Biomimetics

19 pages, 1944 KB

Open AccessArticle

Research on Adaptive Cooperative Positioning Algorithm for Underwater Robots Based on Dolphin Group Cooperative Mechanism

by Shiwei Fan, Jiachong Chang, Zicheng Wang, Mingfeng Ding, Hongchao Sun and Yubo Zhao

Biomimetics 2026, 11(1), 82; https://doi.org/10.3390/biomimetics11010082 - 20 Jan 2026

Abstract

Inspired by the remarkable collaborative echolocation mechanisms of dolphin pods, the paper addresses the challenge of achieving high-precision cooperative positioning for clusters of unmanned underwater vehicles (UUVs) in complex marine environments. Cooperative positioning systems for UUVs typically rely on acoustic ranging information to [...] Read more.

Inspired by the remarkable collaborative echolocation mechanisms of dolphin pods, the paper addresses the challenge of achieving high-precision cooperative positioning for clusters of unmanned underwater vehicles (UUVs) in complex marine environments. Cooperative positioning systems for UUVs typically rely on acoustic ranging information to correct positional errors. However, the propagation characteristics of underwater acoustic signals are susceptible to environmental disturbances, often resulting in non-Gaussian, heavy-tailed distributions of ranging noise. Additionally, the strong nonlinearity of the system and the limited observability of measurement information further constrain positioning accuracy. To tackle these issues, this paper innovatively proposes a Factor Graph-based Adaptive Cooperative Positioning Algorithm (FGAWSP) suitable for heavy-tailed noise environments. The method begins by constructing a factor graph model for UUV cooperative positioning to intuitively represent the probabilistic dependencies between system states and observed variables. Subsequently, a novel factor graph estimation mechanism integrating adaptive weights with the product algorithm is designed. By conducting online assessment of residual information, this mechanism dynamically adjusts the fusion weights of different measurements, thereby achieving robust handling of anomalous range values. Experimental results demonstrate that the proposed method reduces positioning errors by 22.31% compared to the traditional algorithm, validating the effectiveness of our approach. Full article

(This article belongs to the Special Issue Bioinspired Robot Sensing and Navigation)

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32 pages, 18470 KB

Open AccessArticle

Enhancing Neuromorphic Robustness via Recurrence Resonance: The Role of Shared Weak Attractors in Quantum Logic Networks

by Yu Huang and Yukio-Pegio Gunji

Biomimetics 2026, 11(1), 81; https://doi.org/10.3390/biomimetics11010081 - 19 Jan 2026

Abstract

Recurrence resonance, a phenomenon that enhances system computational capability by exploiting noise to amplify hidden attractors, holds significant potential for applications such as edge computing and neuromorphic computing. Although previous studies have extensively explored its characteristics, the underlying mechanism regarding its generation remains [...] Read more.

Recurrence resonance, a phenomenon that enhances system computational capability by exploiting noise to amplify hidden attractors, holds significant potential for applications such as edge computing and neuromorphic computing. Although previous studies have extensively explored its characteristics, the underlying mechanism regarding its generation remains unclear. Here, we employed a Stochastic Recurrent Neural Network to simulate neural networks under various coupling conditions. By introducing appropriate inhibitory connections and examining the state transition matrices, we analyzed the characteristics and correlations of attractor landscapes in both global and local systems to elucidate the generative mechanism behind the “Edge of Chaos” dynamics observed under the quantum logic connectivity structure during recurrence resonance. The results show that the strategic introduction of inhibitory connections enriches the system’s attractor landscape without compromising the intensity of recurrence resonance. Furthermore, we find that when neurons are coupled via quantum logic and noise intensity meets specific conditions, the strong attractors of the global system decompose into those of distinct local subsystems, accompanied by the sharing of structurally similar weak attractors. These findings suggest that under quantum logic connectivity, the interaction between the strong attractors of different subsystems is mediated by a background of shared weak attractors, thereby enhancing both the system’s robustness against noise and the diversity of its state evolution. Full article

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23 pages, 3108 KB

Open AccessArticle

Hydrodynamic Study of Flow-Channel and Wall-Effect Characteristics in an Oscillating Hydrofoil Biomimetic Pumping Device

by Ertian Hua, Yang Lin, Sihan Li and Xiaopeng Wu

Biomimetics 2026, 11(1), 80; https://doi.org/10.3390/biomimetics11010080 - 19 Jan 2026

Abstract

To clarify how flow-channel configuration and wall spacing govern the hydrodynamic performance of an oscillating-hydrofoil biomimetic pumping device, this study conducted a systematic numerical investigation under confined-flow conditions. Using a finite-volume solver with an overset-grid technique, we compared pumping performance across three channel [...] Read more.

To clarify how flow-channel configuration and wall spacing govern the hydrodynamic performance of an oscillating-hydrofoil biomimetic pumping device, this study conducted a systematic numerical investigation under confined-flow conditions. Using a finite-volume solver with an overset-grid technique, we compared pumping performance across three channel configurations and a range of channel–wall distances. The results showed that bidirectional-channel confinement suppresses wake deflection and irregular vorticity evolution, enabling symmetric and periodic vortex organization and thereby improving pumping efficiency by approximately 33.6% relative to the single-channel case and by 62.7% relative to the no-channel condition. Wall spacing exhibited a distinctly non-monotonic influence on performance, revealing two high-performance regimes: under extreme confinement (gap ratio

h / c =

1.4), the device attains peak pumping and thrust efficiencies of 19.9% and 30.7%, respectively, associated with a strongly guided jet-like transport mode; and under moderate spacing (

h / c =

2.2–2.6), both efficiencies remain high due to an improved balance between directional momentum transport and reduced vortex-evolution losses. These findings identify key confinement-driven mechanisms and provide practical guidance for optimizing flow-channel design in ultralow-head oscillating-hydrofoil pumping applications. Full article

(This article belongs to the Special Issue Bio-Inspired Flapping Wing Aerodynamics for Propulsion and Power Generation: 2nd Edition)

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29 pages, 6120 KB

Open AccessArticle

Bionic Technology in Prosthetics: Multi-Objective Optimization of a Bioinspired Shoulder-Elbow Prosthesis with Embedded Actuation

by Jingxu Jiang, Gengbiao Chen, Xin Wang and Hongwei Yan

Biomimetics 2026, 11(1), 79; https://doi.org/10.3390/biomimetics11010079 - 19 Jan 2026

Abstract

The development of upper-limb prostheses is often hindered by limited dexterity, a restricted workspace, and bulky designs, primarily due to performance limitations in proximal joints like the shoulder and elbow, which contribute to high user abandonment rates. To overcome these challenges, this paper [...] Read more.

The development of upper-limb prostheses is often hindered by limited dexterity, a restricted workspace, and bulky designs, primarily due to performance limitations in proximal joints like the shoulder and elbow, which contribute to high user abandonment rates. To overcome these challenges, this paper presents a novel, bioinspired, and integrated prosthetic system as an advancement in bionic technology. The design incorporates a shoulder joint based on an asymmetric 3-RRR spherical parallel mechanism (SPM) with actuators embedded within the moving platform, and an elbow joint actuated by low-voltage Shape Memory Alloy (SMA) springs. The inverse kinematics of the shoulder mechanism was established, revealing the existence of up to eight configurations. We employed Multi-Objective Particle Swarm Optimization (MOPSO) to simultaneously maximize workspace coverage, enhance dexterity, and minimize joint torque. The optimized design achieves remarkable performance: (1) 85% coverage of the natural shoulder’s workspace; (2) a maximum von Mises stress of merely 3.4 MPa under a 40 N load, ensuring structural integrity; and (3) a sub-0.2 s response time for the SMA-driven elbow under low-voltage conditions (6 V) at a motion velocity of 6°/s. Both motion simulation and prototype testing validated smooth and anthropomorphic motion trajectories. This work provides a comprehensive framework for developing lightweight, high-performance prosthetic limbs, establishing a solid foundation for next-generation wearable robotics and bionic devices. Future research will focus on the integration of neural interfaces for intuitive control. Full article

(This article belongs to the Special Issue Editorial Board Members' Collection Series: Biomimetic Design, Constructions and Devices in Times of Change 2025)

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25 pages, 5632 KB

Open AccessArticle

Chaos-Enhanced, Optimization-Based Interpretable Classification Model and Performance Evaluation in Food Drying

by Cagri Kaymak, Bilal Alatas, Suna Yildirim, Ebru Akpinar, Gizem Gul Katircioglu, Murat Catalkaya, Orhan E. Akay and Mehmet Das

Biomimetics 2026, 11(1), 78; https://doi.org/10.3390/biomimetics11010078 - 18 Jan 2026

Abstract

Food drying is a widely used preservation technique; however, achieving high energy efficiency while maintaining product quality remains a significant challenge. This study aims to analyze comprehensive experimental data obtained during the hot-air drying process of the Paşa pear (regional pear) and the [...] Read more.

Food drying is a widely used preservation technique; however, achieving high energy efficiency while maintaining product quality remains a significant challenge. This study aims to analyze comprehensive experimental data obtained during the hot-air drying process of the Paşa pear (regional pear) and the system’s autonomous control structure using an explainable artificial intelligence (XAI)-based method. The intelligent drying system, operating for approximately 17.5 h under two temperatures (50 °C and 65 °C) and two air speeds (0.63 m/s and 1.03 m/s), continuously adjusted the temperature and air speed using a PLC-based control mechanism; it ensured stable control throughout the process by monitoring parameters such as product weight, moisture, inlet–outlet temperatures, and air speed in real time. Experimental results showed that drying performance varied significantly with operating conditions, with product mass decreasing from 450 g to 103 g. The innovative aspect of the study is that it obtained quantitative, interpretable rules without discretization by applying the oscillatory chaotic sunflower optimization algorithm (OCSFO) to multidimensional control and process data for the first time. Thanks to its chaotic search mechanism, OCSFO accurately analyzed complex drying dynamics and created rules that achieved over 90% success for high, medium, and low performance classes. The obtained explainable rules clearly demonstrate that drying temperature and air velocity are the dominant determining parameters for drying efficiency, while energy consumption and cabin temperature distribution play a supporting role in distinguishing between efficiency classes. These rules clearly demonstrate how changes in controlled temperature and air velocity, combined with product weight and heat transfer, affect drying performance. Thus, the study offers a robust framework that identifies critical factors affecting drying performance through a transparent artificial intelligence approach that leverages both the autonomous control system and XAI-based rule mining. Full article

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18 pages, 2899 KB

Open AccessArticle

Numerical Investigation on Drag Reduction Mechanisms of Biomimetic Microstructure Surfaces

by Jiangpeng Liu, Jie Xu, Chaogang Ding, Debin Shan and Bin Guo

Biomimetics 2026, 11(1), 77; https://doi.org/10.3390/biomimetics11010077 - 18 Jan 2026

Abstract

Biomimetic microstructured surfaces offer a promising passive strategy for drag reduction in marine and aerospace applications. This study employs computational fluid dynamics (CFD) simulations to systematically investigate the drag reduction performance and mechanisms of groove-type microstructures, addressing both geometry selection and dimensional optimization. [...] Read more.

Biomimetic microstructured surfaces offer a promising passive strategy for drag reduction in marine and aerospace applications. This study employs computational fluid dynamics (CFD) simulations to systematically investigate the drag reduction performance and mechanisms of groove-type microstructures, addressing both geometry selection and dimensional optimization. Three representative geometries (V-groove, blade-groove, and arc-groove) were compared under identical flow conditions (inflow velocity 5 m/s, Re = 7.5 × 10⁵) using the shear-stress-transport (SST k-ω) turbulence model, and the third-generation Ω criterion was employed for threshold-independent vortex identification. The results establish a clear performance hierarchy: blade-groove achieves the highest drag reduction rate of 18.2%, followed by the V-groove (16.5%) and arc-groove (14.7%). The analysis reveals that stable near-wall microvortices form dynamic vortex isolation layers that separate the high-speed flow from the groove valleys, with blade grooves generating the strongest and most fully developed vortex structures. A parametric study of blade-groove aspect ratios (h⁺/s⁺ = 0.35–1.0) further demonstrates that maintaining h⁺/s⁺ ≥ 0.75 preserves effective vortex-isolation layers, whereas reducing h⁺/s⁺ below 0.6 causes vortex collapse and performance degradation. These findings establish a comprehensive design framework combining geometry selection (blade-groove > V-groove > arc-groove) with dimensional optimization criteria, providing quantitative guidance for practical biomimetic drag-reducing surfaces. Full article

(This article belongs to the Special Issue Adhesion and Friction in Biological and Bioinspired Systems)

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47 pages, 20947 KB

Open AccessReview

Bioinspired Heat Exchangers: A Multi-Scale Review of Thermo-Hydraulic Performance Enhancement

by Hyunsik Yang, Jinhyun Pi, Soyoon Park and Wongyu Bae

Biomimetics 2026, 11(1), 76; https://doi.org/10.3390/biomimetics11010076 - 16 Jan 2026

Abstract

Heat exchangers are central to energy and process industries, yet performance is bounded by the trade-off between higher heat transfer and greater pressure drop. This review targets indirect-type heat exchangers and organizes bioinspired strategies through a multi-scale lens of surface, texture, and network [...] Read more.

Heat exchangers are central to energy and process industries, yet performance is bounded by the trade-off between higher heat transfer and greater pressure drop. This review targets indirect-type heat exchangers and organizes bioinspired strategies through a multi-scale lens of surface, texture, and network scales. It provides a structured comparison of their thermo-hydraulic behaviors and evaluation methods. At the surface scale, control of wettability and liquid-infused interfaces suppresses icing and fouling and stabilizes condensation. At the texture scale, microstructures inspired by shark skin and fish scales regulate near-wall vortices to balance drag reduction with heat-transfer enhancement. At the network scale, branched and bicontinuous pathways inspired by leaf veins, lung architectures, and triply periodic minimal surfaces promote uniform distribution and mixing, improving overall performance. The survey highlights practical needs for manufacturing readiness, durability, scale-up, and validation across operating ranges. By emphasizing analysis across scales rather than reliance on a single metric, the review distills design principles and selection guidelines for next-generation bioinspired heat exchangers. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Biomimetic Surfaces and Interfaces 2025)

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20 pages, 857 KB

Open AccessArticle

Hybrid Spike-Encoded Spiking Neural Networks for Real-Time EEG Seizure Detection: A Comparative Benchmark

by Ali Mehrabi, Neethu Sreenivasan, Upul Gunawardana and Gaetano Gargiulo

Biomimetics 2026, 11(1), 75; https://doi.org/10.3390/biomimetics11010075 - 16 Jan 2026

Abstract

Reliable and low-latency seizure detection from electroencephalography (EEG) is critical for continuous clinical monitoring and emerging wearable health technologies. Spiking neural networks (SNNs) provide an event-driven computational paradigm that is well suited to real-time signal processing, yet achieving competitive seizure detection performance with [...] Read more.

Reliable and low-latency seizure detection from electroencephalography (EEG) is critical for continuous clinical monitoring and emerging wearable health technologies. Spiking neural networks (SNNs) provide an event-driven computational paradigm that is well suited to real-time signal processing, yet achieving competitive seizure detection performance with constrained model complexity remains challenging. This work introduces a hybrid spike encoding scheme that combines Delta–Sigma (change-based) and stochastic rate representations, together with two spiking architectures designed for real-time EEG analysis: a compact feed-forward HybridSNN and a convolution-enhanced ConvSNN incorporating depthwise-separable convolutions and temporal self-attention. The architectures are intentionally designed to operate on short EEG segments and to balance detection performance with computational practicality for continuous inference. Experiments on the CHB–MIT dataset show that the HybridSNN attains 91.8% accuracy with an F1-score of 0.834 for seizure detection, while the ConvSNN further improves detection performance to 94.7% accuracy and an F1-score of 0.893. Event-level evaluation on continuous EEG recordings yields false-alarm rates of 0.82 and 0.62 per day for the HybridSNN and ConvSNN, respectively. Both models exhibit inference latencies of approximately 1.2 ms per 0.5 s window on standard CPU hardware, supporting continuous real-time operation. These results demonstrate that hybrid spike encoding enables spiking architectures with controlled complexity to achieve seizure detection performance comparable to larger deep learning models reported in the literature, while maintaining low latency and suitability for real-time clinical and wearable EEG monitoring. Full article

(This article belongs to the Special Issue Bioinspired Engineered Systems)

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21 pages, 2947 KB

Open AccessArticle

HFSOF: A Hierarchical Feature Selection and Optimization Framework for Ultrasound-Based Diagnosis of Endometrial Lesions

by Yongjun Liu, Zihao Zhang, Tongyu Chai and Haitong Zhao

Biomimetics 2026, 11(1), 74; https://doi.org/10.3390/biomimetics11010074 - 15 Jan 2026

Abstract

Endometrial lesions are common in gynecology, exhibiting considerable clinical heterogeneity across different subtypes. Although ultrasound imaging is the preferred diagnostic modality due to its noninvasive, accessible, and cost-effective nature, its diagnostic performance remains highly operator-dependent, leading to subjectivity and inconsistent results. To address [...] Read more.

Endometrial lesions are common in gynecology, exhibiting considerable clinical heterogeneity across different subtypes. Although ultrasound imaging is the preferred diagnostic modality due to its noninvasive, accessible, and cost-effective nature, its diagnostic performance remains highly operator-dependent, leading to subjectivity and inconsistent results. To address these limitations, this study proposes a hierarchical feature selection and optimization framework for endometrial lesions, aiming to enhance the objectivity and robustness of ultrasound-based diagnosis. Firstly, Kernel Principal Component Analysis (KPCA) is employed for nonlinear dimensionality reduction, retaining the top 1000 principal components. Secondly, an ensemble of three filter-based methods—information gain, chi-square test, and symmetrical uncertainty—is integrated to rank and fuse features, followed by thresholding with Maximum Scatter Difference Linear Discriminant Analysis (MSDLDA) for preliminary feature selection. Finally, the Whale Migration Algorithm (WMA) is applied to population-based feature optimization and classifier training under the constraints of a Support Vector Machine (SVM) and a macro-averaged F1 score. Experimental results demonstrate that the proposed closed-loop pipeline of “kernel reduction—filter fusion—threshold pruning—intelligent optimization—robust classification” effectively balances nonlinear structure preservation, feature redundancy control, and model generalization, providing an interpretable, reproducible, and efficient solution for intelligent diagnosis in small- to medium-scale medical imaging datasets. Full article

(This article belongs to the Special Issue Bio-Inspired AI: When Generative AI and Biomimicry Overlap)

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37 pages, 21684 KB

Open AccessArticle

Multi-Strategy Improved Pelican Optimization Algorithm for Engineering Optimization Problems and 3D UAV Path Planning

by Ming Zhang, Maomao Luo and Huiming Kang

Biomimetics 2026, 11(1), 73; https://doi.org/10.3390/biomimetics11010073 - 15 Jan 2026

Abstract

To address the path-planning challenge for unmanned aerial vehicles (UAVs) in complex environments, this study presents an improved pelican optimization algorithm enhanced with multiple strategies (MIPOA). The proposed method introduces four main improvements: (1) using chaotic mapping to spread the initial search points [...] Read more.

To address the path-planning challenge for unmanned aerial vehicles (UAVs) in complex environments, this study presents an improved pelican optimization algorithm enhanced with multiple strategies (MIPOA). The proposed method introduces four main improvements: (1) using chaotic mapping to spread the initial search points more evenly, thereby increasing population variety; (2) incorporating a random Lévy-flight strategy to improve the exploration of the search space; (3) integrating a differential evolution approach based on Cauchy mutation to strengthen individual diversity and overall optimization ability; and (4) adopting an adaptive disturbance factor to speed up convergence and fine-tune solutions. To evaluate MIPOA, comparative tests were carried out against classical and modern intelligent algorithms using the CEC2017 and CEC2022 benchmark sets, along with a custom UAV environmental model. Results show that MIPOA converges faster and achieves more accurate solutions than the original pelican optimization algorithm (POA). On CEC2017 in 30-, 50-, and 100-dimensional cases, MIPOA attained the best average ranks of 1.57, 2.37, and 2.90, respectively, and achieved the top results on 26, 21, and 19 test functions, outperforming both POA and other advanced algorithms. For CEC2022 (20 dimensions), MIPOA obtained the highest Friedman average rank of 1.42, demonstrating its effectiveness in complex UAV path-planning tasks. The method enables the generation of faster, shorter, safer, and collision-free flight paths for UAVs, underscoring the robustness and wide applicability of MIPOA in real-world UAV path-planning scenarios. Full article

(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)

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31 pages, 9004 KB

Open AccessArticle

Multi-Strategy Fusion Improved Walrus Optimization Algorithm for Coverage Optimization in Wireless Sensor Networks

by Ling Li, Youyi Ding, Xiancun Zhou, Xuemei Zhu, Zongling Wu, Wei Peng, Jingya Zhang and Chaochuan Jia

Biomimetics 2026, 11(1), 72; https://doi.org/10.3390/biomimetics11010072 - 15 Jan 2026

Abstract

The Walrus Optimization (WO) algorithm, a metaheuristic inspired by walrus behavior, is known for its competitive convergence speed and effectiveness in solving high-dimensional and practical engineering optimization problems. However, it suffers from a tendency to converge to local optima and exhibits instability during [...] Read more.

The Walrus Optimization (WO) algorithm, a metaheuristic inspired by walrus behavior, is known for its competitive convergence speed and effectiveness in solving high-dimensional and practical engineering optimization problems. However, it suffers from a tendency to converge to local optima and exhibits instability during the iterative process. To overcome these limitations, this study proposes an improved WO (IMWO) algorithm based on the integration of Differential Evolution/best/1 (DE/best/1) mutation, Logistics–Sine–Cosine (LSC) Mapping, and the Beta Opposition-Based Learning (Beta-OBL) strategy. These strategies work synergistically to enhance the algorithm’s global exploration capability, improve its search stability, and accelerate convergence with higher precision. The performance of the IMWO algorithm was comprehensively evaluated using the CEC2017 and CEC2022 benchmark test suites, where it was compared against the original WO algorithm and six other state-of-the-art metaheuristics. Experimental data revealed that the IMWO algorithm achieved average fitness rankings of 1.66 and 1.33 in the two test suites, ranking first among all compared algorithms. The WSN coverage optimization problem aims to maximize the monitored area while reducing perception blind spots under limited node resources and energy constraints, which is a typical complex optimization problem with multiple constraints. In a practical application addressing the coverage optimization problem in Wireless Sensor Networks (WSNs), the IMWO algorithm attained average coverage rates of 95.86% and 96.48% in two sets of coverage experiments, outperforming both the original WO and other compared algorithms. These results confirm the practical utility and robustness of the IMWO algorithm in solving complex real-world engineering problems. Full article

(This article belongs to the Section Biological Optimisation and Management)

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12 pages, 1777 KB

Open AccessArticle

Enhanced Fracture Energy and Toughness of UV-Curable Resin Using Flax Fiber Composite Laminates

by Mingwen Ou, Huan Li, Dequan Tan, Yizhen Peng, Hao Zhong, Linmei Wu and Wubin Shan

Biomimetics 2026, 11(1), 71; https://doi.org/10.3390/biomimetics11010071 - 15 Jan 2026

Abstract

Ultraviolet (UV) curable resins are widely used in photopolymerization-based 3D printing due to their rapid curing and compatibility with high-resolution processes. However, their brittleness and limited mechanical performance restrict their applicability, particularly in impact-resistant high-performance 3D-printed structures. Inspired by the mantis shrimp’s exceptional [...] Read more.

Ultraviolet (UV) curable resins are widely used in photopolymerization-based 3D printing due to their rapid curing and compatibility with high-resolution processes. However, their brittleness and limited mechanical performance restrict their applicability, particularly in impact-resistant high-performance 3D-printed structures. Inspired by the mantis shrimp’s exceptional energy absorption and impact resistance, attributed to its helicoidal fiber architecture, we developed a Bouligand flax fiber-reinforced composite laminate. By constructing biomimetic helicoidal composites based on Bouligand arrangements, the mechanical performance of flax fiber-reinforced UV-curable resin was systematically investigated. The influence of flax fiber orientation was assessed using mechanical testing combined with the digital image correlation (DIC) method. The results demonstrate that a 45° interlayer angle of flax fiber significantly enhanced the fracture energy of the resin from 1.67 KJ/m² to 15.41 KJ/m², an increase of ~823%. Moreover, the flax fiber-reinforced helicoidal structure markedly improved the ultimate tensile strength of the resin, with the 90° interlayer angle of flax fiber exhibiting the greatest enhancement, increasing from 5.32 MPa to 19.45 MPa. Full article

(This article belongs to the Special Issue Editorial Board Members' Collection Series: Biomimetics of Materials, Functions, Structures and Processes 2025)

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38 pages, 12112 KB

Open AccessArticle

Enhanced Educational Optimization Algorithm Based on Student Psychology for Global Optimization Problems and Real Problems

by Wenyu Miao, Katherine Lin Shu and Xiao Yang

Biomimetics 2026, 11(1), 70; https://doi.org/10.3390/biomimetics11010070 - 14 Jan 2026

Abstract

To address the insufficient exploration ability, susceptibility to local optima, and limited convergence accuracy of the standard Student Psychology-Based Optimization (SPBO) algorithm in three-dimensional UAV trajectory planning, we propose an enhanced variant, Enhanced SPBO (ESPBO). ESPBO augments SPBO with three complementary strategies: (i) [...] Read more.

To address the insufficient exploration ability, susceptibility to local optima, and limited convergence accuracy of the standard Student Psychology-Based Optimization (SPBO) algorithm in three-dimensional UAV trajectory planning, we propose an enhanced variant, Enhanced SPBO (ESPBO). ESPBO augments SPBO with three complementary strategies: (i) Time-Adaptive Scheduling, which uses normalized time (

τ = t / T

) to schedule global step-size shrinking, Gaussian fine-tuning, and Lévy flight intensity, enabling strong early exploration and fine late-stage exploitation; (ii) Mentor Pool Guidance, which selects a top-K mentor set and applies time-varying guidance weights to reduce misleading attraction and improve directional stability; and (iii) Directional Jump Exploration, which couples a differential vector with Lévy flights to strengthen basin-crossing while keeping the differential step bounded for robustness. Numerical experiments on CEC2017, CEC2020 and CEC2022 benchmark functions compare ESPBO with Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Improved multi-strategy adaptive Grey Wolf Optimization (IAGWO), Dung Beetle Optimization (DBO), Snake Optimization (SO), Rime Optimization (RIME), and the original SPBO. We evaluate best path length, mean trajectory length, standard deviation, and convergence curves and assess statistical stability via Wilcoxon rank-sum tests (p = 0.05) and the Friedman test. ESPBO significantly outperforms the comparison algorithms in path-planning accuracy and convergence stability, ranking first on both test suites. Applied to 3D UAV trajectory planning in mountainous terrain with no-fly zones, ESPBO achieves an optimal path length of 199.8874 m, an average path length of 205.8179 m, and a standard deviation of 5.3440, surpassing all baselines; notably, ESPBO’s average path length is even lower than the optimal path length of other algorithms. These results demonstrate that ESPBO provides an efficient and robust solution for UAV trajectory optimization in intricate environments and extends the application of swarm intelligence algorithms in autonomous navigation. Full article

(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)

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30 pages, 10476 KB

Open AccessArticle

Large-Scale Multi-UAV Task Allocation via a Centrality-Driven Load-Aware Adaptive Consensus Bundle Algorithm for Biomimetic Swarm Coordination

by Weifei Gan, Hongxuan Xu, Yunwei Bai, Xin Zhou, Wangyu Wu and Xiaofei Du

Biomimetics 2026, 11(1), 69; https://doi.org/10.3390/biomimetics11010069 - 14 Jan 2026

Abstract

Large multi-UAV mission systems operate over time-varying communication graphs with heterogeneous platforms, where classical distributed task assignment may incur excessive message passing and suboptimal task–resource matching. To address these challenges, this paper proposes CLAC-CBBA (Centrality-Driven and Load-Aware Adaptive Clustering CBBA), an enhanced variant [...] Read more.

Large multi-UAV mission systems operate over time-varying communication graphs with heterogeneous platforms, where classical distributed task assignment may incur excessive message passing and suboptimal task–resource matching. To address these challenges, this paper proposes CLAC-CBBA (Centrality-Driven and Load-Aware Adaptive Clustering CBBA), an enhanced variant of the Consensus-Based Bundle Algorithm (CBBA) for large heterogeneous swarms. The proposed method is biomimetic in the sense that it integrates swarm-inspired self-organization and load-aware self-regulation to improve scalability and robustness, resembling decentralized role emergence and negative-feedback workload balancing in natural swarms. Specifically, CLAC-CBBA first identifies key nodes via a centrality-based adaptive cluster-reconfiguration mechanism (CenCluster) and partitions the network into cooperation domains to reduce redundant communication. It then applies a load-aware cluster self-regulation mechanism (LCSR), which combines resource attributes and spatial information, uses K-medoids clustering, and triggers split/merge reconfiguration based on real-time load imbalance. CBBA bidding is executed locally within clusters, while anchors and cluster representatives synchronize winners/bids to ensure globally consistent, conflict-free assignments. Simulations across diverse network densities and swarm sizes show that CLAC-CBBA reduces communication overhead and runtime while improving total task score compared with CBBA and several advanced variants, with statistically significant gains. These results demonstrate that CLAC-CBBA is scalable and robust for large-scale heterogeneous UAV task allocation. Full article

(This article belongs to the Section Biological Optimisation and Management)

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28 pages, 10210 KB

Open AccessArticle

Black-Winged Kite Algorithm Integrating Opposition-Based Learning and Quasi-Newton Strategy

by Ning Zhao, Tinghua Wang and Yating Zhu

Biomimetics 2026, 11(1), 68; https://doi.org/10.3390/biomimetics11010068 - 14 Jan 2026

Abstract

To address the deficiencies in global search capability and population diversity decline of the black-winged kite algorithm (BKA), this paper proposes an enhanced black-winged kite algorithm integrating opposition-based learning and quasi-Newton strategy (OQBKA). The algorithm introduces a mirror imaging strategy based on convex [...] Read more.

To address the deficiencies in global search capability and population diversity decline of the black-winged kite algorithm (BKA), this paper proposes an enhanced black-winged kite algorithm integrating opposition-based learning and quasi-Newton strategy (OQBKA). The algorithm introduces a mirror imaging strategy based on convex lens imaging (MOBL) during the migration phase to enhance the population’s spatial distribution and assist individuals in escaping local optima. In later iterations, it incorporates the quasi-Newton method to enhance local optimization precision and convergence performance. Ablation studies on the CEC2017 benchmark set confirm the strong complementarity between the two integrated strategies, with OQBKA achieving an average ranking of 1.34 across all 29 test functions. Comparative experiments on the CEC2022 benchmark suite further verify its superior exploration–exploitation balance and optimization accuracy: under 10- and 20-dimensional settings, OQBKA attains the best average rankings of 2.5 and 2.17 across all 12 test functions, outperforming ten state-of-the-art metaheuristic algorithms. Moreover, evaluations on three constrained engineering design problems, including step-cone pulley optimization, corrugated bulkhead design, and reactor network design, demonstrate the practicality and robustness of the proposed approach in generating feasible solutions under complex constraints. Full article

(This article belongs to the Section Biological Optimisation and Management)

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28 pages, 14228 KB

Open AccessReview

Research Progress on Biomimetic Water Collection Materials

by Hengyu Pan, Lingmei Zhu, Huijie Wei, Tiance Zhang, Boyang Tian, Jianhua Wang, Yongping Hou and Yongmei Zheng

Biomimetics 2026, 11(1), 67; https://doi.org/10.3390/biomimetics11010067 - 13 Jan 2026

Abstract

Water scarcity constitutes a major global challenge. Biomimetic water collection materials, which mimic the efficient water capture and transport mechanisms, offer a crucial approach to addressing the water crisis. This review summarizes the research progress on biomimetic water collection materials, focusing on biological [...] Read more.

Water scarcity constitutes a major global challenge. Biomimetic water collection materials, which mimic the efficient water capture and transport mechanisms, offer a crucial approach to addressing the water crisis. This review summarizes the research progress on biomimetic water collection materials, focusing on biological prototypes, operational mechanisms, and core aspects of biomimetic design. Typical water-collecting biological surfaces in nature exhibit distinctive structure–function synergy: spider silk achieves directional droplet transport via periodic spindle-knot structures, utilizing Laplace pressure difference and surface energy gradient; the desert beetle’s back features hydrophilic microstructures and a hydrophobic waxy coating, forming a fog-water collection system based on heterogeneous wettability; cactus spines enhance droplet transport efficiency through the synergy of gradient grooves and barbs; and shorebird beaks enable rapid water convergence via liquid bridge effects. These biological prototypes provide vital inspiration for the design of biomimetic water collection materials. Drawing on biological mechanisms, researchers have developed diverse biomimetic water collection materials. This review offers a theoretical reference for their structural design and performance enhancement, highlighting bio-inspiration’s core value in high-efficiency water collection material development. Additionally, this paper discusses challenges and opportunities of these materials, providing insights for advancing the engineering application of next-generation high-efficiency biomimetic water collection materials. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Biomimetic Surfaces and Interfaces 2025)

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68 pages, 9076 KB

Open AccessReview

Collagen Type I as a Biological Barrier Interface in Biomimetic Microfluidic Devices: Properties, Applications, and Challenges

by Valentina Grumezescu and Liviu Duta

Biomimetics 2026, 11(1), 66; https://doi.org/10.3390/biomimetics11010066 - 13 Jan 2026

Abstract

Collagen type I has become a practical cornerstone for constructing biologically meaningful barrier interfaces in microfluidic systems. Its fibrillar architecture, native ligand display, and susceptibility to cell-mediated remodeling support epithelial and endothelial polarization, tight junctions, and transport behaviors that are difficult to achieve [...] Read more.

Collagen type I has become a practical cornerstone for constructing biologically meaningful barrier interfaces in microfluidic systems. Its fibrillar architecture, native ligand display, and susceptibility to cell-mediated remodeling support epithelial and endothelial polarization, tight junctions, and transport behaviors that are difficult to achieve with purely synthetic barrier interfaces. Recent advances pair these biological strengths with tighter engineering control. For example, ultrathin collagen barriers (tens of micrometers or less) enable faster molecular exchange and short-range signaling; gentle crosslinking and composite designs limit gel compaction and delamination under flow; and patterning/bioprinting introduce alignment, graded porosity, and robust integration into device geometries. Applications now span intestine, vasculature, skin, airway, kidney, and tumor–stroma interfaces, with readouts including transepithelial/transendothelial electrical resistance (TEER), tracer permeability, and image-based quality control of fiber architecture. Persistent constraints include batch variability, long-term mechanical drift, limited standardization of fibrillogenesis conditions, and difficulties scaling fabrication without loss of bioactivity. Priorities include reporting standards for microstructure and residual crosslinker, chips for continuous monitoring, immune-competent co-cultures, and closer collaboration across materials science, microfabrication, computational modelling, and clinical pharmacology. Thus, this review synthesizes the state-of-the-art and offers practical guidance on technological readiness and future directions for using collagen type I as a biological barrier interface in biomimetic microfluidic systems. Full article

(This article belongs to the Special Issue Biomimetic Coating Technologies and Biomaterials for Medical Applications)

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36 pages, 3003 KB

Open AccessArticle

A Modified Artificial Protozoa Optimizer for Robust Parameter Identification in Nonlinear Dynamic Systems

by Davut Izci, Serdar Ekinci, Gökhan Yüksek, Mostafa Rashdan, Burcu Bektaş Güneş, Muhammet İsmail Güngör and Mohammad Salman

Biomimetics 2026, 11(1), 65; https://doi.org/10.3390/biomimetics11010065 - 12 Jan 2026

Abstract

Accurate parameter identification in nonlinear and chaotic dynamic systems requires optimization algorithms that can reliably balance global exploration and local refinement in complex, multimodal search landscapes. To address this challenge, a modified artificial protozoa optimizer (mAPO) is developed in this study by embedding [...] Read more.

Accurate parameter identification in nonlinear and chaotic dynamic systems requires optimization algorithms that can reliably balance global exploration and local refinement in complex, multimodal search landscapes. To address this challenge, a modified artificial protozoa optimizer (mAPO) is developed in this study by embedding two complementary mechanisms into the original artificial protozoa optimizer: a probabilistic random learning strategy to enhance population diversity and global search capability, and a Nelder–Mead simplex-based local refinement stage to improve exploitation and fine-scale solution adjustment. The general optimization performance and scalability of the proposed framework are first evaluated using the CEC2017 benchmark suite. Statistical analyses conducted over shifted and rotated, hybrid, and composition functions demonstrate that mAPO achieves improved mean performance and reduced variability compared with the original APO, indicating enhanced robustness in high-dimensional and complex optimization problems. The effectiveness of mAPO is then examined in nonlinear system identification applications involving chaotic dynamics. Offline and online parameter identification experiments are performed on the Rössler chaotic system and a permanent magnet synchronous motor, including scenarios with abrupt parameter variations. Comparative simulations against APO and several state-of-the-art optimizers show that mAPO consistently yields smaller objective function values, more accurate parameter estimates, and superior statistical stability. In the PMSM case, exact parameter reconstruction with zero error is achieved across all independent runs, while rapid and smooth convergence is observed under both static and time-varying conditions. Full article

(This article belongs to the Section Biological Optimisation and Management)

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13 pages, 7015 KB

Open AccessArticle

Preload-Free Conformal Integration of Tactile Sensors on the Fingertip’s Curved Surface

by Lei Liu, Peng Ran, Yongyao Li, Tian Tang, Yun Hu, Jian Xiao, Daijian Luo, Lu Dai, Yufei Liu, Jiahu Yuan and Dapeng Wei

Biomimetics 2026, 11(1), 64; https://doi.org/10.3390/biomimetics11010064 - 12 Jan 2026

Abstract

Humans could sensitively perceive and identify objects through dense mechanoreceptors distributed on the skin of curved fingertips. Inspired by this biological structure, this study presents a general conformal integration method for flexible tactile sensors on curved fingertip surfaces. By adopting a spherical partition [...] Read more.

Humans could sensitively perceive and identify objects through dense mechanoreceptors distributed on the skin of curved fingertips. Inspired by this biological structure, this study presents a general conformal integration method for flexible tactile sensors on curved fingertip surfaces. By adopting a spherical partition design and an inverse mode auxiliary layering process, it ensures the uniform distribution of stress at different curvatures. The sensor adopts a 3 × 3 tactile array configuration, replicating the 3D curved surface distribution of human mechanoreceptors. By analyzing multi-point outputs, the sensor reconstructs contact pressure gradients and infers the softness or stiffness of touched objects, thereby realizing both structural and functional bionics. These sensors exhibit excellent linearity within 0–100 kPa (sensitivity ≈ 36.86 kPa⁻¹), fast response (2 ms), and outstanding durability (signal decay of only 1.94% after 30,000 cycles). It is worth noting that this conformal tactile fingertip integration method not only exhibits uniform responses at each unit, but also has the preload-free advantage, and then performs well in pulse detection and hardness discrimination. This work provides a novel bioinspired pathway for conformal integration of tactile sensors, enabling artificial skins and robotic fingertips with human-like tactile perception. Full article

(This article belongs to the Special Issue Bionic Engineering Materials and Structural Design)

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