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Biomimetics
Special Issues
Bio-Inspired Design and Optimisation of Engineering Systems
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Bio-Inspired Design and Optimisation of Engineering Systems

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

Deadline for manuscript submissions: closed (1 February 2023) | Viewed by 13241

Special Issue Editor

Prof. Dr. Stuart Burgess

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Bristol, Bristol, UK
Interests: bimechanics; biomimetics; machine design; structural optimisation

Special Issue Information

Dear Colleagues,

There is a growing demand for extremely high levels of performance of engineering systems to meet the needs of industries, including the following sectors: energy; aerospace; transport; buildings; agriculture and medicine. Designers are needing to improve the performance of engineering systems in many areas including: compactness and miniaturisation; energy efficiency; structural efficiency; recycling efficiency; robustness and cost performance.

In nature, we observe very sophisticated design strategies such as multiscale and multiphasic materials and devices. We also observe compact multifunctioning devices that are highly optimised for several performance factors. These systems possess great potential for biomimicry.  

In this Special Issue of Bio-inspired Design and Optimisation of Engineering Systems, we welcome a wide range of research papers, from fundamental studies of systems optimisation to case studies of bio-inspired design optimisation. The goal of this Special Issue is to present and promote the valuable contributions of researchers and scientists across different disciplines to the development and applications of engineering systems, which will benefit the scientific community society at large.

Prof. Dr. Stuart Burgess
Guest Editor

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

  • bio-inspired design
  • systems optimisation
  • multi-objective optimisation
  • multi-function design
  • multiscale design
  • energy efficiency
  • structural efficiency

Published Papers (7 papers)

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Research

14 pages, 5131 KiB  
Article
A TEC Cooling Soft Robot Driven by Twisted String Actuators
by Shun Zhao, Xuewei Lu, Kunyang Wang, Di Zhao, Xu Wang, Lei Ren and Luquan Ren
Biomimetics 2023, 8(2), 221; https://doi.org/10.3390/biomimetics8020221 - 25 May 2023
Viewed by 1448
Abstract
Similar to biological muscles in nature, artificial muscles have unique advantages for driving bionic robots. However, there is still a large gap between the performance of existing artificial muscles and biological muscles. Twisted polymer actuators (TPAs) convert rotary motion from torsional to linear [...] Read more.
Similar to biological muscles in nature, artificial muscles have unique advantages for driving bionic robots. However, there is still a large gap between the performance of existing artificial muscles and biological muscles. Twisted polymer actuators (TPAs) convert rotary motion from torsional to linear motion. TPAs are known for their high energy efficiency and large linear strain and stress outputs. A simple, lightweight, low-cost, self-sensing robot powered using a TPA and cooled using a thermoelectric cooler (TEC) was proposed in this study. Because TPA burns easily at high temperatures, traditional soft robots driven by TPAs have low movement frequencies. In this study, a temperature sensor and TEC were combined to develop a closed-loop temperature control system to ensure that the internal temperature of the robot was 5 °C to cool the TPAs quickly. The robot could move at a frequency of 1 Hz. Moreover, a self-sensing soft robot was proposed based on the TPA contraction length and resistance. When the motion frequency was 0.01 Hz, the TPA had good self-sensing ability and the root-mean-square error of the angle of the soft robot was less than 3.89% of the measurement amplitude. This study not only proposed a new cooling method for improving the motion frequency of soft robots but also verified the autokinetic performance of the TPAs. Full article
(This article belongs to the Special Issue Bio-Inspired Design and Optimisation of Engineering Systems)
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22 pages, 9981 KiB  
Article
On the Mechanical Behaviour of Biomimetic Cornstalk-Inspired Lightweight Structures
by Shakib Hyder Siddique, Paul J. Hazell, Gerald G. Pereira, Hongxu Wang, Juan P. Escobedo and Ali A. H. Ameri
Biomimetics 2023, 8(1), 92; https://doi.org/10.3390/biomimetics8010092 - 24 Feb 2023
Cited by 2 | Viewed by 1696
Abstract
This paper presents an investigation on the stiffness and energy absorption capabilities of three proposed biomimetic structures based on the internal architecture of a cornstalk. 3D printing was used to manufacture specimens using a tough and impact-resistant thermoplastic material, acrylonitrile butadiene styrene (ABS). [...] Read more.
This paper presents an investigation on the stiffness and energy absorption capabilities of three proposed biomimetic structures based on the internal architecture of a cornstalk. 3D printing was used to manufacture specimens using a tough and impact-resistant thermoplastic material, acrylonitrile butadiene styrene (ABS). The structural stiffness, maximum stress, densification strain, and energy absorption were extracted from the compression tests performed at a strain rate of 10−3 s−1. A numerical model was developed to analyse the behaviour of the biomimetic structures under compression loading. Further, a damage examination was conducted through optical microscopy and profilometry. The results showed that the cornstalk-inspired biomimetic structure exhibited a superior specific energy absorption (SEA) capability that was three times higher than that of the other core designs as reported in the literature. Full article
(This article belongs to the Special Issue Bio-Inspired Design and Optimisation of Engineering Systems)
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21 pages, 4428 KiB  
Article
Coverage Optimization of Heterogeneous Wireless Sensor Network Based on Improved Wild Horse Optimizer
by Chuijie Zeng, Tao Qin, Wei Tan, Chuan Lin, Zhaoqiang Zhu, Jing Yang and Shangwei Yuan
Biomimetics 2023, 8(1), 70; https://doi.org/10.3390/biomimetics8010070 - 6 Feb 2023
Cited by 12 | Viewed by 1516
Abstract
One of the most important challenges for heterogeneous wireless sensor networks (HWSNs) is adequate network coverage and connectivity. Aiming at this problem, this paper proposes an improved wild horse optimizer algorithm (IWHO). Firstly, the population’s variety is increased by using the SPM chaotic [...] Read more.
One of the most important challenges for heterogeneous wireless sensor networks (HWSNs) is adequate network coverage and connectivity. Aiming at this problem, this paper proposes an improved wild horse optimizer algorithm (IWHO). Firstly, the population’s variety is increased by using the SPM chaotic mapping at initialization; secondly, the WHO and Golden Sine Algorithm (Golden-SA) are hybridized to improve the WHO’s accuracy and arrive at faster convergence; Thirdly, the IWHO can escape from a local optimum and broaden the search space by using opposition-based learning and the Cauchy variation strategy. The results indicate that the IWHO has the best capacity for optimization by contrasting the simulation tests with seven algorithms on 23 test functions. Finally, three sets of coverage optimization experiments in different simulated environments are designed to test the effectiveness of this algorithm. The validation results demonstrate that the IWHO can achieve better and more effective sensor connectivity and coverage ratio compared to that of several algorithms. After optimization, the HWSN’s coverage and connectivity ratio attained 98.51% and 20.04%, and after adding obstacles, 97.79% and 17.44%, respectively. Full article
(This article belongs to the Special Issue Bio-Inspired Design and Optimisation of Engineering Systems)
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11 pages, 2797 KiB  
Article
Development of a Bionic Tube with High Bending-Stiffness Properties Based on Human Tibiofibular Shapes
by Jianqiao Jin, Kunyang Wang, Lei Ren, Zhihui Qian, Xuewei Lu, Wei Liang, Xiaohan Xu, Shun Zhao, Di Zhao, Xu Wang and Luquan Ren
Biomimetics 2023, 8(1), 18; https://doi.org/10.3390/biomimetics8010018 - 3 Jan 2023
Cited by 2 | Viewed by 1290
Abstract
The human tibiofibular complex has undergone a long evolutionary process, giving its structure a high bearing-capacity. The distinct tibiofibular shape can be used in engineering to acquire excellent mechanical properties. In this paper, four types of bionic tubes were designed by extracting the [...] Read more.
The human tibiofibular complex has undergone a long evolutionary process, giving its structure a high bearing-capacity. The distinct tibiofibular shape can be used in engineering to acquire excellent mechanical properties. In this paper, four types of bionic tubes were designed by extracting the dimensions of different cross-sections of human tibia–fibula. They had the same outer profiles, but different inner shapes. The concept of specific stiffness was introduced to evaluate the mechanical properties of the four tubes. Finite-element simulations and physical bending-tests using a universal testing machine were conducted, to compare their mechanical properties. The simulations showed that the type 2 bionic tube, i.e., the one closest to the human counterpart, obtained the largest specific-stiffness (ε = 6.46 × 104), followed by the type 4 (ε = 6.40 × 104) and the type 1 (ε = 6.39 × 104). The type 3 had the largest mass but the least stiffness (ε = 6.07 × 104). The specific stiffness of the type 2 bionic tube increased by approximately 25.8%, compared with that of the type 3. The physical tests depicted similar findings. This demonstrates that the bionic tube inspired by the human tibiofibular shape has excellent effectiveness and bending properties, and could be used in the fields of healthcare engineering, such as robotics and prosthetics. Full article
(This article belongs to the Special Issue Bio-Inspired Design and Optimisation of Engineering Systems)
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32 pages, 10712 KiB  
Article
An Improved Chimp-Inspired Optimization Algorithm for Large-Scale Spherical Vehicle Routing Problem with Time Windows
by Yifei Xiang, Yongquan Zhou, Huajuan Huang and Qifang Luo
Biomimetics 2022, 7(4), 241; https://doi.org/10.3390/biomimetics7040241 - 15 Dec 2022
Cited by 6 | Viewed by 1617
Abstract
The vehicle routing problem with time windows (VRPTW) is a classical optimization problem. There have been many related studies in recent years. At present, many studies have generally analyzed this problem on the two-dimensional plane, and few studies have explored it on spherical [...] Read more.
The vehicle routing problem with time windows (VRPTW) is a classical optimization problem. There have been many related studies in recent years. At present, many studies have generally analyzed this problem on the two-dimensional plane, and few studies have explored it on spherical surfaces. In order to carry out research related to the distribution of goods by unmanned vehicles and unmanned aerial vehicles, this study carries out research based on the situation of a three-dimensional sphere and proposes a three-dimensional spherical VRPTW model. All of the customer nodes in this problem were mapped to the three-dimensional sphere. The chimp optimization algorithm is an excellent intelligent optimization algorithm proposed recently, which has been successfully applied to solve various practical problems and has achieved good results. The chimp optimization algorithm (ChOA) is characterized by its excellent ability to balance exploration and exploitation in the optimization process so that the algorithm can search the solution space adaptively, which is closely related to its outstanding adaptive factors. However, the performance of the chimp optimization algorithm in solving discrete optimization problems still needs to be improved. Firstly, the convergence speed of the algorithm is fast at first, but it becomes slower and slower as the number of iterations increases. Therefore, this paper introduces the multiple-population strategy, genetic operators, and local search methods into the algorithm to improve its overall exploration ability and convergence speed so that the algorithm can quickly find solutions with higher accuracy. Secondly, the algorithm is not suitable for discrete problems. In conclusion, this paper proposes an improved chimp optimization algorithm (MG-ChOA) and applies it to solve the spherical VRPTW model. Finally, this paper analyzes the performance of this algorithm in a multi-dimensional way by comparing it with many excellent algorithms available at present. The experimental result shows that the proposed algorithm is effective and superior in solving the discrete problem of spherical VRPTW, and its performance is superior to that of other algorithms. Full article
(This article belongs to the Special Issue Bio-Inspired Design and Optimisation of Engineering Systems)
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47 pages, 7439 KiB  
Article
Serval Optimization Algorithm: A New Bio-Inspired Approach for Solving Optimization Problems
by Mohammad Dehghani and Pavel Trojovský
Biomimetics 2022, 7(4), 204; https://doi.org/10.3390/biomimetics7040204 - 20 Nov 2022
Cited by 14 | Viewed by 2652
Abstract
This article introduces a new metaheuristic algorithm called the Serval Optimization Algorithm (SOA), which imitates the natural behavior of serval in nature. The fundamental inspiration of SOA is the serval’s hunting strategy, which attacks the selected prey and then hunts the prey in [...] Read more.
This article introduces a new metaheuristic algorithm called the Serval Optimization Algorithm (SOA), which imitates the natural behavior of serval in nature. The fundamental inspiration of SOA is the serval’s hunting strategy, which attacks the selected prey and then hunts the prey in a chasing process. The steps of SOA implementation in two phases of exploration and exploitation are mathematically modeled. The capability of SOA in solving optimization problems is challenged in the optimization of thirty-nine standard benchmark functions from the CEC 2017 test suite and CEC 2019 test suite. The proposed SOA approach is compared with the performance of twelve well-known metaheuristic algorithms to evaluate further. The optimization results show that the proposed SOA approach, due to the appropriate balancing exploration and exploitation, is provided better solutions for most of the mentioned benchmark functions and has superior performance compared to competing algorithms. SOA implementation on the CEC 2011 test suite and four engineering design challenges shows the high efficiency of the proposed approach in handling real-world optimization applications. Full article
(This article belongs to the Special Issue Bio-Inspired Design and Optimisation of Engineering Systems)
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16 pages, 5652 KiB  
Article
Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing
by Yong Li, Jinxing Li, Yu Sun and Haisheng Li
Biomimetics 2022, 7(4), 168; https://doi.org/10.3390/biomimetics7040168 - 17 Oct 2022
Cited by 1 | Viewed by 1896
Abstract
With the wide application of computational fluid dynamics in various fields and the continuous growth of the complexity of the problem and the scale of the computational grid, large-scale parallel computing came into being and became an indispensable means to solve this problem. [...] Read more.
With the wide application of computational fluid dynamics in various fields and the continuous growth of the complexity of the problem and the scale of the computational grid, large-scale parallel computing came into being and became an indispensable means to solve this problem. In the numerical simulation of multi-block grids, the mapping strategy from grid block to processor is an important factor affecting the efficiency of load balancing and communication overhead. The multi-level graph partitioning algorithm is an important algorithm that introduces graph network dynamic programming to solve the load-balancing problem. This paper proposed a firefly-ant compound optimization (FaCO) algorithm for the weighted fusion of two optimization rules of the firefly and ant colony algorithm. For the graph, results after multi-level graph partitioning are transformed into a traveling salesman problem (TSP). This algorithm is used to optimize the load distribution of the solution, and finally, the rough graph segmentation is projected to obtain the most original segmentation optimization results. Although firefly algorithm (FA) and ant colony optimization (ACO), as swarm intelligence algorithms, are widely used to solve TSP problems, for the problems for which swarm intelligence algorithms easily fall into local optimization and low search accuracy, the improvement of the FaCO algorithm adjusts the weight of iterative location selection and updates the location. Experimental results on publicly available datasets such as the Oliver30 dataset and the eil51 dataset demonstrated the effectiveness of the FaCO algorithm. It is also significantly better than the commonly used firefly algorithm and other algorithms in terms of the search results and efficiency and achieves better results in optimizing the load-balancing problem of parallel computing. Full article
(This article belongs to the Special Issue Bio-Inspired Design and Optimisation of Engineering Systems)
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