2299

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 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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