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
Development of Acellular Hepatic Scaffolds Through a Low-Cost Gravity-Assisted Perfusion Decellularization Method
Biomimetics 2025, 10(11), 777; https://doi.org/10.3390/biomimetics10110777 (registering DOI) - 15 Nov 2025
Abstract
Background: Developing reliable and cost-effective decellularization methods is critical for advancing tissue engineering and regenerative medicine, particularly in regions with limited access to specialized perfusion systems. Methods: This study standardized a gravity-assisted perfusion protocol for rat liver decellularization, designed to operate without pumps
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Background: Developing reliable and cost-effective decellularization methods is critical for advancing tissue engineering and regenerative medicine, particularly in regions with limited access to specialized perfusion systems. Methods: This study standardized a gravity-assisted perfusion protocol for rat liver decellularization, designed to operate without pumps or pressurized equipment. Adult Wistar rat livers were processed through a gravity-driven vascular flushing method and compared with a conventional immersion-based protocol. The resulting scaffolds were evaluated by macroscopic inspection, histological staining (Masson’s trichrome), and residual DNA quantification. Results: The gravity-assisted perfusion method achieved more efficient cellular removal and superior preservation of extracellular matrix (ECM) integrity compared with immersion. Residual DNA levels were 3.7 ng/mg in perfused samples, 209.47 ng/mg in immersed samples, and 331.97 ng/mg in controls, confirming a statistically significant reduction (p < 0.05). Only the perfused group met the accepted threshold for effective decellularization (<50 ng/mg dry tissue). Histological analysis corroborated these findings, showing the absence of nuclei and the preservation of collagen architecture characteristic of a structurally intact ECM. Conclusions: This low-cost, reproducible, and technically simple system enables the generation of high-quality acellular hepatic scaffolds without mechanical pumps. Its accessibility and scalability make it suitable for laboratories with limited infrastructure and educational settings. Moreover, this gravity-assisted approach provides a foundation for future recellularization and preclinical studies aimed at developing bioengineered liver constructs for regenerative and transplant applications.
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(This article belongs to the Section Biomimetic Processing and Molecular Biomimetics)
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Machine Learning Distinguishes Plant Bioelectric Recordings with and Without Nearby Human Movement
by
Peter A. Gloor and Moritz Weinbeer
Biomimetics 2025, 10(11), 776; https://doi.org/10.3390/biomimetics10110776 (registering DOI) - 15 Nov 2025
Abstract
Background: Quantitatively detecting whether plants exhibit measurable bioelectric differences in the presence of nearby human movement remains challenging, in part because plant signals are low-amplitude, slow, and easily confounded by environmental factors. Methods: We recorded bioelectric activity from 2978 plant samples
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Background: Quantitatively detecting whether plants exhibit measurable bioelectric differences in the presence of nearby human movement remains challenging, in part because plant signals are low-amplitude, slow, and easily confounded by environmental factors. Methods: We recorded bioelectric activity from 2978 plant samples across three species (basil, salad, tomato) using differential electrode pairs (leaf and soil electrodes) sampling at 142 Hz. Two trained performers executed three specific eurythmic gestures near experimental plants while control plants remained isolated. Random Forest and Convolutional Neural Network classifiers were applied to distinguish the control from treatment conditions using engineered features including spectral, temporal, wavelet, and frequency domain characteristics. Results: Random Forest classification achieved 62.7% accuracy (AUC = 0.67) distinguishing differences in recordings collected near a moving human from control conditions, representing a statistically significant 12.7 percentage point improvement over chance. Individual performer signatures were detectable with 68.2% accuracy, while plant species classification achieved only 44.5% accuracy, indicating minimal species-specific artifacts. Temporal analysis revealed that the plants with repeated exposure exhibited consistently less negative bioelectric amplitudes compared to single-exposure plants. Innovation: We introduce a data-driven approach that pairs standardized, short-window bioelectric recordings with machine-learning classifiers (Random Forest, CNN) to test, in an exploratory manner, whether plant signals differ between human-moving-nearby and isolation conditions. Conclusions: Plants exhibit modest but statistically detectable bioelectric differences in the presence of nearby human movement. Rather than attributing these differences to eurythmic movement itself, the present design can only demonstrate that plant recordings collected within ~1 m of a moving human differ, modestly but statistically, from recordings taken ≥3 m away. The underlying biophysical pathways and specific contributing factors (airflow, VOCs, thermal plumes, vibration, electromagnetic fields) remain unknown. These results should therefore be interpreted as exploratory correlations, not mechanistic evidence of gesture-specific plant sensing.
Full article
(This article belongs to the Special Issue Biomimetics in Intelligent Sensor: 2nd Edition)
Open AccessArticle
Research on the Optimization of Uncertain Multi-Stage Production Integrated Decisions Based on an Improved Grey Wolf Optimizer
by
Weifei Gan, Xin Zhou, Wangyu Wu and Chang-An Xu
Biomimetics 2025, 10(11), 775; https://doi.org/10.3390/biomimetics10110775 (registering DOI) - 15 Nov 2025
Abstract
Defect-rate uncertainty creates cascading operational challenges in multi-stage production, often driving inefficiency and misallocation of labor, materials, and capacity. To confront this, we develop a multi-stage Production Integrated Decision (MsPID) framework that unifies quality inspection and shop-floor decision-making within a single computational model.
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Defect-rate uncertainty creates cascading operational challenges in multi-stage production, often driving inefficiency and misallocation of labor, materials, and capacity. To confront this, we develop a multi-stage Production Integrated Decision (MsPID) framework that unifies quality inspection and shop-floor decision-making within a single computational model. The framework couples a two-stage sampling inspection policy—used to statistically learn and control defect-rate uncertainty via estimation and rejection rules—with a multi-process, multi-part production decision model. Optimization is carried out with an Improved Grey Wolf Optimizer (IGWO) enhanced with Latin hypercube sampling (LHS) for uniformly diverse initialization; an evolutionary factor mechanism that blends simulated binary crossover (SBX) among three leadership-guided parents (Alpha, Beta, Delta) to strengthen global exploration in early iterations and focus exploitation later; and a greedy, mutation-assisted opposition learning step applied to the lowest-performing quartile of the population to effect leader-informed local refinement and accept only fitness-improving moves. Experiments show the method identifies minimum-cost policies across six single-stage benchmark cases and yields a total profit of 43,800 units in a representative multi-stage scenario, demonstrating strong performance in uncertain environments. Sensitivity analysis further clarifies how recommended decisions adapt to shifts in estimated defect rates, finished product prices, and swap/changeover losses. These results highlight how bio-inspired intelligence can enable adaptive, efficient, and resilient integrated production management at scale.
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(This article belongs to the Section Biological Optimisation and Management)
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Open AccessArticle
Biomimetic Digital Twin of Future Embodied Internet for Advancing Autonomous Vehicles and Robots
by
Ming Xie and Xiaohui Wang
Biomimetics 2025, 10(11), 774; https://doi.org/10.3390/biomimetics10110774 - 14 Nov 2025
Abstract
Efficient coordination among software modules is essential for biomimetic robotic systems, much like the interaction among organs in a biological organism. However, implementing inter-process or inter-module communication in autonomous systems remains a complex and time-consuming task, particularly for new researchers. Simplifying inter-module communication
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Efficient coordination among software modules is essential for biomimetic robotic systems, much like the interaction among organs in a biological organism. However, implementing inter-process or inter-module communication in autonomous systems remains a complex and time-consuming task, particularly for new researchers. Simplifying inter-module communication is the central focus of this study. To address this challenge, we propose the DigitalTwinPort framework, a novel communication abstraction inspired by the port-based connectivity of embedded hardware systems. Unlike middleware-dependent solutions such as ROS, the proposed framework provides a lightweight, object-oriented structure that enables unified and scalable communication between software modules and networked devices. The concept is implemented in C++ and validated through an autonomous surface vehicle (ASV) developed for the RobotX Challenge. Results demonstrate that the DigitalTwinPort simplifies the development of distributed systems, reduces configuration overhead, and enhances synchronization between digital and physical components. This work lays the foundation for future digital twin architectures in embodied Internet systems, where software and hardware can interact seamlessly through standardized digital ports.
Full article
(This article belongs to the Special Issue Artificial Intelligence for Autonomous Robots: 4th Edition)
Open AccessArticle
Biomimicry of Echinocactus grusonii Spines as a Source of Inspiration for Design Principles and Implantation Strategies of Self-Inserting Intraneural Interfaces
by
Pier Nicola Sergi
Biomimetics 2025, 10(11), 773; https://doi.org/10.3390/biomimetics10110773 - 14 Nov 2025
Abstract
Cactaceae are plants equipped with spines and adapted to extremely arid environments. In particular, Echinocactus grusonii spines are almost cylindrical structures, which may occasionally present an enlargement of their proximal cross sectional area. In this work, the spines of Echinocactus grusonii were explored
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Cactaceae are plants equipped with spines and adapted to extremely arid environments. In particular, Echinocactus grusonii spines are almost cylindrical structures, which may occasionally present an enlargement of their proximal cross sectional area. In this work, the spines of Echinocactus grusonii were explored as a possible source of biomimetic inspiration for the design and the implantation strategies of self-inserting intraneural interfaces. More specifically, the elastic stability of spines was theoretically studied for structures able to puncture the surface of an external object, as well as for structures unable to pierce it. The biomimicry of Echinocactus grusonii spines suggested an improved insertion strategy for self-inserting intraneural interfaces together with structural changes able to increase their elastic stability. The theoretical approach provided in this work was able to predict an increase of the first buckling threshold up to for not puncturing self-inserting neural interfaces, and up to for puncturing ones.
Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Biomimetics of Materials, Functions, Structures and Processes 2025)
Open AccessArticle
Development of a Numerical Model of a Bio-Inspired Sea Lion Robot
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Shraman Kadapa, Nicholas Marcouiller, Anthony C. Drago, James L. Tangorra and Harry G. Kwatny
Biomimetics 2025, 10(11), 772; https://doi.org/10.3390/biomimetics10110772 - 14 Nov 2025
Abstract
There is a growing demand for underwater robots to support offshore tasks such as exploration, environmental monitoring, and critical underwater missions. To enhance the performance of these systems, researchers are increasingly turning to biological inspiration to develop robots that understand and adapt the
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There is a growing demand for underwater robots to support offshore tasks such as exploration, environmental monitoring, and critical underwater missions. To enhance the performance of these systems, researchers are increasingly turning to biological inspiration to develop robots that understand and adapt the swimming strategies of aquatic animals. Numerical modeling plays a critical role in evaluating and improving the performance of these complex, multi-body robotic systems. However, developing accurate models for multi-body robots that swim freely in three dimensions remains a significant challenge. This study presents the development and validation of a numerical model of a bio-inspired California sea lion (Zalophus californianus) robot. The model was developed to simulate, analyze, and visualize the robot’s body motions in water. The equations of motion were derived in closed form using the Euler–Poincaré formulation, offering advantages for control and stability analysis. Hydrodynamic coefficients essential for estimating fluid forces were computed using computational fluid dynamics (CFD) and strip theory and further refined using a genetic algorithm to reduce the sim-to-real gap. The model demonstrated strong agreement with experiments, accurately predicting the translation and orientation of the robot. This framework provides a validated foundation for simulation, control, and optimization of bio-inspired multi-body systems.
Full article
(This article belongs to the Special Issue Advances in Sensing, Dynamics, and Control for Bio-Inspired Underwater Systems)
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Open AccessArticle
The Impact of Collagen Fiber and Slit Orientations on Meshing Ratios in Skin Meshing Models
by
Masoumeh Razaghi Pey Ghaleh and Denis O’Mahoney
Biomimetics 2025, 10(11), 771; https://doi.org/10.3390/biomimetics10110771 - 14 Nov 2025
Abstract
Skin meshing facilitates the greater expansion of donor skin through patterned slits and is widely used for treating extensive burn injuries. However, the actual expansion often falls below manufacturers’ claims. Previous computational analyses using the isotropic Yeoh model have shown that Langer’s line
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Skin meshing facilitates the greater expansion of donor skin through patterned slits and is widely used for treating extensive burn injuries. However, the actual expansion often falls below manufacturers’ claims. Previous computational analyses using the isotropic Yeoh model have shown that Langer’s line orientation and slit direction significantly affect induced stress and meshing ratios, yet the use of nonlinear anisotropic models that represent collagen fiber alignment corresponding to Langer’s lines remains unexplored. This study employs a nonlinear anisotropic Gasser–Ogden–Holzapfel (GOH) model with slit orientations of 0°, 45°, and 90°, consistent with geometries reported in the literature, to quantify induced stress in skin meshing by incorporating collagen fibers within the dermis layer. The GOH parameters were calibrated to human back skin data uniaxially stretched parallel and perpendicular to Langer’s lines using Levenberg–Marquardt optimization in the GIBBON toolbox (MATLAB R2023a) coupled with FEBio v4.0, achieving a standard deviation of 3% relative to experimental data. The GOH model predicted the highest induced stress at 100% strain for the 45° slit parallel to Langer’s lines and the lowest for the 90° slit perpendicular, exceeding 40 MPa due to absence of damage and rupture modeling but accurately representing anisotropic mesh behavior.
Full article
(This article belongs to the Special Issue Biomimetic Approaches in Healthcare—Innovations Inspired by Nature: 3rd Edition)
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Open AccessArticle
EABI-DETR: An Efficient Aerial Small Object Detection Network
by
Fufang Li, Yuehua Zhang and Yuxuan Fan
Biomimetics 2025, 10(11), 770; https://doi.org/10.3390/biomimetics10110770 - 13 Nov 2025
Abstract
Small object detection, as an important research topic in computer vision, has been widely applied in aerial visual tasks such as remote sensing and UAV imagery. However, due to challenges such as small object size, large-scale variations, and complex backgrounds, existing detection models
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Small object detection, as an important research topic in computer vision, has been widely applied in aerial visual tasks such as remote sensing and UAV imagery. However, due to challenges such as small object size, large-scale variations, and complex backgrounds, existing detection models often struggle to capture fine-grained semantics and high-resolution texture information in aerial scenes, leading to limited performance. To address these issues, this paper proposes an efficient aerial small object detection model, EABI-DETR (Efficient Attention and Bi-level Integration DETR), based on the RT-DETR framework. The proposed model introduces systematic enhancements from three aspects: (1) A lightweight backbone network, C2f-EMA, is developed by integrating the C2f structure with an efficient multi-scale attention (EMA) mechanism. This design jointly models channel semantics and spatial details with minimal computational overhead, thereby strengthening the perception of small objects. (2) A P2-BiFPN bi-directional multi-scale fusion module is further designed to incorporate shallow high-resolution features. Through top-down and bottom-up feature interactions, this module enhances cross-scale information flow and effectively preserves the fine details and textures of small objects. (3) To improve localization robustness, a Focaler-MPDIoU loss function is introduced to better handle hard samples during regression optimization. Experiments conducted on the VisDrone2019 dataset demonstrate that EABI-DETR achieves 53.4% mAP@0.5 and 34.1% mAP@0.5:0.95, outperforming RT-DETR by 6.2% and 5.1%, respectively, while maintaining high inference efficiency. These results confirm the effectiveness of integrating lightweight attention mechanisms and shallow feature fusion for aerial small object detection, offering a new paradigm for efficient UAV-based visual perception.
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(This article belongs to the Special Issue Exploration of Bioinspired Computer Vision and Pattern Recognition)
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Designing Biomimetic Learning Environments for Animal Welfare Education: A Gamified Approach
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Ebru Emsen, Bahadir Baran Odevci, Muzeyyen Kutluca Korkmaz, Fatma Alshamsi and Alyaziya Alkaabi
Biomimetics 2025, 10(11), 769; https://doi.org/10.3390/biomimetics10110769 - 13 Nov 2025
Abstract
Animal welfare education requires pedagogical models that bridge conceptual knowledge with practice. This study presents GamifyWELL, a biomimetic, gamified learning environment for students, farmers, and veterinary technicians. Grounded in ecological principles of adaptation, diversification, and niche specialization, the design emulates how living systems
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Animal welfare education requires pedagogical models that bridge conceptual knowledge with practice. This study presents GamifyWELL, a biomimetic, gamified learning environment for students, farmers, and veterinary technicians. Grounded in ecological principles of adaptation, diversification, and niche specialization, the design emulates how living systems evolve through feedback and cooperation. These principles were translated into an instructional model that integrates a core pathway (Pre-Test, Levels 1–4, Post-Test) with optional enrichment tasks and a role-specific Reward Marketplace. Question formats are constant across levels (MCQ, image-based, video-based) while cognitive difficulty increases, culminating in Positive Welfare scenarios. We describe the learning design structure and report preliminary implementation observations using a mixed-methods evaluation plan (pre/post knowledge assessments and engagement indicators). Results from early deployment indicate strong usability and engagement, with high voluntary uptake of enrichment tasks and positive learner feedback on role-tailored rewards; full empirical testing is in progress. Findings support the feasibility and pedagogical promise of biomimetic gamification to enhance knowledge, motivation, and intended practice in animal welfare education. GamifyWELL offers a replicable framework for nature-inspired instructional design that can be extended to allied sustainability domains.
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(This article belongs to the Special Issue Biologically-Inspired Product Development)
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Open AccessReview
Recent Advances in the Applications of Biomaterials in Ovarian Cancer
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A M U B Mahfuz, Amol V. Janorkar, Rodney P. Rocconi and Yuanyuan Duan
Biomimetics 2025, 10(11), 768; https://doi.org/10.3390/biomimetics10110768 - 12 Nov 2025
Abstract
Among the gynecological cancers, ovarian cancer is the most fatal. Despite advancements in modern medicine, the survival rate is abysmally low among ovarian cancer patients. Ovarian cancer poses several unique challenges, like late diagnosis due to the initial vagueness of the symptoms and
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Among the gynecological cancers, ovarian cancer is the most fatal. Despite advancements in modern medicine, the survival rate is abysmally low among ovarian cancer patients. Ovarian cancer poses several unique challenges, like late diagnosis due to the initial vagueness of the symptoms and lack of effective screening protocols. Recently, biomaterials have been explored and utilized extensively for the diagnosis, treatment, and screening of ovarian malignancies. Biomaterials can help bypass the obstacles of traditional chemotherapy and enhance imaging capabilities. They are also indispensable for next-generation biosensors and tumor organoids. Biomaterials inspired by biomimetic strategies that replicate the structural, chemical, and functional properties of natural biological systems have proven to have better functionalities. While numerous review articles have examined biomaterials in oncology, there is a lack of reviews dedicated specifically to their applications in ovarian cancer. This review aims to address this critical gap by providing the first comprehensive overview of the current biomaterial research on ovarian cancer and highlighting key challenges, opportunities, and future directions in this evolving interdisciplinary field.
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(This article belongs to the Special Issue Functional Biomimetic Materials and Devices for Biomedical Applications: 4th Edition)
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Arctic Puffin Optimization Algorithm Integrating Opposition-Based Learning and Differential Evolution with Engineering Applications
by
Yating Zhu, Tinghua Wang and Ning Zhao
Biomimetics 2025, 10(11), 767; https://doi.org/10.3390/biomimetics10110767 - 12 Nov 2025
Abstract
The Arctic Puffin Optimization (APO) algorithm, proposed in 2024, is a swarm intelligence optimization. Similar to other swarm intelligence optimization algorithms, it suffers from issues such as slow convergence in the early stage, being easy to fall into local optima, and insufficient balance
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The Arctic Puffin Optimization (APO) algorithm, proposed in 2024, is a swarm intelligence optimization. Similar to other swarm intelligence optimization algorithms, it suffers from issues such as slow convergence in the early stage, being easy to fall into local optima, and insufficient balance between exploration and exploitation. To address these limitations, an improved APO (IAPO) algorithm incorporating multiple strategies is proposed. Firstly, a mirror opposition-based learning mechanism is introduced to expand the search scope, improving the efficiency of searching for the optimal solution, which enhances the algorithm’s convergence accuracy and optimization speed. Secondly, a dynamic differential evolution strategy with adaptive parameters is integrated to improve the algorithm’s ability to escape local optima and achieve precise optimization. Comparative experimental results between IAPO and eight other optimization algorithms on 20 benchmark functions, as well as CEC2019 and CEC2022 test functions, show that IAPO achieves higher accuracy, faster convergence, and superior robustness, securing first-place average rankings of 1.35, 1.30, 1.25, and 1.08 on the 20 benchmark functions, CEC 2019, 10- and 20-dimensional CEC 2022 test sets, respectively. Finally, simulation experiments were conducted on three engineering optimization design problems. IAPO achieved optimal values of 5.2559 × 10−1, 1.09 × 103, and 1.49 × 104 for these engineering problems, ranking first in all cases. This further validates the effectiveness and practicality of the IAPO algorithm.
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(This article belongs to the Section Biological Optimisation and Management)
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An Improved Crested Porcupine Optimization Algorithm Incorporating Butterfly Search and Triangular Walk Strategies
by
Binhe Chen, Yaodan Chen, Li Cao, Changzu Chen and Yinggao Yue
Biomimetics 2025, 10(11), 766; https://doi.org/10.3390/biomimetics10110766 - 12 Nov 2025
Abstract
The Crested Porcupine Optimizer (CPO), as a newly emerging swarm intelligence algorithm, demonstrates advantages in balancing global exploration and local exploitation but still suffers from limitations in convergence speed and local exploitation precision. To address these issues, this paper proposes an enhanced variant,
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The Crested Porcupine Optimizer (CPO), as a newly emerging swarm intelligence algorithm, demonstrates advantages in balancing global exploration and local exploitation but still suffers from limitations in convergence speed and local exploitation precision. To address these issues, this paper proposes an enhanced variant, the Butterfly Search and Triangular Walk Crested Porcupine Optimizer (BTCPO). The method achieves a dynamic balance between exploration and exploitation by combining triangular walk to boost local exploitation and butterfly search to increase global variety. Experimental results on 23 classical benchmark functions and the CEC2021 test suite show that BTCPO outperforms CPO as well as seven state-of-the-art algorithms (DBO, HBA, BKA, HHO, GWO, GOOSE, and SSA). Specifically, BTCPO achieves the best performance on more than 80% of CEC2021 functions, with convergence speed improved by approximately 25% compared to CPO. Furthermore, BTCPO exhibits higher efficiency and usefulness in engineering design problems such as trusses, welded beams, and cantilever beams. These findings demonstrate the theoretical and practical advantages of BTCPO, making it a workable approach to solving difficult optimization problems.
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(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)
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MEIAO: A Multi-Strategy Enhanced Information Acquisition Optimizer for Global Optimization and UAV Path Planning
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Yongzheng Chen, Ruibo Sun, Jun Zheng, Yuanyuan Shao and Haoxiang Zhou
Biomimetics 2025, 10(11), 765; https://doi.org/10.3390/biomimetics10110765 - 12 Nov 2025
Abstract
With the expansion of unmanned aerial vehicles (UAVs) into complex three-dimensional (3D) terrains for reconnaissance, rescue, and related missions, traditional path planning methods struggle to meet multi-constraint and multi-objective requirements. Existing swarm intelligence algorithms, limited by the “no free lunch” theorem, also face
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With the expansion of unmanned aerial vehicles (UAVs) into complex three-dimensional (3D) terrains for reconnaissance, rescue, and related missions, traditional path planning methods struggle to meet multi-constraint and multi-objective requirements. Existing swarm intelligence algorithms, limited by the “no free lunch” theorem, also face challenges when the standard Information Acquisition Optimizer (IAO) is applied to such tasks, including low exploration efficiency in high-dimensional search spaces, rapid loss of population diversity, and improper boundary handling. To address these issues, this study proposes a Multi-Strategy Enhanced Information Acquisition Optimizer (MEIAO). First, a Levy Flight-based information collection strategy is introduced to leverage its combination of short-range local searches and long-distance jumps, thereby broadening global exploration. Second, an adaptive differential evolution operator is designed to dynamically balance exploration and exploitation via a variable mutation factor, while crossover and greedy selection mechanisms help maintain population diversity. Third, a globally guided boundary handling strategy adjusts out-of-bound dimensions to feasible regions, preventing the generation of low-quality paths. Performance was evaluated on the CEC2017 (dim = 30/50/100) and CEC2022 (dim = 10/20) benchmark suites by comparing MEIAO with eight algorithms, including VPPSO and IAO. Based on the mean, standard deviation, Friedman mean rank, and Wilcoxon rank-sum tests, MEIAO demonstrated superior performance in local exploitation of unimodal functions, global exploration of multimodal functions, and complex adaptation on composite functions while exhibiting stronger robustness. Finally, MEIAO was applied to 3D mountainous UAV path planning, where a cost model considering path length, altitude standard deviation, and turning smoothness was established. The experimental results show that MEIAO achieved an average path cost of 253.9190, a 25.7% reduction compared to IAO (341.9324), with the lowest standard deviation (60.6960) among all algorithms. The generated paths were smoother, collision-free, and achieved faster convergence, offering an efficient and reliable solution for UAV operations in complex environments.
Full article
(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications: 2nd Edition)
Open AccessArticle
Design Methodology for a Backrest-Lifting Nursing Bed Based on Dual-Channel Behavior–Emotion Data Fusion and Biomechanical Simulation: A Human-Centered and Data-Driven Optimization Approach
by
Xiaochan Wang, Cheolhee Cho, Peng Zhang, Shuyuan Ge and Liyun Wang
Biomimetics 2025, 10(11), 764; https://doi.org/10.3390/biomimetics10110764 - 12 Nov 2025
Abstract
Population aging and rising rehabilitation demands highlight the need for advanced assistive devices to improve mobility in individuals with motor impairments. Existing back-support lifting nursing beds often lack adequate human–machine adaptability, safety, and emotional consideration. This study presents a human-centered, data-driven optimization pipeline
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Population aging and rising rehabilitation demands highlight the need for advanced assistive devices to improve mobility in individuals with motor impairments. Existing back-support lifting nursing beds often lack adequate human–machine adaptability, safety, and emotional consideration. This study presents a human-centered, data-driven optimization pipeline that integrates behavior–emotion dual recognition, simulation verification, and parameter optimization with user demand mining, biomechanical simulation, and sustainable practices. The design utilizes GreenAI, focusing on low-power algorithms and eco-friendly materials, ensuring energy-efficient AI models and reducing the environmental footprint. A dual-channel data fusion method was developed, combining movement parameters from sit-to-lie transitions with emotional needs extracted from e-commerce reviews using the Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) models. The fuzzy Kano model prioritized design objectives, identifying multi-position adjustment, joint protection, armrest optimization, and interaction comfort as key targets. An AnyBody-based human–device model quantified muscle (erector spinae, rectus abdominis, trapezius) and hip joint loads during posture changes. Simulations verified the design’s ability to improve load distribution, reduce joint stress, and enhance comfort. The optimized nursing bed demonstrated improved adaptability across user profiles while maintaining functional reliability. This framework offers a scalable paradigm for intelligent rehabilitation equipment design, with potential extension toward AI-driven adaptive control and clinical validation. This sustainable methodology ensures that the device not only meets rehabilitation goals but also contributes to a more environmentally responsible healthcare solution, aligning with global sustainability efforts.
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(This article belongs to the Special Issue Advanced Intelligent Systems and Biomimetics)
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ACGA a Novel Biomimetic Hybrid Optimisation Algorithm Based on a HP Protein Visualizer: An Interpretable Web-Based Tool for 3D Protein Folding Based on the Hydrophobic-Polar Model
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Ioan Sima, Daniela-Maria Cristea, Laszlo Barna Iantovics and Virginia Niculescu
Biomimetics 2025, 10(11), 763; https://doi.org/10.3390/biomimetics10110763 - 12 Nov 2025
Abstract
In this study, we used the hydrophobic-polar (HP) two-dimensional square and three-dimensional cubic lattice models for the problem of protein structure prediction (PSP). This kind of lattice reduces computational time and calculations, the conformational space from to
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In this study, we used the hydrophobic-polar (HP) two-dimensional square and three-dimensional cubic lattice models for the problem of protein structure prediction (PSP). This kind of lattice reduces computational time and calculations, the conformational space from to for the 2D square lattice and for the 3D cubic lattice. Even within this context, it remains challenging for genetic algorithms or other metaheuristics to identify the optimal solutions. The contributions of the paper consist of: (1) implementation of a high-performing novel genetic algorithm (GA); instead of considering only the self-avoiding walk (SAW) conformations approached in other work, we decided to allow any conformation to appear in the population at all stages of the proposed all conformations biomimetic genetic algorithm (ACGA). This increases the probability of achieving good conformations (self avoiding walk ones), with the lowest energy. In addition to classical crossover and mutation operators, (2) we introduced specific translation operators for these two operations. We have proposed and implemented an HP Protein Visualizer tool which offers interpretability, a hybrid approach in that the visualizer gives some insight to the algorithm, that analyse and optimise protein structures HP model. The program resulted based on performed research, provides a molecular modeling tool for studying protein folding using technologies such as Node.js, Express and p5js for 3D rendering, and includes optimization algorithms to simulate protein folding.
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(This article belongs to the Special Issue Bio-Inspired Artificial Intelligence in Healthcare)
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Low-Overhead Learning: Quantized Shallow Neural Networks at the Service of Genetic Algorithm Optimization
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Fabián Pizarro, Emanuel Vega, Ricardo Soto, Broderick Crawford and José Villamayor
Biomimetics 2025, 10(11), 762; https://doi.org/10.3390/biomimetics10110762 - 12 Nov 2025
Abstract
Online parameter tuning significantly enhances the performance of optimization algorithms by dynamically adjusting mutation and crossover rates. However, current approaches often suffer from high computational costs and limited adaptability to complex and dynamic fitness landscapes, particularly when machine learning methods are employed. This
[...] Read more.
Online parameter tuning significantly enhances the performance of optimization algorithms by dynamically adjusting mutation and crossover rates. However, current approaches often suffer from high computational costs and limited adaptability to complex and dynamic fitness landscapes, particularly when machine learning methods are employed. This work proposes a quantized shallow neural network (SNN) as an efficient learning-based component for dynamically adjusting the mutation and crossover rates of a genetic algorithm (GA). By leveraging runtime-generated data and applying quantization techniques like Quantization-aware Training (QaT) and Post-training Quantization (PtQ), the proposed approach reduces computational overhead while maintaining competitive performance. Experimental evaluation on 15 continuous benchmark functions demonstrates that the quantized SNN achieves high-quality solutions while significantly reducing execution time compared to alternative shallow learning methods. This study highlights the potential of quantized SNNs to balance efficiency and performance, broadening the applicability of shallow learning in optimization.
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(This article belongs to the Section Biological Optimisation and Management)
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Open AccessArticle
An Enhanced Secretary Bird Optimization Algorithm Based on Multi Population Management for Numerical Optimization Problems
by
Jin Zhu, Bojun Liu, Jun Zheng, Shaojie Yin and Meng Wang
Biomimetics 2025, 10(11), 761; https://doi.org/10.3390/biomimetics10110761 - 12 Nov 2025
Abstract
The Secretary Bird Optimization Algorithm (SBOA) is a novel swarm-based meta-heuristic that formulates an optimization model by mimicking the secretary bird’s hunting and predator-evasion behaviors, and thus possesses appreciable application potential. Nevertheless, it suffers from an unbalanced exploration–exploitation ratio, difficulty in maintaining population
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The Secretary Bird Optimization Algorithm (SBOA) is a novel swarm-based meta-heuristic that formulates an optimization model by mimicking the secretary bird’s hunting and predator-evasion behaviors, and thus possesses appreciable application potential. Nevertheless, it suffers from an unbalanced exploration–exploitation ratio, difficulty in maintaining population diversity, and a tendency to be trapped in local optima. To eliminate these drawbacks, this paper proposes an SBOA variant (MESBOA) that integrates a multi-population management strategy with an experience-trend guidance strategy. The proposed method is compared with eight advanced basic/enhanced algorithms of different categories on both the CEC2017 and CEC2022 test suites. Experimental results demonstrate that MESBOA delivers faster convergence, more stable robustness and higher accuracy, achieving mean rankings of 2.500 (CEC2022 10-D), 2.333 (CEC2022 20-D), 1.828 (CEC2017 50-D) and 1.931 (CEC2017 100-D). Moreover, engineering constrained optimization problems further verify its applicability to real-world optimization tasks.
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(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)
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Open AccessArticle
Multi-Strategy Improved POA for Global Optimization Problems and 3D UAV Path Planning
by
Rui Zhang, Jingbo Zhan and Jianfeng Wang
Biomimetics 2025, 10(11), 760; https://doi.org/10.3390/biomimetics10110760 - 11 Nov 2025
Abstract
With the rapid development of smart manufacturing and the low-altitude economy, drone technology—as a vital component of next-generation intelligent equipment—has garnered significant attention from researchers. Path planning, one of the core challenges in drone technology advancement, directly impacts the efficiency and safety of
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With the rapid development of smart manufacturing and the low-altitude economy, drone technology—as a vital component of next-generation intelligent equipment—has garnered significant attention from researchers. Path planning, one of the core challenges in drone technology advancement, directly impacts the efficiency and safety of drone mission execution. However, most existing drone path planning algorithms suffer from issues such as requiring extensive interactive information or being prone to getting stuck in local optima. This study introduces a multi-strategy enhanced Pelican Optimization Algorithm (MIPOA) tailored for UAV path planning. To improve the quality of the initial population, a hybrid initialization approach combining low-discrepancy sequences with heuristic refinement is developed. The low-discrepancy component promotes a more uniform distribution across the search space, while the heuristic mechanism enhances the fitness of selected individuals and reduces redundant exploration. Furthermore, a subgroup mean-guided updating strategy is designed to accelerate convergence toward the global optimum. To maintain exploration ability, a random reinitialization boundary mechanism is incorporated, effectively preventing premature convergence. To validate the algorithm’s performance, MIPOA is compared with eleven benchmark metaheuristics on the CEC2017 test suite, and statistical analyses confirm its superior optimization capability. Finally, MIPOA is applied to 3D UAV path planning under four threat scenarios in a realistic environment, demonstrating robust adaptability and achieving successful mission completion.
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(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)
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Open AccessArticle
Impact of Replicated Biomimetic Microstructures on the Wettability of Injection-Molded Polymer Surfaces
by
Vojtěch Šorm, Jakub Bittner, Petr Lenfeld, Dora Kroisová and Štěpánka Dvořáčková
Biomimetics 2025, 10(11), 759; https://doi.org/10.3390/biomimetics10110759 - 11 Nov 2025
Abstract
This article evaluates the influence of replicated natural structures, produced by micro-machining, on the wettability of plastic parts made from hydrophilic and hydrophobic polymer materials under various temperature and pressure conditions. Although many studies have focused on biomimetic surface design, the effect of
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This article evaluates the influence of replicated natural structures, produced by micro-machining, on the wettability of plastic parts made from hydrophilic and hydrophobic polymer materials under various temperature and pressure conditions. Although many studies have focused on biomimetic surface design, the effect of specific processing parameters on the accurate replication of natural topologies and their resulting wettability has been only partially explored. This study addresses this gap by systematically analyzing the effect of melt temperature and packing pressure on the functional replication of micro-machined biomimetic structures. The research describes the design of hierarchical microstructures inspired by biomimetics and their fabrication by micro-milling on molded parts. Test samples were prepared from polypropylene (PP), acrylonitrile butadiene styrene (ABS), and polyamide 6.6 (PA 6.6) under different processing parameters, and wettability was assessed using contact angle (CA) measurements. The results confirmed significant variations in surface wettability depending on polymer type, melt temperature, and packing pressure. For the hydrophilic relief (Rock Moss), contact angles below 90° were obtained for all tested polymers, including PP, which decreased from 98.7° on a flat surface to 82.4° at 220 °C and 500 bar. In PA 6.6, a reduction of up to 12% in contact angle was observed compared to smooth samples at 310 °C and 500 bar. For hydrophobic reliefs (Three-part Hibiscus and Tricolor Pansy), contact angles exceeded 100–110°, with the highest value of 108.3 ± 1.6° for PP at 200 °C and 500 bar. Suitable combinations of melt temperature and packing pressure enabled accurate replication of microstructures while preserving their functional wettability, demonstrating the possibility of tuning surface properties through topological design.
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(This article belongs to the Special Issue Bioinspired Engineered Systems)
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Open AccessReview
Hydrogel-Based Strategies for the Prevention and Treatment of Radiation-Induced Skin Injury: Progress and Mechanistic Insights
by
Yinhui Wang, Huan Liu, Yushan He, Mei Li, Jie Gao, Zongtai Han, Jiayu Zhou and Jianguo Li
Biomimetics 2025, 10(11), 758; https://doi.org/10.3390/biomimetics10110758 - 11 Nov 2025
Abstract
Radiation-induced skin injury (RISI) is one of the most common complications of radiotherapy, severely compromising patients’ quality of life. However, no standardized treatment has yet been established. Owing to their high water content, three-dimensional porous structure, excellent biocompatibility, and tunable functionalization, hydrogels have
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Radiation-induced skin injury (RISI) is one of the most common complications of radiotherapy, severely compromising patients’ quality of life. However, no standardized treatment has yet been established. Owing to their high water content, three-dimensional porous structure, excellent biocompatibility, and tunable functionalization, hydrogels have emerged as promising candidates for both the prevention and treatment of RISI. This review provides a comprehensive overview of recent advances in hydrogel-based interventions for RISI, with particular focus on material classifications and underlying mechanisms. Mechanistically, hydrogels facilitate tissue repair through multiple synergistic pathways, including antioxidation, anti-inflammation, angiogenesis, and tissue remodeling. Understanding these mechanisms not only provides a theoretical basis for the rational design of next-generation wound dressings but also enhances the translational potential of hydrogels in clinical radiotherapy. With the convergence of materials science, radiation medicine, and pharmaceutical innovation, hydrogels are poised to redefine therapeutic strategies for RISI and accelerate their clinical implementation.
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(This article belongs to the Section Biomimetics of Materials and Structures)
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