Journal Description
Biomimetics
Biomimetics
is an international, peer-reviewed, open access journal on biomimicry and bionics, published monthly online by MDPI. The International Society of Bionic Engineering (ISBE) is affiliated with Biomimetics.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, Ei Compendex, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q1 (Engineering, Multidisciplinary) / CiteScore - Q2 (Biomedical Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.1 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.9 (2024);
5-Year Impact Factor:
4.0 (2024)
Latest Articles
An Improved Elk Herd Optimization Algorithm for Maximum Power Point Tracking in Photovoltaic Systems Under Partial Shading Conditions
Biomimetics 2025, 10(8), 533; https://doi.org/10.3390/biomimetics10080533 - 13 Aug 2025
Abstract
In partial shading conditions (PSCs), the power–voltage characteristics of photovoltaic systems exhibit multiple peaks, causing traditional maximum power point tracking (MPPT) algorithms to easily become trapped in local optima and fail to achieve global maximum power point tracking, thereby reducing energy conversion efficiency.
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In partial shading conditions (PSCs), the power–voltage characteristics of photovoltaic systems exhibit multiple peaks, causing traditional maximum power point tracking (MPPT) algorithms to easily become trapped in local optima and fail to achieve global maximum power point tracking, thereby reducing energy conversion efficiency. Effectively and rapidly locating the global maximum power under complex environmental conditions has become crucial for enhancing MPPT performance in photovoltaic systems. This paper therefore proposes an improved elk herd optimization (IEHO) algorithm to achieve the rapid tracking of the global maximum power point under various weather conditions. The algorithm proposes a position update mechanism guided by the predation risk probability to direct elk herd migration and introduces the triangle walk strategy, thereby enhancing the algorithm’s capability to avoid local optima. Furthermore, IEHO employs a memory-guided redirection strategy to skip redundant calculations of historical duty cycles, significantly improving the convergence speed of MPPT. To validate the algorithm’s performance advantages, the proposed IEHO method is compared with other recognized meta-heuristic algorithms under various weather conditions. The experimental results demonstrate that, across all tested conditions, the proposed IEHO method achieves an average tracking efficiency of 99.99% and an average tracking time of 0.3886 s, outperforming other comparative algorithms.
Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)
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Open AccessArticle
Design and Experimental Validation of a Multimodal Snake Robot with Elliptical Wheels
by
Xuan Xiao, Zizhu Zhao, Lianzhi Qi, Michael Albert Sumantri, Hengwei Liu, Jianqin Li, Keyang Zheng and Jianming Wang
Biomimetics 2025, 10(8), 532; https://doi.org/10.3390/biomimetics10080532 - 13 Aug 2025
Abstract
Snake robots are characterized by their flexibility and environmental adaptability, achieved through various optimized gaits. However, their forward propulsion still requires improvement. This challenge can be addressed by integrating wheels or legs, but these mechanisms often limit the ability of snake robots to
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Snake robots are characterized by their flexibility and environmental adaptability, achieved through various optimized gaits. However, their forward propulsion still requires improvement. This challenge can be addressed by integrating wheels or legs, but these mechanisms often limit the ability of snake robots to perform most optimized gaits. In this article, we develop a novel multimodal snake robot, JiAo-II, with both body-based locomotion and wheeled locomotion to handle complex terrains. The mechanical design and implementation of JiAo-II are presented in detail, with particular emphasis on its innovative elliptical wheels and gear transmission mechanism. Experimental results validate the effectiveness and multifunctionality of JiAo-II across various scenarios, including traversing grasslands, crossing gaps, ascending slopes, navigating pipelines, and climbing cylindrical surfaces. Furthermore, a series of experiments are conducted to evaluate the performance of the wheel–body coordinated locomotion on uneven ground, demonstrating the robustness even without requiring external sensing or sophisticated control strategies. In summary, the proposed multimodal mechanism significantly enhances the locomotion speed, terrain adaptability and robustness of snake robots.
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(This article belongs to the Special Issue Biomimetic Robot Motion Control)
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Open AccessReview
Skin-Inspired Healthcare Electronics
by
Saite Li, Qiaosheng Xu, Yukai Zhou, Zhengdao Chu, Lulu Li, Xidi Sun, Fengchang Huang, Fei Wang, Cai Chen, Xin Guo, Jiean Li, Wen Cheng and Lijia Pan
Biomimetics 2025, 10(8), 531; https://doi.org/10.3390/biomimetics10080531 - 13 Aug 2025
Abstract
With the improvement in living standards and the aging of the population, the development of thin, light, and unobtrusive electronic skin devices is accelerating. These electronic devices combine the convenience of wearable electronics with the comfort of a skin-like fit. They are used
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With the improvement in living standards and the aging of the population, the development of thin, light, and unobtrusive electronic skin devices is accelerating. These electronic devices combine the convenience of wearable electronics with the comfort of a skin-like fit. They are used to acquire multimodal physiological signal data from the wearer and real-time transmission of signals for vital signs monitoring, health dynamics warning, and disease prevention. These capabilities impose unique requirements on material selection, signal transmission, and data processing for such electronic devices. Firstly, this review provides a systematic introduction to nanomaterials, conductive hydrogels, and liquid metals, which are currently used in human health monitoring. Then, it introduces the solution to the contradiction between wireless data transmission and flexible electronic skin devices. Then, the latest data processing progress is briefly described. Finally, the latest research advances in electronic skin devices based on medical scenarios are presented, and their current development, challenges faced, and future opportunities in the field of vital signs monitoring are discussed.
Full article
(This article belongs to the Special Issue Recent Advances in Wearable Bioelectronics in Healthcare/Medical Devices)
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Open AccessArticle
Outcomes of Regenerative Endodontic Therapy Using Dehydrated Human-Derived Amnion–Chorion Membranes and Collagen Matrices: A Retrospective Analysis
by
Anjali K. Dave, Julia Y. Cheung and Sahng G. Kim
Biomimetics 2025, 10(8), 530; https://doi.org/10.3390/biomimetics10080530 - 13 Aug 2025
Abstract
Dehydrated human-derived amnion–chorion membranes (ACM), known for their bioactive composition of growth factors and cytokines, have demonstrated potential as a bioactive scaffold in regenerative medicine; however, their clinical application in regenerative endodontic procedures (REPs) remains unexplored. This retrospective study aimed to evaluate the
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Dehydrated human-derived amnion–chorion membranes (ACM), known for their bioactive composition of growth factors and cytokines, have demonstrated potential as a bioactive scaffold in regenerative medicine; however, their clinical application in regenerative endodontic procedures (REPs) remains unexplored. This retrospective study aimed to evaluate the clinical and radiographic outcomes of REPs using ACM compared to collagen matrices (CM) in immature necrotic permanent teeth. Forty-one immature necrotic teeth from 38 patients (mean age: 14.68 ± 7.43 years) were treated with REPs using either ACM (n = 21) or CM (n = 20) scaffolds over a mean follow-up period of 23.23 months. Outcomes assessed included survival, success, root development measured by radiographic root area (RRA), and pulp sensibility. Independent t-tests compared outcomes between groups, while Cox regression and generalized linear models identified predictors of treatment outcomes. Overall survival and success rates were 87.8% and 82.9%, respectively. ACM-treated teeth achieved 90.5% survival and 85.7% success rates, while CM-treated teeth demonstrated 85.0% survival and 80.0% success rates, with no statistically significant differences between groups (p > 0.05). Root development occurred in 85.4% of cases overall, with significant RRA increases of 13.89 ± 13.95% for ACM and 11.24 ± 11.21% for CM (p < 0.05 within each group). Pulp sensibility recovery was observed in 51.2% of treated teeth overall, with 42.9% for ACM-treated teeth and 55.0% for CM-treated teeth (p > 0.05). Notably, ACM-treated teeth demonstrated earlier sensibility recovery compared to those of CM-treated teeth. Age was identified as a significant negative predictor of root development outcomes (p < 0.05). This clinical study demonstrates that both ACM and CM are clinically effective scaffolds for REPs, achieving high survival rates and promoting root development in immature necrotic teeth. While overall success rates were comparable, ACM showed faster sensibility recovery, suggesting potential biological advantages for enhanced tissue regeneration and earlier functional recovery.
Full article
(This article belongs to the Special Issue Biomimicry and Functional Materials: 5th Edition)
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Open AccessArticle
CCESC: A Crisscross-Enhanced Escape Algorithm for Global and Reservoir Production Optimization
by
Youdao Zhao and Xiangdong Li
Biomimetics 2025, 10(8), 529; https://doi.org/10.3390/biomimetics10080529 - 12 Aug 2025
Abstract
Global optimization problems, ubiquitous scientific research, and engineering applications necessitate sophisticated algorithms adept at navigating intricate, high-dimensional search landscapes. The Escape (ESC) algorithm, inspired by the complex dynamics of crowd evacuation behavior—where individuals exhibit calm, herding, or panic responses—offers a compelling nature-inspired paradigm
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Global optimization problems, ubiquitous scientific research, and engineering applications necessitate sophisticated algorithms adept at navigating intricate, high-dimensional search landscapes. The Escape (ESC) algorithm, inspired by the complex dynamics of crowd evacuation behavior—where individuals exhibit calm, herding, or panic responses—offers a compelling nature-inspired paradigm for addressing these challenges. While ESC demonstrates a strong intrinsic balance between exploration and exploitation, opportunities exist to enhance its inter-agent communication and search trajectory diversification. This paper introduces an advanced bio-inspired algorithm, termed Crisscross Escape Algorithm (CCESC), which strategically incorporates a Crisscross (CC) information exchange mechanism. This CC strategy, by promoting multi-directional interaction and information sharing among individuals irrespective of their behavioral group (calm, herding, panic), fosters a richer exploration of the solution space, helps to circumvent local optima, and accelerates convergence towards superior solutions. The CCESC’s performance is extensively validated on the demanding CEC2017 benchmark suites, alongside several standard engineering design problems, and compared against a comprehensive set of prominent metaheuristic algorithms. Experimental results consistently reveal CCESC’s superior or highly competitive performance across a wide array of benchmark functions. Furthermore, CCESC is effectively applied to a complex reservoir production optimization problem, demonstrating its capacity to achieve significantly improved Net Present Value (NPV) over other established methods. This successful application underscores CCESC’s robustness and efficacy as a powerful optimization tool for tackling multifaceted real-world problems, particularly in reservoir production optimization within complex sedimentary environments.
Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)
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Open AccessArticle
Bio-Inspired Design of Mechanical Properties of Hybrid Topological Cellular Honeycomb Structures
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Yingqiu Sun, Fan Guo and Yangyang Liu
Biomimetics 2025, 10(8), 528; https://doi.org/10.3390/biomimetics10080528 - 12 Aug 2025
Abstract
Inspired by the evolutionary optimization of biological load-bearing systems, honeycomb structures are highly valued in applications involving impact protection and lightweight load-bearing due to their outstanding mechanical properties. This study introduces an interesting honeycomb structure known as the hybrid topological cellular honeycomb structure
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Inspired by the evolutionary optimization of biological load-bearing systems, honeycomb structures are highly valued in applications involving impact protection and lightweight load-bearing due to their outstanding mechanical properties. This study introduces an interesting honeycomb structure known as the hybrid topological cellular honeycomb structure (HTCHS), which integrates four distinctive topological cells. To effectively fabricate HTCHS samples, the research utilized a fused deposition modeling (FDM) process, employing polyethylene terephthalate glycol-modified (PETG) as the matrix material, successfully producing the HTCHS samples. A finite element simulation model for the HTCHS is created using LS-DYNA software(LS-DYNA R11.1.0 software), and its accuracy is confirmed through a comparative analysis of experimental and simulation results. The influence of the topological cell parameters (T1 to T4) on compressive energy absorption, specific energy absorption, and peak crushing force through parametric modeling is investigated. The mechanical properties of honeycomb structures vary depending on the cell parameters at different positions, and monotonically increasing the design parameters does not improve the energy absorption capacity of the HTCHS. To enhance the mechanical performance of the HTCHS, the initial periodic cell configurations are transformed into non-periodic designs. A discrete optimization design framework for local parameters of the HTCHS is established, integrating cell coding with the MOPSO algorithm. The feasibility of the optimization results is validated through experimental data, demonstrating that this study offers an effective technical solution for developing a novel generation of cellular honeycomb structures with customizable mechanical properties.
Full article
(This article belongs to the Special Issue Computer-Aided Biomimetics: 3rd Edition)
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Open AccessArticle
Real-Time Cascaded State Estimation Framework on Lie Groups for Legged Robots Using Proprioception
by
Botao Liu, Fei Meng, Zhihao Zhang, Maosen Wang, Tianqi Wang, Xuechao Chen and Zhangguo Yu
Biomimetics 2025, 10(8), 527; https://doi.org/10.3390/biomimetics10080527 - 12 Aug 2025
Abstract
This paper proposes a cascaded state estimation framework based on proprioception for robots. A generalized-momentum-based Kalman filter (GMKF) estimates the ground reaction forces at the feet through joint torques, which are then input into an error-state Kalman filter (ESKF) to obtain the robot’s
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This paper proposes a cascaded state estimation framework based on proprioception for robots. A generalized-momentum-based Kalman filter (GMKF) estimates the ground reaction forces at the feet through joint torques, which are then input into an error-state Kalman filter (ESKF) to obtain the robot’s prior state estimate. The system’s dynamic equations on the Lie group are parameterized using canonical coordinates of the first kind, and variations in the tangent space are mapped to the Lie algebra via the inverse of the right trivialization. The resulting parameterized system state equations, combined with the prior estimates and a sliding window, are formulated as a moving horizon estimation (MHE) problem, which is ultimately solved using a parallel real-time iteration (Para-RTI) technique. The proposed framework operates on manifolds, providing a tightly coupled estimation with higher accuracy and real-time performance, and is better suited to handle the impact noise during foot–ground contact in legged robots. Experiments were conducted on the BQR3 robot, and comparisons with measurements from a Vicon motion capture system validate the superiority and effectiveness of the proposed method.
Full article
(This article belongs to the Section Locomotion and Bioinspired Robotics)
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Open AccessArticle
A Transfer Function-Based Binary Version of Improved Pied Kingfisher Optimizer for Solving the Uncapacitated Facility Location Problem
by
Ayşe Beşkirli
Biomimetics 2025, 10(8), 526; https://doi.org/10.3390/biomimetics10080526 - 12 Aug 2025
Abstract
In this study, the pied kingfisher optimizer (PKO) algorithm is adapted to the uncapacitated facility location problem (UFLP), and its performance is evaluated. The PKO algorithm is binarized with fourteen different transfer functions (TF), and each variant is tested on a total of
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In this study, the pied kingfisher optimizer (PKO) algorithm is adapted to the uncapacitated facility location problem (UFLP), and its performance is evaluated. The PKO algorithm is binarized with fourteen different transfer functions (TF), and each variant is tested on a total of fifteen different Cap problems. In addition, performance improvement was realized by adding the Levy flight strategy to BinPKO, and this improved method was named BinIPKO. The experimental results show that the TF1 transfer function for BinIPKO performs very well on all problems in terms of both best and mean solution values. The TF2 transfer function performed efficiently on most Cap problems, ranking second only to TF1. Although the other transfer functions provided competitive solutions in some Cap problems, they lagged behind TF1 and TF2 in terms of overall performance. In addition, the performance of BinIPKO was also compared with the well-known PSO and GWO algorithms in the literature, as well as the recently proposed APO and EEFO algorithms, and it was found that BinIPKO performs well overall. In line with this information, it is seen that the IPKO algorithm, especially when used with the TF1 transfer function, provides an effective alternative for UFLP.
Full article
(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)
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Open AccessArticle
Impact Resistance Behaviors of Carbon Fiber Fabric Reinforced Composite Laminates with Bio-Inspired Helicoidal Layups
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Lizhen Du, Jiaqi Tang, Zisheng Wang, Jiacheng Zhou, Xiaoshuang Xiong, Xiang Li and Mingzhang Chen
Biomimetics 2025, 10(8), 525; https://doi.org/10.3390/biomimetics10080525 - 11 Aug 2025
Abstract
Carbon fiber fabric reinforced composite laminates are widely used in the automotive and aerospace components, which are prone to suffering low velocity impacts. In this paper, helicoidal layups of fabrics inspired by the Bouligand type structure of the dactyl clubs of mantis shrimp
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Carbon fiber fabric reinforced composite laminates are widely used in the automotive and aerospace components, which are prone to suffering low velocity impacts. In this paper, helicoidal layups of fabrics inspired by the Bouligand type structure of the dactyl clubs of mantis shrimp are proposed to improve the impact resistance of carbon fiber fabric reinforced composite laminates. Low velocity impact tests and finite element simulation are carried out to investigate the effect of the rotation angle of helicoidal layups on the impact damage behaviors of composite laminates, including impact force response, energy absorption characteristics and damage mechanism. Results show that the simulation results of impact force–time response, absorbed energy–time response, and damage characteristics show good agreements with the experimental results. With the increase in impact energy, the maximum value of impact force, the absorbed energy and the energy absorption ratio for all specimens are all increased. Under all impact energies, the impact damage of specimens with helicoidal layups are lower than that of specimen QI1 (rotation angle of 0°), indicating that the helical layup of woven carbon fabric can sufficiently enhance the impact resistance of the composite material. Furthermore, the impact resistance of specimen HL2 (rotation angle of 12.8°) is the best, because it demonstrates the lowest impact damage and highest impact force under all energies. This work provides a bionic design guideline for the high impact performance of carbon fiber fabric reinforced composite laminate.
Full article
(This article belongs to the Special Issue Bionic Engineering for Boosting Multidisciplinary Integration: 2nd Edition)
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Open AccessSystematic Review
State-of-the-Art Organ-on-Chip Models and Designs for Medical Applications: A Systematic Review
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Gustavo Adolfo Marcelino de Almeida Nunes, Ana Karoline Almeida da Silva, Rafael Mendes Faria, Klériston Silva Santos, Arthur da Costa Aguiar, Lindemberg Barreto Mota da Costa, Glécia Virgolino da Silva Luz, Marcella Lemos Brettas Carneiro, Mário Fabrício Fleury Rosa, Graziella Anselmo Joanitti, Karoany Maria Ibiapina, Ana Karen Gonçalves de Barros Gomes, Adson Ferreira da Rocha and Suélia de Siqueira Rodrigues Fleury Rosa
Biomimetics 2025, 10(8), 524; https://doi.org/10.3390/biomimetics10080524 - 11 Aug 2025
Abstract
Organ-on-a-chip (OoC) devices simulate human organs within a microenvironment, potentially surpassing traditional preclinical methods and paving the way for innovative treatments. A thorough understanding of the current state of OoC design enables the development of more precise and relevant models that replicate not
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Organ-on-a-chip (OoC) devices simulate human organs within a microenvironment, potentially surpassing traditional preclinical methods and paving the way for innovative treatments. A thorough understanding of the current state of OoC design enables the development of more precise and relevant models that replicate not only the structure of organs but also their intricate cellular interactions and responses to external stimuli. This knowledge facilitates the optimization of biomimetic materials and allows for the real-time simulation of physiological microenvironments. By keeping abreast of new microfabrication techniques, we can explore opportunities to create customized and highly functional OoCs. Objective: To provide a comprehensive overview of microphysiological platform designs. Methods: This systematic review was registered in PROSPERO under the number CRD42022352569. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The eligibility criteria included studies utilizing human tissue, either primary or secondary lineage cells. Results: A total of 9.790 papers were retrieved from the Scopus, Embase, IEEE and Web of Science databases. After removing duplicates and applying a 10-year publication filter, 3.150 articles were screened by title and abstract. Full-text analyses were then performed. Eighteen studies met the eligibility criteria and were included in this systematic review. In this review, we examine the principles of OoC design, focusing on structure, dimensions, cell culturing options and manufacturing techniques. We also examine recent advances and future prospects in the field. Conclusions: Microphysiological devices in health research can facilitate drug discovery and improve our understanding of human physiology. They contribute to more ethical research by reducing the number of animals used in experiments.
Full article
(This article belongs to the Section Biomimetic Design, Constructions and Devices)
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Open AccessArticle
A Hybrid Ensemble Equilibrium Optimizer Gene Selection Algorithm for Microarray Data
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Peng Su, Yuxin Zhao, Xiaobo Li, Zhendi Ma and Hui Wang
Biomimetics 2025, 10(8), 523; https://doi.org/10.3390/biomimetics10080523 - 10 Aug 2025
Abstract
As modern medical technology advances, the utilization of gene expression data has proliferated across diverse domains, particularly in cancer diagnosis and prognosis monitoring. However, gene expression data is often characterized by high dimensionality and a prevalence of redundant and noisy information, prompting the
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As modern medical technology advances, the utilization of gene expression data has proliferated across diverse domains, particularly in cancer diagnosis and prognosis monitoring. However, gene expression data is often characterized by high dimensionality and a prevalence of redundant and noisy information, prompting the need for effective strategies to mitigate issues like the curse of dimensionality and overfitting. This study introduces a novel hybrid ensemble equilibrium optimizer gene selection algorithm in response. In the first stage, a hybrid approach, combining multiple filters and gene correlation-based methods, is used to select an optimal subset of genes, which is achieved by evaluating the redundancy and complementary relationships among genes to obtain a subset with maximal information content. In the second stage, an equilibrium optimizer algorithm incorporating Gaussian Barebone and a novel gene pruning strategy is employed to further search for the optimal gene subset within the candidate gene space selected in the first stage. To demonstrate the superiority of the proposed method, it was compared with nine feature selection techniques on 15 datasets. The results indicate that the ensemble filtering method in the first stage exhibits strong stability and effectively reduces the search space of the gene selection algorithms. The improved equilibrium optimizer algorithm enhances the prediction accuracy while significantly reducing the number of selected features. These findings highlight the effectiveness of the proposed method as a valuable approach for gene selection.
Full article
(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)
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Open AccessArticle
Aerodynamic Effects of a Blended Multi-Winglet on an Airliner in Subsonic and Transonic Regimes
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Erina Kobayashi, Kazuhisa Chiba, Wataru Yamazaki and Masahiro Kanazaki
Biomimetics 2025, 10(8), 522; https://doi.org/10.3390/biomimetics10080522 - 10 Aug 2025
Abstract
This study investigates the aerodynamic performance of a blended multi-winglet configuration installed on the wingtip of a transonic commercial aircraft, focusing on both subsonic and transonic regimes. Conventional single winglets are typically optimized to reduce induced drag during cruise, but multi-winglets have the
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This study investigates the aerodynamic performance of a blended multi-winglet configuration installed on the wingtip of a transonic commercial aircraft, focusing on both subsonic and transonic regimes. Conventional single winglets are typically optimized to reduce induced drag during cruise, but multi-winglets have the potential to enhance lift during takeoff and landing. However, their effectiveness in transonic conditions remains insufficiently explored. In this work, a reference Boeing 767 blended winglet was divided into three distinct elements, each retaining the original wingtip airfoil. Computational simulations were conducted to compare single- and multi-winglet configurations under cruise conditions. Additional analyses were performed at subsonic speeds to evaluate lift performance. Under transonic conditions, the multi-winglet configuration exhibited a 1.4% increase in total drag due to a greater projected frontal area. However, it achieved reduced induced drag, attributed to the rearmost winglet’s negative cant angle, which suppresses vortex formation by inhibiting upward airflow. In subsonic flight, lift improved by up to 1.3% due to accelerated flow over the upper surface, enhanced by smaller leading-edge radii and air acceleration through inter-winglet gaps. These findings suggest that multi-winglets outperform single winglets in reducing induced drag during cruise and enhancing lift during takeoff and landing.
Full article
(This article belongs to the Section Biomimetic Design, Constructions and Devices)
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Open AccessReview
Recent Advances in Biomimetic Porous Materials for Real-World Applications
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Qunren Qiu, Yi Yang, Fanghua Liang, Gang Wang, Xuelong Han, Chuanfeng Zang and Mingzheng Ge
Biomimetics 2025, 10(8), 521; https://doi.org/10.3390/biomimetics10080521 - 8 Aug 2025
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Bionic synthesis technology has made significant breakthroughs in porous functional materials by replicating and optimizing biological structures. For instance, biomimetic titanium dioxide-coated carbon multilayer materials, prepared via biological templating, exhibit a hierarchical structure, abundant nanopores, and synergistic effects. Bionic mineralization further enhances microcapsules
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Bionic synthesis technology has made significant breakthroughs in porous functional materials by replicating and optimizing biological structures. For instance, biomimetic titanium dioxide-coated carbon multilayer materials, prepared via biological templating, exhibit a hierarchical structure, abundant nanopores, and synergistic effects. Bionic mineralization further enhances microcapsules by forming a secondary inorganic wall, granting them superior impermeability, high elastic modulus, and hardness. Through techniques like molecular self-assembly, electrospinning, and pressure-driven fusion, researchers have successfully fabricated centimeter-scale artificial lamellar bones without synthetic polymers. In environmental applications, electrospun membranes inspired by lotus leaves and bird bones achieve 99.94% separation efficiency for n-hexane–water mixtures, retaining nearly 99% efficiency after 20 cycles. For energy applications, an all-ceramic silica nanofiber aerogel with a bionic blind bristle structure demonstrates ultralow thermal conductivity (0.0232–0.0643 W·m−1·K−1) across a broad temperature range (−50 to 800 °C). This review highlights the preparation methods and recent advances in biomimetic porous materials for practical applications.
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Open AccessArticle
Development of an Artificial Neural Network-Based Tool for Predicting Failures in Composite Laminate Structures
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Milica Milic Jankovic, Jelena Svorcan and Ivana Atanasovska
Biomimetics 2025, 10(8), 520; https://doi.org/10.3390/biomimetics10080520 - 8 Aug 2025
Abstract
Composite materials are widely used in aerospace, automotive, biomedical, and renewable energy sectors due to their high strength-to-weight ratio and design flexibility. However, their anisotropic and layered nature makes structural analysis and failure prediction challenging. Traditional methods require solving complex interlaminar stress–strain equations,
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Composite materials are widely used in aerospace, automotive, biomedical, and renewable energy sectors due to their high strength-to-weight ratio and design flexibility. However, their anisotropic and layered nature makes structural analysis and failure prediction challenging. Traditional methods require solving complex interlaminar stress–strain equations, demanding significant computational resources. This paper presents a bio-inspired machine learning approach, based on human reasoning, to accelerate predictions and reduce dependence on computationally intensive Finite Element Analysis (FEA). An artificial neural network model was developed to rapidly estimate key parameters—laminate thickness, total weight, maximum stress, displacement, deformation, and failure criteria—based on stacking sequence and geometry for a desired load case. Although validated using a specific composite beam, the methodology demonstrates potential for broader use in rapid structural assessment, with prediction deviations under 15% compared to FEA results. The time savings are particularly significant—while conventional FEA can take several hours or even days, the ANN model delivers accurate predictions within seconds. The approach significantly reduces computational time while maintaining precision. Moreover, with further refinement, this logic-driven model could be effectively applied to aircraft maintenance, enabling faster decision-making and improved structural reliability assessment.
Full article
(This article belongs to the Section Biological Optimisation and Management)
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Open AccessArticle
Bioinspired Design of Ergonomic Tool Handles Using 3D-Printed Cellular Metamaterials
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Gregor Harih and Vasja Plesec
Biomimetics 2025, 10(8), 519; https://doi.org/10.3390/biomimetics10080519 - 8 Aug 2025
Abstract
The design of ergonomic tool handles is crucial for user comfort and performance, yet conventional stiff materials often lead to uneven pressure distribution and discomfort. This study investigates the application of 3D-printed cellular metamaterials with tunable stiffness, specifically gyroid structures, to enhance the
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The design of ergonomic tool handles is crucial for user comfort and performance, yet conventional stiff materials often lead to uneven pressure distribution and discomfort. This study investigates the application of 3D-printed cellular metamaterials with tunable stiffness, specifically gyroid structures, to enhance the ergonomic and haptic properties of tool handles. We employed finite element analysis to simulate finger–handle interactions and conducted subjective comfort evaluations with participants using a foxtail saw with handles of varying gyroid infill densities and a rigid PLA handle. Numerical results demonstrated that handles with medium stiffness significantly reduced peak contact pressures and promoted a more uniform pressure distribution compared to the stiff PLA handle. The softest gyroid handle, while compliant, exhibited excessive deformation, potentially compromising stability. Subjective comfort ratings corroborated these findings, with medium-stiffness handles receiving the highest scores for overall comfort, fit, and force transmission. These results highlight that a plateau-like mechanical response of the 3D-printed cellular metamaterial handle, inversely bioinspired by human soft tissue, effectively balances pressure redistribution and grip stability. This bioinspired design approach offers a promising direction for developing user-centered products that mitigate fatigue and discomfort in force-intensive tasks.
Full article
(This article belongs to the Special Issue 3D Bio-Printing for Regenerative Medicine Applications)
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Open AccessArticle
NEuroMOrphic Neural-Response Decoding System for Adaptive and Personalized Neuro-Prosthetics’ Control
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Georgi Rusev, Svetlozar Yordanov, Simona Nedelcheva, Alexander Banderov, Hugo Lafaye de Micheaux, Fabien Sauter-Starace, Tetiana Aksenova, Petia Koprinkova-Hristova and Nikola Kasabov
Biomimetics 2025, 10(8), 518; https://doi.org/10.3390/biomimetics10080518 - 7 Aug 2025
Abstract
In our previous work, we developed a neuromorphic decoder of intended movements of tetraplegic patients using ECoG recordings from the brain motor cortex, called Motor Control Decoder (MCD). Even though the training data are labeled based on the desired movement, there is no
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In our previous work, we developed a neuromorphic decoder of intended movements of tetraplegic patients using ECoG recordings from the brain motor cortex, called Motor Control Decoder (MCD). Even though the training data are labeled based on the desired movement, there is no guarantee that the patient is satisfied by the action of the effectors. Hence, the need for the classification of brain signals as satisfactory/unsatisfactory is obvious. Based on previous work, we upgrade our neuromorphic MCD with a Neural Response Decoder (NRD) that is intended to predict whether ECoG data are satisfactory or not in order to improve MCD accuracy. The main aim is to design an actor–critic structure able to adapt via reinforcement learning the MCD (actor) based on NRD (critic) predictions. For this aim, NRD was trained using not only an ECoG signal but also the MCD prediction or prescribed intended movement of the patient. The achieved accuracy of the trained NRD is satisfactory and contributes to improved MCD performance. However, further work has to be carried out to fully utilize the NRD for MCD performance optimization in an on-line manner. Possibility to include feedback from the patient would allow for further improvement of MCD-NRD accuracy.
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(This article belongs to the Special Issue Advances in Brain–Computer Interfaces 2025)
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Evolution and Trends of the Exploration–Exploitation Balance in Bio-Inspired Optimization Algorithms: A Bibliometric Analysis of Metaheuristics
by
Yoslandy Lazo, Broderick Crawford, Felipe Cisternas-Caneo, José Barrera-Garcia, Ricardo Soto and Giovanni Giachetti
Biomimetics 2025, 10(8), 517; https://doi.org/10.3390/biomimetics10080517 - 7 Aug 2025
Abstract
The balance between exploration and exploitation is a fundamental element in the design and performance of bio-inspired optimization algorithms. However, to date, its conceptual evolution and its treatment in the scientific literature have not been systematically characterized from a bibliometric approach. This study
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The balance between exploration and exploitation is a fundamental element in the design and performance of bio-inspired optimization algorithms. However, to date, its conceptual evolution and its treatment in the scientific literature have not been systematically characterized from a bibliometric approach. This study performs an exhaustive analysis of the scientific production on the balance between exploration and exploitation using records extracted from the Web of Science (WoS) database. The processing and analysis of the data were carried out through the combined use of Bibliometrix (R package) and VOSviewer, tools that made it possible to quantify productivity, map collaborative networks, and visualize emerging thematic trends. The results show a sustained growth in the volume of publications over the last decade, as well as the consolidation of academic collaboration networks and the emergence of new thematic lines in the field. In particular, metaheuristic algorithms have demonstrated a significant and growing impact, constituting a fundamental pillar in the advancement and methodological diversification of the exploration–exploitation balance. This work provides a quantitative framework and a structured view of the evolution of research, identifies the main actors and trends, and raises opportunities for future lines of research in the field of optimization using metaheuristics, the most prominent instantiation of bio-inspired optimization algorithms.
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(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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NeuroQ: Quantum-Inspired Brain Emulation
by
Jordi Vallverdú and Gemma Rius
Biomimetics 2025, 10(8), 516; https://doi.org/10.3390/biomimetics10080516 - 7 Aug 2025
Abstract
Traditional brain emulation approaches often rely on classical computational models that inadequately capture the stochastic, nonlinear, and potentially coherent features of biological neural systems. In this position paper, we introduce NeuroQ a quantum-inspired framework grounded in stochastic mechanics, particularly Nelson’s formulation. By reformulating
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Traditional brain emulation approaches often rely on classical computational models that inadequately capture the stochastic, nonlinear, and potentially coherent features of biological neural systems. In this position paper, we introduce NeuroQ a quantum-inspired framework grounded in stochastic mechanics, particularly Nelson’s formulation. By reformulating the FitzHugh–Nagumo neuron model with structured noise, we derive a Schrödinger-like equation that encodes membrane dynamics in a quantum-like formalism. This formulation enables the use of quantum simulation strategies—including Hamiltonian encoding, variational eigensolvers, and continuous-variable models—for neural emulation. We outline a conceptual roadmap for implementing NeuroQ on near-term quantum platforms and discuss its broader implications for neuromorphic quantum hardware, artificial consciousness, and time-symmetric cognitive architectures. Rather than demonstrating a working prototype, this work aims to establish a coherent theoretical foundation for future research in quantum brain emulation.
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(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)
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A Bio-Inspired Adaptive Probability IVYPSO Algorithm with Adaptive Strategy for Backpropagation Neural Network Optimization in Predicting High-Performance Concrete Strength
by
Kaifan Zhang, Xiangyu Li, Songsong Zhang and Shuo Zhang
Biomimetics 2025, 10(8), 515; https://doi.org/10.3390/biomimetics10080515 - 6 Aug 2025
Abstract
Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant
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Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R2 of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design.
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(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)
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Auto Deep Spiking Neural Network Design Based on an Evolutionary Membrane Algorithm
by
Chuang Liu and Haojie Wang
Biomimetics 2025, 10(8), 514; https://doi.org/10.3390/biomimetics10080514 - 6 Aug 2025
Abstract
In scientific research and engineering practice, the design of deep spiking neural network (DSNN) architectures remains a complex task that heavily relies on the expertise and experience of professionals. These architectures often require repeated adjustments and modifications based on factors such as the
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In scientific research and engineering practice, the design of deep spiking neural network (DSNN) architectures remains a complex task that heavily relies on the expertise and experience of professionals. These architectures often require repeated adjustments and modifications based on factors such as the DSNN’s performance, resulting in significant consumption of human and hardware resources. To address these challenges, this paper proposes an innovative evolutionary membrane algorithm for optimizing DSNN architectures. This algorithm automates the construction and design of promising network models, thereby reducing reliance on manual tuning. More specifically, the architecture of DSNN is transformed into the search space of the proposed evolutionary membrane algorithm. The proposed algorithm thoroughly explores the impact of hyperparameters, such as the candidate operation blocks of DSNN, to identify optimal configurations. Additionally, an early stopping strategy is adopted in the performance evaluation phase to mitigate the time loss caused by objective evaluations, further enhancing efficiency. The optimal models identified by the proposed algorithm were evaluated on the CIFAR-10 and CIFAR-100 datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, showing significant improvements in accuracy compared to the existing state-of-the-art methods. This work highlights the potential of evolutionary membrane algorithms to streamline the design and optimization of DSNN architectures, offering a novel and efficient approach to address the challenges in the applications of automated parameter optimization for DSNN.
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(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)
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