Biomimetics最新文献_第4页

Three Strategies Enhance the Bionic Coati Optimization Algorithm for Global Optimization and Feature Selection Problems. 三种策略增强了全局优化和特征选择问题的仿生Coati优化算法。

IF 3.4 3区医学

Biomimetics Pub Date : 2025-06-07 DOI: 10.3390/biomimetics10060380

Qingzheng Cao, Shuqi Yuan, Yi Fang

{"title":"Three Strategies Enhance the Bionic Coati Optimization Algorithm for Global Optimization and Feature Selection Problems.","authors":"Qingzheng Cao, Shuqi Yuan, Yi Fang","doi":"10.3390/biomimetics10060380","DOIUrl":"10.3390/biomimetics10060380","url":null,"abstract":"With the advancement of industrial digitization, utilizing large datasets for model training to boost performance is a pivotal technical approach for industry progress. However, raw training datasets often contain abundant redundant features, which increase model training's computational cost and impair generalization ability. To tackle this, this study proposes the bionic ABCCOA algorithm, an enhanced version of the bionic Coati Optimization Algorithm (COA), to improve redundant feature elimination in datasets. To address the bionic COA's inadequate global search performance in feature selection (FS) problems, leading to lower classification accuracy, an adaptive search strategy is introduced. This strategy combines individual learning capability and the learnability of disparities, enhancing global exploration. For the imbalance between the exploration and exploitation phases in the bionic COA algorithm when solving FS problems, which often traps it in suboptimal feature subsets, a balancing factor is proposed. By integrating phase control and dynamic adjustability, a good balance between the two phases is achieved, reducing the likelihood of getting stuck in suboptimal subsets. Additionally, to counter the bionic COA's insufficient local exploitation performance in FS problems, increasing classification error rates, a centroid guidance strategy is presented. By combining population centroid guidance and fractional-order historical memory, the algorithm lowers the classification error rate of feature subsets and speeds up convergence. The bionic ABCCOA algorithm was tested on the CEC2020 test functions and engineering problem, achieving an over 90% optimization success rate and faster convergence, confirming its efficiency. Applied to 27 FS problems, it outperformed comparative algorithms in best, average, and worst fitness function values, classification accuracy, feature subset size, and running time, proving it an efficient and robust FS algorithm.","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12190305/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144483005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect of Surfactant on Bubble Formation on Superhydrophobic Surface in Quasi-Static Regime. 表面活性剂对准静态超疏水表面气泡形成的影响。

IF 3.4 3区医学

Biomimetics Pub Date : 2025-06-07 DOI: 10.3390/biomimetics10060382

Hangjian Ling, John Ready, Daniel O'Coin

{"title":"Effect of Surfactant on Bubble Formation on Superhydrophobic Surface in Quasi-Static Regime.","authors":"Hangjian Ling, John Ready, Daniel O'Coin","doi":"10.3390/biomimetics10060382","DOIUrl":"10.3390/biomimetics10060382","url":null,"abstract":"We experimentally studied the effect of a surfactant on bubble formation on a superhydrophobic surface (SHS). The bubble was created by injecting gas through an orifice on the SHS at a constant flow rate in the quasi-static regime. The surfactant, 1-pentanol, was mixed with water at concentration C ranging from 0 to 0.08 mol/L, corresponding to surface tension σ ranging from 72 to 43 mN/m. We found that as C increased, the bubble detachment volume (Vd) and maximum bubble base radius (Rdmax) decreased. For a low surfactant concentration, the static contact angle θ0 remained nearly constant, and Vd and Rdmax decreased due to lower surface tensions, following the scaling laws Rdmax~σ1/2 and Vd~σ3/2. The bubble shapes at different concentrations were self-similar. The bubble height, bubble base radius, radius at the bubble apex, and neck radius all scaled with the capillary length. For high surfactant concentrations, however, θ0 was greatly reduced, and Vd and Rdmax decreased due to the combined effects of reduced θ0 and smaller σ. Lastly, we found that the surfactant had a negligible impact on the forces acting on the bubble, except for reducing their magnitudes, and had little effect on the dynamics of bubble pinch-off, except for reducing the time and length scales. Overall, our results provide a better understanding of bubble formation on complex surfaces in complex liquids.","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12191381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144483060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer. 一种新颖的自然启发优化算法：灰熊脂肪增加优化器。

IF 3.4 3区医学

Biomimetics Pub Date : 2025-06-07 DOI: 10.3390/biomimetics10060379

Moslem Dehghani, Mokhtar Aly, Jose Rodriguez, Ehsan Sheybani, Giti Javidi

{"title":"A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer.","authors":"Moslem Dehghani, Mokhtar Aly, Jose Rodriguez, Ehsan Sheybani, Giti Javidi","doi":"10.3390/biomimetics10060379","DOIUrl":"10.3390/biomimetics10060379","url":null,"abstract":"This paper introduces a novel nature-inspired optimization algorithm called the Grizzly Bear Fat Increase Optimizer (GBFIO). The GBFIO algorithm mimics the natural behavior of grizzly bears as they accumulate body fat in preparation for winter, drawing on their strategies of hunting, fishing, and eating grass, honey, etc. Hence, three mathematical steps are modeled and considered in the GBFIO algorithm to solve the optimization problem: (1) finding food sources (e.g., vegetables, fruits, honey, oysters), based on past experiences and olfactory cues; (2) hunting animals and protecting offspring from predators; and (3) fishing. Thirty-one standard benchmark functions and thirty CEC2017 test benchmark functions are applied to evaluate the performance of the GBFIO, such as unimodal, multimodal of high dimensional, fixed dimensional multimodal, and also the rotated and shifted benchmark functions. In addition, four constrained engineering design problems such as tension/compression spring design, welded beam design, pressure vessel design, and speed reducer design problems have been considered to show the efficiency of the proposed GBFIO algorithm in solving constrained problems. The GBFIO can successfully solve diverse kinds of optimization problems, as shown in the results of optimization of objective functions, especially in high dimension objective functions in comparison to other algorithms. Additionally, the performance of the GBFIO algorithm has been compared with the ability and efficiency of other popular optimization algorithms in finding the solutions. In comparison to other optimization algorithms, the GBFIO algorithm offers yields superior or competitive quasi-optimal solutions relative to other well-known optimization algorithms.","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12190834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144483033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Design Process Framework and Tools for Teaching and Practicing Biomimicry. 仿生教学与实践的设计过程框架与工具。

IF 3.4 3区医学

Biomimetics Pub Date : 2025-06-06 DOI: 10.3390/biomimetics10060376

Benjamin Linder, Jean Huang

引用次数: 0

An Enhanced Artificial Lemming Algorithm and Its Application in UAV Path Planning. 一种改进型人工Lemming算法及其在无人机路径规划中的应用。

IF 3.4 3区医学

Biomimetics Pub Date : 2025-06-06 DOI: 10.3390/biomimetics10060377

Xuemei Zhu, Chaochuan Jia, Jiangdong Zhao, Chunyang Xia, Wei Peng, Ji Huang, Ling Li

引用次数: 0

Multi-Agent Reinforcement Learning in Games: Research and Applications. 游戏中的多智能体强化学习：研究与应用。

IF 3.4 3区医学

Biomimetics Pub Date : 2025-06-06 DOI: 10.3390/biomimetics10060375

Haiyang Li, Ping Yang, Weidong Liu, Shaoqiang Yan, Xinyi Zhang, Donglin Zhu

{"title":"Multi-Agent Reinforcement Learning in Games: Research and Applications.","authors":"Haiyang Li, Ping Yang, Weidong Liu, Shaoqiang Yan, Xinyi Zhang, Donglin Zhu","doi":"10.3390/biomimetics10060375","DOIUrl":"10.3390/biomimetics10060375","url":null,"abstract":"Biological systems, ranging from ant colonies to neural ecosystems, exhibit remarkable self-organizing intelligence. Inspired by these phenomena, this study investigates how bio-inspired computing principles can bridge game-theoretic rationality and multi-agent adaptability. This study systematically reviews the convergence of multi-agent reinforcement learning (MARL) and game theory, elucidating the innovative potential of this integrated paradigm for collective intelligent decision-making in dynamic open environments. Building upon stochastic game and extensive-form game-theoretic frameworks, we establish a methodological taxonomy across three dimensions: value function optimization, policy gradient learning, and online search planning, thereby clarifying the evolutionary logic and innovation trajectories of algorithmic advancements. Focusing on complex smart city scenarios-including intelligent transportation coordination and UAV swarm scheduling-we identify technical breakthroughs in MARL applications for policy space modeling and distributed decision optimization. By incorporating bio-inspired optimization approaches, the investigation particularly highlights evolutionary computation mechanisms for dynamic strategy generation in search planning, alongside population-based learning paradigms for enhancing exploration efficiency in policy refinement. The findings reveal core principles governing how groups make optimal choices in complex environments while mapping the technological development pathways created by blending cross-disciplinary methods to enhance multi-agent systems.","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12190516/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144483074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Control of Bio-Inspired Joints for Legged Robots Driven by Shape Memory Alloy Wires. 形状记忆合金丝驱动足式机器人仿生关节设计与控制。

IF 3.4 3区医学

Biomimetics Pub Date : 2025-06-06 DOI: 10.3390/biomimetics10060378

Xiaojie Niu, Xiang Yao, Erbao Dong

引用次数: 0

A Bipedal Robotic Platform Leveraging Reconfigurable Locomotion Policies for Terrestrial, Aquatic, and Aerial Mobility. 利用可重构运动策略的两足机器人平台，用于陆地、水生和空中移动。

IF 3.4 3区医学

Biomimetics Pub Date : 2025-06-05 DOI: 10.3390/biomimetics10060374

Zijie Sun, Yangmin Li, Long Teng

{"title":"A Bipedal Robotic Platform Leveraging Reconfigurable Locomotion Policies for Terrestrial, Aquatic, and Aerial Mobility.","authors":"Zijie Sun, Yangmin Li, Long Teng","doi":"10.3390/biomimetics10060374","DOIUrl":"10.3390/biomimetics10060374","url":null,"abstract":"Biological systems can adaptively navigate multi-terrain environments via morphological and behavioral flexibility. While robotic systems increasingly achieve locomotion versatility in one or two domains, integrating terrestrial, aquatic, and aerial mobility into a single platform remains an engineering challenge. This work tackles this by introducing a bipedal robot equipped with a reconfigurable locomotion framework, enabling seven adaptive policies: (1) thrust-assisted jumping, (2) legged crawling, (3) balanced wheeling, (4) tricycle wheeling, (5) paddling-based swimming, (6) air-propelled drifting, and (7) quadcopter flight. Field experiments and indoor statistical tests validated these capabilities. The robot achieved a 3.7-m vertical jump via thrust forces counteracting gravitational forces. A unified paddling mechanism enabled seamless transitions between crawling and swimming modes, allowing amphibious mobility in transitional environments such as riverbanks. The crawling mode demonstrated the traversal on uneven substrates (e.g., medium-density grassland, soft sand, and cobblestones) while generating sufficient push forces for object transport. In contrast, wheeling modes prioritize speed and efficiency on flat terrain. The aquatic locomotion was validated through trials in static water, an open river, and a narrow stream. The flight mode was investigated with the assistance of the jumping mechanism. By bridging terrestrial, aquatic, and aerial locomotion, this platform may have the potential for search-and-rescue and environmental monitoring applications.","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12191364/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144483024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved Northern Goshawk Optimization Algorithm for Mural Image Segmentation. 一种改进的北方苍鹰优化算法用于壁画图像分割。

IF 3.4 3区医学

Biomimetics Pub Date : 2025-06-05 DOI: 10.3390/biomimetics10060373

Jianfeng Wang, Zuowen Bao, Hao Dong

{"title":"An Improved Northern Goshawk Optimization Algorithm for Mural Image Segmentation.","authors":"Jianfeng Wang, Zuowen Bao, Hao Dong","doi":"10.3390/biomimetics10060373","DOIUrl":"10.3390/biomimetics10060373","url":null,"abstract":"In the process of mural protection and restoration, using optimization algorithms for image segmentation is a common method for restoring mural details. However, existing optimization-based image segmentation methods often lack image segmentation quality. To alleviate the aforementioned issues, this paper proposes a mural image segmentation algorithm based on OPBNGO by integrating the Northern Goshawk Optimization (NGO) algorithm with the off-center learning strategy, partitioned learning strategy, and Bernstein-weighted learning strategy. In OPBNGO, firstly, the off-center learning strategy is proposed, which effectively improves the global search ability of the algorithm by utilizing biased center individuals. Secondly, the partitioned learning strategy is introduced, which achieves a better balance between the exploration and development phases by applying diverse learning methods to the population. Finally, the Bernstein-weighted learning strategy is proposed, which effectively improves the algorithm's development performance. Subsequently, the OPBNGO algorithm is applied to solve the image segmentation problem for eight mural images. Experimental results show that it achieves a winning rate of over 96.87% in terms of fitness function value, achieves a winning rate of over 93.75% in terms of FSIM, SSIM, and PSNR metrics, and can be considered a promising mural image segmentation algorithm.","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12190925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144483038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Neural Network Robust Control of FOG with Output Constraints. 带输出约束的光纤陀螺自适应神经网络鲁棒控制。

IF 3.4 3区医学

Biomimetics Pub Date : 2025-06-05 DOI: 10.3390/biomimetics10060372

Shangbo Liu, Baowang Lian, Jiajun Ma, Xiaokun Ding, Haiyan Li

{"title":"Adaptive Neural Network Robust Control of FOG with Output Constraints.","authors":"Shangbo Liu, Baowang Lian, Jiajun Ma, Xiaokun Ding, Haiyan Li","doi":"10.3390/biomimetics10060372","DOIUrl":"10.3390/biomimetics10060372","url":null,"abstract":"In this work, an adaptive robust control method based on Radial Basis Function Neural Network (RBFNN) is proposed. Inspired by the local response characteristics of biological neurons, this method can reduce the influence of nonlinear errors and unknown perturbations in the extreme working conditions of the aircraft, such as high dynamics and strong vibration, so as to achieve high tracking accuracy. In this method, the dynamic model of the nonlinear error of the fiber optic gyroscope is proposed, and then the unknown external interference observer is designed for the system to realize the estimation of the unknown disturbances. The controller design method combines the design of the adaptive law outside the finite approximation domain of the achievable condition design of the sliding mode surface, and adjusts the controller parameters online according to the conditions satisfied by the real-time error state, breaking through the limitation of the finite approximation domain of the traditional neural network. In the finite approximation domain, an online adaptive controller is constructed by using the universal approximation ability of RBFNN, so as to enhance the robustness to nonlinear errors and external disturbances. By designing the output constraint mechanism, the dynamic stability of the system is further guaranteed under the constraints, and finally its effectiveness is verified by simulation analysis, which provides a new solution for high-precision inertial navigation.","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12190539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144483035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0