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Open AccessArticle

A Novel Starfish Optimization Algorithm for Secure STAR-RIS Communications

by Mona Gafar, Shahenda Sarhan, Abdullah M. Shaheen and Ahmed S. Alwakeel

Biomimetics 2026, 11(4), 243; https://doi.org/10.3390/biomimetics11040243 - 3 Apr 2026

Viewed by 161

Abstract

This paper develops an intelligent Enhanced Starfish Optimization (ESFO) algorithm for optimizing a secure wireless communication infrastructure. The Starfish Optimization (SFO) algorithm is inspired by starfish biology, using the integrated modeling of the arm-based exploration, preying, and regeneration behaviors of starfish. To further [...] Read more.

This paper develops an intelligent Enhanced Starfish Optimization (ESFO) algorithm for optimizing a secure wireless communication infrastructure. The Starfish Optimization (SFO) algorithm is inspired by starfish biology, using the integrated modeling of the arm-based exploration, preying, and regeneration behaviors of starfish. To further enhance the exploitation capability of the standard Starfish Optimization (SFO), the proposed Enhanced Starfish Optimization (ESFO) integrates a fitness-based interacting mechanism within the exploitation phase. This innovative modification improves local search accuracy, preserves population diversity, and mitigates premature convergence without introducing additional control parameters. Moreover, the proposed Enhanced Starfish Optimization (ESFO) is designed for secure wireless transmission, which is considered one of the main topics in next-generation wireless network infrastructure. The investigated network addresses the use of Simultaneously Transmitting and Reflecting RIS (STAR-RIS) in the security of the physical layer. This implemented STAR-RIS has a coupled phase shift to create reflected and transmission links, unlike traditional Reconfigurable Intelligent Surface (RIS). In this regard, we create a safe beamforming architecture that optimizes both Base Station (BS) precoding vectors and STAR-RIS transmission/reflection coefficients. In order to validate the efficiency of the proposed Enhanced Starfish Optimization (ESFO) algorithm, it is compared to several benchmark optimizers such as standard Starfish Optimization (SFO), Dhole Optimizer (DO), Neural Network Algorithm (NNA), Crocodile Ambush Optimization Algorithm (CAOA), and white shark Optimizer (WSO). These comparisons include several scenarios based on the transmitted power threshold which is varied in the range of 20 to 70 dBm with step of 5 dBm. The simulation results show that the proposed Enhanced Star Fish Optimization (ESFO) algorithm consistently outperforms existing benchmark approaches. This study supports future intelligent communication infrastructures in terms of secrecy and achievable rates over a range of transmit power levels. In particular, ESFO improves performance by up to 20–25% while converging 40–50% faster than traditional optimization algorithms, demonstrating its usefulness and resilience in STAR-RIS-assisted secure communication systems. The suggested ESFO-enabled architecture outperforms standard RIS-based systems in terms of secrecy capacity, according to numerical studies, and low-resolution STAR-RIS phase-shifters are sufficient to ensure robust secrecy performance. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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25 pages, 3924 KB

Open AccessArticle

A Bio-Inspired Data-Driven Hybrid Optimization Framework for Task Unit Partition in Cruise Itinerary Planning

by Zixiang Zhang, Dening Song and Jinghua Li

Biomimetics 2026, 11(4), 239; https://doi.org/10.3390/biomimetics11040239 - 2 Apr 2026

Viewed by 169

Abstract

Personalized itinerary planning for large-scale passengers under resource constraints is a critical challenge in enhancing the operational efficiency and service quality of cruise tourism. Traditional clustering methods, which primarily rely on geometric similarity, often fail to address the intricate coupling between passenger preferences [...] Read more.

Personalized itinerary planning for large-scale passengers under resource constraints is a critical challenge in enhancing the operational efficiency and service quality of cruise tourism. Traditional clustering methods, which primarily rely on geometric similarity, often fail to address the intricate coupling between passenger preferences and finite venue capacities, lacking predictive capability for the ultimate planning quality. To overcome these limitations, this study proposes a novel bio-inspired data-driven hybrid optimization framework for the cruise itinerary planning task unit partition. The framework innovatively integrates a Genetic Balanced Clustering Algorithm (GBCA) for multi-objective passenger grouping, Kernel Principal Component Analysis (KPCA) for feature extraction from preference data, an improved Adaptive Spiral Flying Sparrow Search Algorithm (ASFSSA) for hyperparameter optimization, and a Kernel Extreme Learning Machine (KELM) for data-driven prediction of itinerary planning quality. This synergy enables the framework to dynamically allocate venue capacities based on group preferences and optimize partitioning towards maximizing overall benefits, ensuring load balance and fairness. Extensive experiments on simulated cruise scenarios demonstrate that the proposed framework significantly outperforms conventional methods, improving segmentation quality by at least 40% while exhibiting superior convergence speed and stability. This work provides a scalable, intelligent solution for complex resource-constrained scheduling problems, showcasing the effective application of bio-inspired data-driven methodologies in engineering optimization. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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26 pages, 3911 KB

Open AccessArticle

Parametric Optimization of VLM Panel Discretization Using Bio-Inspired Crayfish and Aquila Algorithms Coupled with Hybrid RSM-Based Ensemble Machine Learning Surrogate Models: A Case Study

by Yüksel Eraslan and Esmanur Şengün

Biomimetics 2026, 11(3), 204; https://doi.org/10.3390/biomimetics11030204 - 11 Mar 2026

Viewed by 443

Abstract

Fast and reliable aerodynamic predictions are crucial in the early phases of aircraft design, where a quick assessment of various configurations is required. In this context, the Vortex Lattice Method (VLM) is widely adopted due to its computational efficiency; however, its predictive accuracy [...] Read more.

Fast and reliable aerodynamic predictions are crucial in the early phases of aircraft design, where a quick assessment of various configurations is required. In this context, the Vortex Lattice Method (VLM) is widely adopted due to its computational efficiency; however, its predictive accuracy is highly sensitive to panel discretization strategies, which are often determined heuristically. This study proposes a bio-inspired optimization framework for VLM panel discretization and evaluates it through a systematic case study on a representative wing geometry. A grid-convergence analysis was initially carried out to ensure solution independence across various spanwise-to-chordwise panel ratios. Subsequently, a novel Hybrid Response Surface Methodology (HRSM), integrating Box–Behnken and Central Composite experimental designs, was employed to enable a more comprehensive exploration of the factor space while quantifying the effects of clustering parameters at the leading-edge, trailing-edge, root, and tip regions of the wing. The HRSM dataset was further utilized to train Ensemble Machine-Learning surrogate models, which were coupled with bio-inspired Crayfish and Aquila optimization algorithms, alongside a classical Genetic Algorithm (GA) as a performance benchmark, to identify the optimal discretization strategy and to enable a comparative assessment of their convergence behavior and robustness against the numerical noise of the ensemble-based landscape. Compared to base (i.e., uniform) panel distribution, the optimally clustered discretization enhanced overall aerodynamic prediction accuracy by approximately 33%, particularly at low angles of attack, while maintaining robust performance at higher angles. Both algorithms converged to similar minima; however, the Aquila algorithm achieved higher solution consistency, whereas the Crayfish algorithm exhibited greater dispersion despite faster convergence, revealing a multimodal optimization landscape. The variance decomposition revealed that trailing-edge clustering dominated aerodynamic accuracy at low angles of attack, contributing up to 90% of the total variance, whereas tip clustering became increasingly influential at higher angles, exceeding 30%, highlighting the need for adaptive discretization strategies to ensure reliable VLM-based aerodynamic analyses. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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41 pages, 8475 KB

Open AccessArticle

Clustering Performance Analysis Using Chaotic and Lévy Flight-Enhanced Black-Winged Kite Algorithms

by Taybe Alabed and Sema Servi

Biomimetics 2026, 11(3), 200; https://doi.org/10.3390/biomimetics11030200 - 9 Mar 2026

Viewed by 539

Abstract

Clustering is a fundamental unsupervised learning technique used to uncover hidden patterns in unlabeled data. Although metaheuristic algorithms have demonstrated effectiveness in clustering, many suffer from premature convergence and limited population diversity. This study employs the Black-Winged Kite Algorithm (BKA) and its enhanced [...] Read more.

Clustering is a fundamental unsupervised learning technique used to uncover hidden patterns in unlabeled data. Although metaheuristic algorithms have demonstrated effectiveness in clustering, many suffer from premature convergence and limited population diversity. This study employs the Black-Winged Kite Algorithm (BKA) and its enhanced variants, Chaotic BKA (CBKA), Lévy Flight-based BKA (LBKA), and Chaotic Levy BKA (CLBKA), to address these limitations in centroid-based clustering formulated as a Sum of Squared Errors (SSE) minimization problem. Chaotic logistic mapping improves search diversity and adaptability, while Levy flight introduces long-range exploration. In addition, Cauchy based perturbations are incorporated to enhance convergence stability. The algorithms are evaluated on sixteen UCI benchmark datasets, with 30 independent runs conducted under different population and iteration settings. Experimental results show that CLBKA consistently achieves superior clustering performance in terms of accuracy and stability. Statistical validation using the Friedman and Wilcoxon tests confirms significant performance differences, with CLBKA obtaining the lowest mean rank across configurations. The findings indicate that integrating chaotic dynamics and Levy flight mechanisms enhances clustering robustness and optimization efficiency. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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33 pages, 1846 KB

Open AccessArticle

Evaluating Bio-Inspired Metaheuristics for Dynamic Surgical Scheduling: A Resilient Three-Stage Flow Shop Model Under Stochastic Emergency Arrivals

by Marcelo Becerra-Rozas, Bady Gana, José Lara, Andres Leiva-Araos, Broderick Crawford, José M. Gómez Pulido, Cristian Contreras, José J. Caro-Miranda and Miguel García-Remesal

Biomimetics 2026, 11(3), 183; https://doi.org/10.3390/biomimetics11030183 - 3 Mar 2026

Viewed by 492

Abstract

Optimal surgical scheduling necessitates a strategic balance between elective efficiency and responsiveness to stochastic emergency arrivals. This study evaluates a Genetic Algorithm alongside discretized variants of Particle Swarm Optimization, the Secretary Bird Optimization Algorithm, and the Mantis Shrimp Optimization Algorithm. These algorithms are [...] Read more.

Optimal surgical scheduling necessitates a strategic balance between elective efficiency and responsiveness to stochastic emergency arrivals. This study evaluates a Genetic Algorithm alongside discretized variants of Particle Swarm Optimization, the Secretary Bird Optimization Algorithm, and the Mantis Shrimp Optimization Algorithm. These algorithms are assessed within a dynamic three-stage flexible flow shop model under no-buffer blocking constraints. Findings from 300 Monte Carlo replications demonstrate that while the Genetic Algorithm achieves peak global efficiency, discretized bio-inspired algorithms reach a comparable statistical efficiency frontier. Notably, the discretized Secretary Bird Optimization Algorithm facilitates superior emergency integration by maintaining natural capacity buffers, whereas the aggressive local optimization characteristic of alternative methods often triggers resource saturation in recovery units. These results indicate a potential recovery of 90 annual operating hours per theater.These results indicate a potential recovery of 90 annual operating hours per theater, representing a 6.7% increase in resource utilization efficiency. This improvement provides a critical data-driven capacity margin to mitigate the non-prioritized (Non-GES) surgical backlog in Chilean public hospitals. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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25 pages, 12476 KB

Open AccessArticle

Hybrid Neuro-Symbolic State-Space Modeling for Industrial Robot Calibration via Adaptive Wavelet Networks and PSO

by He Mao, Zhouyi Lai and Zhibin Li

Biomimetics 2026, 11(3), 171; https://doi.org/10.3390/biomimetics11030171 - 2 Mar 2026

Viewed by 378

Abstract

The absolute positioning accuracy of industrial manipulators is frequently bottlenecked by the interplay of geometric tolerances and complex, unmodeled non-geometric parameter drifts. Traditional static kinematic models, predicated on rigid-body assumptions, often struggle to characterize these state-dependent dynamic behaviors. To bridge this gap, this [...] Read more.

The absolute positioning accuracy of industrial manipulators is frequently bottlenecked by the interplay of geometric tolerances and complex, unmodeled non-geometric parameter drifts. Traditional static kinematic models, predicated on rigid-body assumptions, often struggle to characterize these state-dependent dynamic behaviors. To bridge this gap, this study introduces a PSO-Driven Neuro-Symbolic State-Space Framework incorporating Adaptive Wavelet Networks, drawing inspiration from two biological principles: the collective swarm intelligence observed in bird flocking and fish schooling, and the localized receptive field structure of mammalian visual cortex neurons. By reformulating calibration as a latent state estimation problem, we model kinematic parameters as stochastic states. Crucially, the observation model fuses symbolic Denavit–Hartenberg (D–H) predictions with an Adaptive Wavelet Network (AWNN). The AWNN utilizes Mexican Hat kernels, whose morphology mirrors the center-surround antagonism of cortical receptive fields, and leverages their precise time–frequency localization to effectively learn complex, configuration-dependent residuals. The framework employs a robust decoupled strategy. First, Particle Swarm Optimization (PSO) executes meta-optimization to autonomously determine hyperparameters, thereby mitigating initialization sensitivity. Second, a recursive inference engine estimates the hybrid states. Third, a global batch optimization refines the symbolic parameters against a frozen non-geometric error field. Experimental validation on an ABB IRB 120 robot (400 datasets) yielded a test RMSE of 0.73 mm. Compared to the standard Levenberg–Marquardt method, our approach reduced the RMSE by 40.16% and the maximum error by 35.71% (down to 0.99 mm). Moreover, it outperforms the state-of-the-art RPSO-DCFNN baseline by 12.05% while maintaining high computational efficiency (convergence within 20.15 s). These findings underscore the superiority of the proposed bio-inspired state-space fusion strategy for high-precision industrial applications. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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34 pages, 2555 KB

Open AccessArticle

An Evolutionary-Algorithm-Driven Efficient Temporal Convolutional Network for Radar Image Extrapolation

by Peiyang Wei, Changyuan Fan, Yuyan Wang, Tianlong Li, Jianhong Gan, Can Hu and Zhibin Li

Biomimetics 2026, 11(2), 122; https://doi.org/10.3390/biomimetics11020122 - 6 Feb 2026

Cited by 1 | Viewed by 469

Abstract

Radar image extrapolation serves as a fundamental methodology in meteorological forecasting, facilitating precise short-term weather prediction through spatiotemporal sequence analysis. Conventional approaches remain constrained by progressive image degradation and artifacts, substantially limiting operational forecasting reliability. This research introduces E-HEOA—an enhanced deep learning architecture [...] Read more.

Radar image extrapolation serves as a fundamental methodology in meteorological forecasting, facilitating precise short-term weather prediction through spatiotemporal sequence analysis. Conventional approaches remain constrained by progressive image degradation and artifacts, substantially limiting operational forecasting reliability. This research introduces E-HEOA—an enhanced deep learning architecture with integrated hyperparameter optimization. Our framework incorporates two fundamental innovations: (a) a hybrid metaheuristic optimizer merging a Gaussian-mutated ESOA and Cauchy-mutated HEOA for autonomous learning rate and dropout optimization and (b) embedded ConvLSTM2D modules for enhanced spatiotemporal feature preservation. Experimental validation on 170,000 radar echo samples demonstrates superior performance, demonstrating considerable enhancement in almost all aspects relative to the baseline model while establishing new state-of-the-art benchmarks in prediction fidelity, convergence efficiency, and structural similarity metrics. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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30 pages, 7150 KB

Open AccessArticle

Research on Gas Pipeline Leakage Prediction Model Based on Physics-Aware GL-TransLSTM

by Chunjiang Wu, Haoyu Lu, Dianming Liu, Chen Wang, Jianhong Gan and Zhibin Li

Biomimetics 2025, 10(11), 743; https://doi.org/10.3390/biomimetics10110743 - 5 Nov 2025

Cited by 3 | Viewed by 880

Abstract

Natural gas pipeline leak monitoring suffers from severe environmental noise, non-stationary signals, and complex multi-source variable couplings, limiting prediction accuracy and robustness. Inspired by biological perceptual systems, particularly their multimodal integration and dynamic attention allocation, we propose GL-TransLSTM, a biomimetic hybrid deep learning [...] Read more.

Natural gas pipeline leak monitoring suffers from severe environmental noise, non-stationary signals, and complex multi-source variable couplings, limiting prediction accuracy and robustness. Inspired by biological perceptual systems, particularly their multimodal integration and dynamic attention allocation, we propose GL-TransLSTM, a biomimetic hybrid deep learning model. It synergistically combines Transformer’s global self-attention (emulating selective focus) and LSTM’s gated memory (mimicking neural temporal retention). The architecture incorporates a multimodal fusion pipeline; raw sensor data are first decomposed via CEEMDAN to extract multi-scale features, then processed by an enhanced LSTM-Transformer backbone. A novel physics-informed gated attention mechanism embeds gas diffusion dynamics into attention weights, while an adaptive sliding window adjusts temporal granularity. This study makes evaluations on an industrial dataset with methane concentration, temperature, and pressure, GL-TransLSTM achieves 99.93% accuracy, 99.86% recall, and 99.89% F1-score, thereby significantly outperforming conventional LSTM and Transformer-LSTM baselines. Experimental results demonstrate that the proposed biomimetic framework substantially enhances modeling capacity and generalization for non-stationary signals in noisy and complex industrial environments through multi-scale fusion, physics-guided learning, and bio-inspired architectural synergy. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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17 pages, 2784 KB

Open AccessArticle

Water Body Identification from Satellite Images Using a Hybrid Evolutionary Algorithm-Optimized U-Net Framework

by Yue Yuan, Peiyang Wei, Zhixiang Qi, Xun Deng, Ji Zhang, Jianhong Gan, Tinghui Chen and Zhibin Li

Biomimetics 2025, 10(11), 732; https://doi.org/10.3390/biomimetics10110732 - 1 Nov 2025

Viewed by 1054

Abstract

Accurate and automated identification of water bodies from satellite imagery is critical for environmental monitoring, water resource management, and disaster response. Current deep learning approaches, however, suffer from a strong dependence on manual hyperparameter tuning, which limits their automation capability and robustness in [...] Read more.

Accurate and automated identification of water bodies from satellite imagery is critical for environmental monitoring, water resource management, and disaster response. Current deep learning approaches, however, suffer from a strong dependence on manual hyperparameter tuning, which limits their automation capability and robustness in complex, multi-scale scenarios. To overcome this limitation, this study proposes a fully automated segmentation framework that synergistically integrates an enhanced U-Net model with a novel hybrid evolutionary optimization strategy. Extensive experiments on public Kaggle and Sentinel-2 datasets demonstrate the superior performance of our method, which achieves a Pixel Accuracy of 96.79% and an F1-Score of 94.75, outperforming various mainstream baseline models by over 10% in key metrics. The framework effectively addresses the class imbalance problem and enhances feature representation without human intervention. This work provides a viable and efficient path toward fully automated remote sensing image analysis, with significant potential for application in large-scale water resource monitoring, dynamic environmental assessment, and emergency disaster management. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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15 pages, 1516 KB

Open AccessArticle

Bio-Inspired Multi-Granularity Model for Rice Pests and Diseases Named Entity Recognition in Chinese

by Zhan Tang, Xiaoyu Lu, Enli Liu, Yan Zhong and Xiaoli Peng

Biomimetics 2025, 10(10), 676; https://doi.org/10.3390/biomimetics10100676 - 8 Oct 2025

Viewed by 778

Abstract

Rice, as one of the world’s four major staple crops, is frequently threatened by pests and diseases during its growth. With the rapid expansion of agricultural information data, the effective management and utilization of such data have become crucial for the development of [...] Read more.

Rice, as one of the world’s four major staple crops, is frequently threatened by pests and diseases during its growth. With the rapid expansion of agricultural information data, the effective management and utilization of such data have become crucial for the development of agricultural informatization. Named entity recognition technology offers precise support for the early prevention and control of crop pests and diseases. However, entity recognition for rice pests and diseases faces challenges such as structural complexity and prevalent nesting issues. Inspired by biological visual mechanisms, we propose a deep learning model capable of extracting multi-granularity features. Text representations are encoded using BERT, and the model enhances its ability to capture nested boundary information through multi-granularity convolutional neural networks (CNNs). Finally, sequence modeling and labeling are performed using a bidirectional long short-term memory network (BiLSTM) combined with a conditional random field (CRF). Experimental results demonstrate that the proposed model effectively identifies entities related to rice diseases and pests, achieving an F1 score of 91.74% on a self-constructed dataset. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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26 pages, 1947 KB

Open AccessArticle

Active Suspension Control for Improved Ride Comfort and Vehicle Performance Using HHO-Based Type-I and Type-II Fuzzy Logic

by Tayfun Abut, Enver Salkim and Harun Tugal

Biomimetics 2025, 10(10), 673; https://doi.org/10.3390/biomimetics10100673 - 7 Oct 2025

Cited by 6 | Viewed by 1508

Abstract

This study focuses on improving the control system of vehicle suspension, which is critical for optimizing driving dynamics and enhancing passenger comfort. Traditional passive suspension systems are limited in their ability to effectively mitigate road-induced vibrations, often resulting in compromised ride quality and [...] Read more.

This study focuses on improving the control system of vehicle suspension, which is critical for optimizing driving dynamics and enhancing passenger comfort. Traditional passive suspension systems are limited in their ability to effectively mitigate road-induced vibrations, often resulting in compromised ride quality and vehicle handling. To overcome these limitations, this work explores the application of active suspension control strategies aimed at improving both comfort and performance. Type-I and Type-II Fuzzy Logic Control (FLC) methods were designed and implemented to enhance vehicle stability and ride quality. The Harris Hawks Optimization (HHO) algorithm was employed to optimize the membership function parameters of both fuzzy control types. The system was tested under two distinct road disturbance inputs to evaluate performance. The designed control methods were evaluated in simulations where results demonstrated that the proposed active control approaches significantly outperformed the passive suspension system in terms of vibration reduction. Specifically, the Type-II FLC achieved a 54.7% reduction in vehicle body displacement and a 76.8% reduction in acceleration for the first road input, while improvements of 75.2% and 72.8% were recorded, respectively, for the second input. Performance was assessed using percentage-based metrics and Root Mean Square Error (RMSE) criteria. Numerical and graphical analyses of suspension deflection and tire deformation further confirm that the proposed control strategies substantially enhance both ride comfort and vehicle handling. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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18 pages, 2042 KB

Open AccessArticle

Hybrid Algorithms Based on Two Evolutionary Computations for Image Classification

by Peiyang Wei, Rundong Zou, Jianhong Gan and Zhibin Li

Biomimetics 2025, 10(8), 544; https://doi.org/10.3390/biomimetics10080544 - 19 Aug 2025

Cited by 3 | Viewed by 903

Abstract

Convolutional neural networks (CNNs) and their improved models (like DenseNet-121) have achieved significant results in image classification tasks. However, the performance of these models is still constrained by issues such as hyperparameter optimization and gradient vanishing and exploding. Owing to their unique exploration [...] Read more.

Convolutional neural networks (CNNs) and their improved models (like DenseNet-121) have achieved significant results in image classification tasks. However, the performance of these models is still constrained by issues such as hyperparameter optimization and gradient vanishing and exploding. Owing to their unique exploration and exploitation capabilities, evolutionary algorithms offer new avenues for addressing these problems. Simultaneously, to prevent these algorithms from falling into a local optimum during the search process, this study designs a novel interpolation algorithm. To achieve better image classification performance, thus enhancing classification accuracy and boosting model stability, this paper utilizes a hybrid algorithm based on the horned lizard algorithm with quadratic interpolation and the giant armadillo optimization with Newton interpolation (HGAO) to optimize the hyperparameters of DenseNet-121. It is applied to five datasets spanning different domains. The learning rate and dropout rate have notable impacts on the outcomes of the DenseNet-121 model, which are chosen as the hyperparameters to be optimized. Experiments are conducted using the HGAO algorithm on five image datasets and compared with nine state-of-the-art algorithms. The performance of the model is evaluated based on accuracy, precision, recall, and F1-score metrics. The experimental results reveal that the combination of hyperparameters becomes more reasonable after optimization with the HGAO algorithm, thus providing a crucial improvement. In the comparative experiments, the accuracy of the image classification on the training set increased by up to 0.5%, with a maximum reduction in loss of 0.018. On the test set, the accuracy rose by 0.5%, and the loss decreased by 54 points. The HGAO algorithm provides an effective solution for optimizing the DenseNet-121 model. The designed method boosts classification accuracy and model stability, which also dramatically augments hyperparameter optimization effects and resolves gradient difficulties. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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44 pages, 4172 KB

Open AccessArticle

A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer

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

Biomimetics 2025, 10(6), 379; https://doi.org/10.3390/biomimetics10060379 - 7 Jun 2025

Cited by 4 | Viewed by 2736

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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23 pages, 2720 KB

Open AccessArticle

Binary Particle Swarm Optimization with Manta Ray Foraging Learning Strategies for High-Dimensional Feature Selection

by Jianhua Liu, Yuxiang Chen and Shanglong Li

Biomimetics 2025, 10(5), 315; https://doi.org/10.3390/biomimetics10050315 - 13 May 2025

Cited by 5 | Viewed by 1337

Abstract

High-dimensional feature selection is one of the key problems of big data analysis. The binary particle swarm optimization (BPSO) method, when used to achieve feature selection for high-dimensional data problems, can get stuck in local optima, leading to reduced search efficiency and inferior [...] Read more.

High-dimensional feature selection is one of the key problems of big data analysis. The binary particle swarm optimization (BPSO) method, when used to achieve feature selection for high-dimensional data problems, can get stuck in local optima, leading to reduced search efficiency and inferior feature selection results. This paper proposes a novel BPSO method with manta ray foraging learning strategies (BPSO-MRFL) to address the challenges of high-dimensional feature selection tasks. The BPSO-MRFL algorithm draws inspiration from the manta ray foraging optimization (MRFO) algorithm and incorporates several distinctive search strategies to enhance its efficiency and effectiveness. These search strategies include chain learning, cyclone learning, and somersault learning. Chain learning allows particles to learn from each other and share information more effectively in order to improve the social learning ability of the population. Cyclone learning introduces a gradual increase over iterations, which helps the BPSO-MRFL algorithm to transition smoothly from exploratory searching to exploitative searching, and it creates a balance between exploration and exploitation. Somersault learning enables particles to adaptively search within a changing search range and allows the algorithm to fine-tune the selected features, which enhances the algorithm’s local search ability and improves the quality of the selected subset. The proposed BPSO-MRFL algorithm was evaluated using 10 high-dimensional small-sample gene expression datasets. The results demonstrate that the proposed BPSO-MRFL algorithm achieves enhanced classification accuracy and feature reduction compared to traditional feature selection methods. Additionally, it exhibits competitive performance compared to other advanced feature selection methods. The BPSO-MRFL algorithm presents a promising approach to feature selection in high-dimensional data mining tasks. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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28 pages, 2214 KB

Open AccessArticle

Efficient Online Controller Tuning for Omnidirectional Mobile Robots Using a Multivariate-Multitarget Polynomial Prediction Model and Evolutionary Optimization

by Alam Gabriel Rojas-López, Miguel Gabriel Villarreal-Cervantes, Alejandro Rodríguez-Molina and Jesús Aldo Paredes-Ballesteros

Biomimetics 2025, 10(2), 114; https://doi.org/10.3390/biomimetics10020114 - 14 Feb 2025

Cited by 1 | Viewed by 1718

Abstract

The growing reliance on mobile robots has resulted in applications where users have limited or no control over operating conditions. These applications require advanced controllers to ensure the system’s performance by dynamically changing its parameters. Nowadays, online bioinspired controller tuning approaches are among [...] Read more.

The growing reliance on mobile robots has resulted in applications where users have limited or no control over operating conditions. These applications require advanced controllers to ensure the system’s performance by dynamically changing its parameters. Nowadays, online bioinspired controller tuning approaches are among the most successful and innovative tools for dealing with uncertainties and disturbances. Nevertheless, these bioinspired approaches present a main limitation in real-world applications due to the extensive computational resources required in their exhaustive search when evaluating the controller tuning of complex dynamics. This paper develops an online bioinspired controller tuning approach leveraging a surrogate modeling strategy for an omnidirectional mobile robot controller. The polynomial response surface method is incorporated as an identification stage to model the system and predict its behavior in the tuning stage of the indirect adaptive approach. The comparative analysis concerns state-of-the-art controller tuning approaches, such as online, offline robust, and offline non-robust approaches, based on bioinspired optimization. The results show that the proposal reduces its computational load by up to

62.85 %

while maintaining the controller performance regarding the online approach under adverse uncertainties and disturbances. The proposal also increases the controller performance by up to

93 %

compared to offline tuning approaches. Then, the proposal retains its competitiveness on mobile robot systems under adverse conditions, while other controller tuning approaches drop it. Furthermore, a posterior comparison against another surrogate tuning approach based on Gaussian process regression corroborates the proposal as the best online controller tuning approach by reducing the competitor’s computational load by up to

91.37 %

while increasing its performance by

63 %

. Hence, the proposed controller tuning approach decreases the execution time to be applied in the evolution of the control system without deteriorating the closed-loop performance. To the best of the authors’ knowledge, this is the first time that such a controller tuning strategy has been tested on an omnidirectional mobile robot. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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22 pages, 4829 KB

Open AccessEditor’s ChoiceArticle

Robot Task-Constrained Optimization and Adaptation with Probabilistic Movement Primitives

by Guanwen Ding, Xizhe Zang, Xuehe Zhang, Changle Li, Yanhe Zhu and Jie Zhao

Biomimetics 2024, 9(12), 738; https://doi.org/10.3390/biomimetics9120738 - 3 Dec 2024

Cited by 1 | Viewed by 1837

Abstract

Enabling a robot to learn skills from a human and adapt to different task scenarios will enable the use of robots in manufacturing to improve efficiency. Movement Primitives (MPs) are prominent tools for encoding skills. This paper investigates how to learn MPs from [...] Read more.

Enabling a robot to learn skills from a human and adapt to different task scenarios will enable the use of robots in manufacturing to improve efficiency. Movement Primitives (MPs) are prominent tools for encoding skills. This paper investigates how to learn MPs from a small number of human demonstrations and adapt to different task constraints, including waypoints, joint limits, virtual walls, and obstacles. Probabilistic Movement Primitives (ProMPs) model movements with distributions, thus providing the robot with additional freedom for task execution. We provide the robot with three modes to move, with only one human demonstration required for each mode. We propose an improved via-point generalization method to generalize smooth trajectories with encoded ProMPs. In addition, we present an effective task-constrained optimization method that incorporates all task constraints analytically into a probabilistic framework. We separate ProMPs as Gaussians at each timestep and minimize Kullback–Leibler (KL) divergence, with a gradient ascent–descent algorithm performed to obtain optimized ProMPs. Given optimized ProMPs, we outline a unified robot movement adaptation method for extending from a single obstacle to multiple obstacles. We validated our approach with a 7-DOF Xarm robot using a series of movement adaptation experiments. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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20 pages, 3256 KB

Open AccessArticle

Application of Real-Time Palm Imaging with Nelder–Mead Particle Swarm Optimization/Regression Algorithms for Non-Contact Blood Pressure Detection

by Te-Jen Su, Ya-Chung Hung, Wei-Hong Lin, Wen-Rong Yang, Qian-Yi Zhuang, Yan-Xiang Fei and Shih-Ming Wang

Biomimetics 2024, 9(11), 713; https://doi.org/10.3390/biomimetics9110713 - 20 Nov 2024

Viewed by 1649

Abstract

In response to the rising prevalence of hypertension due to lifestyle changes, this study introduces a novel approach for non-contact blood pressure (BP) monitoring. Recognizing the “silent killer” nature of hypertension, this research focuses on developing accessible, non-invasive BP measurement methods. This study [...] Read more.

In response to the rising prevalence of hypertension due to lifestyle changes, this study introduces a novel approach for non-contact blood pressure (BP) monitoring. Recognizing the “silent killer” nature of hypertension, this research focuses on developing accessible, non-invasive BP measurement methods. This study compares two distinct non-contact BP measurement approaches: one combining the Nelder–Mead simplex method with particle swarm optimization (NM-PSO) and the other using machine learning regression analysis. In the NM-PSO method, a standard webcam captures continuous images of the palm, extracting physiological data through light wave reflection and employing independent component analysis (ICA) to remove noise artifacts. The NM-PSO achieves a verified root mean square error (RMSE) of 2.71 mmHg for systolic blood pressure (SBP) and 3.42 mmHg for diastolic blood pressure (DBP). Alternatively, the regression method derives BP values through machine learning-based regression formulas, resulting in an RMSE of 2.88 mmHg for SBP and 2.60 mmHg for DBP. Both methods enable fast, accurate, and convenient BP measurement within 10 s, suitable for home use. This study demonstrates a cost-effective solution for non-contact BP monitoring and highlights each method’s advantages. The NM-PSO approach emphasizes optimization in noise handling, while the regression method leverages formulaic efficiency in BP estimation. These results offer a biomimetic approach that could replace traditional contact-based BP measurement devices, contributing to enhanced accessibility in hypertension management. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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19 pages, 9978 KB

Open AccessArticle

Analysis of Cushioned Landing Strategies of Cats Based on Posture Estimation

by Li Zhang, Liangliang Han, Haohang Liu, Rui Shi, Meiyang Zhang, Weijun Wang and Xuyan Hou

Biomimetics 2024, 9(11), 691; https://doi.org/10.3390/biomimetics9110691 - 13 Nov 2024

Viewed by 2947

Abstract

This article addresses the challenge of minimizing landing impacts for legged space robots during on-orbit operations. Inspired by the agility of cats, we investigate the role of forelimbs in the landing process. By identifying the kinematic chain of the cat skeleton and tracking [...] Read more.

This article addresses the challenge of minimizing landing impacts for legged space robots during on-orbit operations. Inspired by the agility of cats, we investigate the role of forelimbs in the landing process. By identifying the kinematic chain of the cat skeleton and tracking it using animal posture estimation, we derive the cushioning strategy that cats use to handle landing impacts. The results indicate that the strategy effectively transforms high-intensity impacts into prolonged low-intensity impacts, thereby safeguarding the brain and internal organs. We adapt this cushioning strategy for robotic platforms through reasonable assumptions and simplifications. Simulations are conducted in both gravitational and zero gravity environments, demonstrating that the optimized strategy not only reduces ground impact and prolongs the cushioning duration but also effectively suppresses the robot’s rebound. In zero gravity, the strategy enhances stable attachment to target surfaces. This research introduces a novel biomimetic control strategy for landing control in the on-orbit operations of space robots. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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Graphical abstract

16 pages, 3840 KB

Open AccessArticle

Oxygen-Plasma-Treated Al/TaO_X/Al Resistive Memory for Enhanced Synaptic Characteristics

by Gyeongpyo Kim, Seoyoung Park, Minsuk Koo and Sungjun Kim

Biomimetics 2024, 9(9), 578; https://doi.org/10.3390/biomimetics9090578 - 23 Sep 2024

Cited by 5 | Viewed by 1928

Abstract

In this study, we investigate the impact of O₂ plasma treatment on the performance of Al/TaO_X/Al-based resistive random-access memory (RRAM) devices, focusing on applications in neuromorphic systems. Comparative analysis using scanning electron microscopy and X-ray photoelectron spectroscopy confirmed the differences [...] Read more.

In this study, we investigate the impact of O₂ plasma treatment on the performance of Al/TaO_X/Al-based resistive random-access memory (RRAM) devices, focusing on applications in neuromorphic systems. Comparative analysis using scanning electron microscopy and X-ray photoelectron spectroscopy confirmed the differences in chemical composition between O₂-plasma-treated and untreated RRAM cells. Direct-current measurements showed that O₂-plasma-treated RRAM cells exhibited significant improvements over untreated RRAM cells, including higher on/off ratios, improved uniformity and distribution, longer retention times, and enhanced durability. The conduction mechanism is investigated by current–voltage (I–V) curve fitting. In addition, paired-pulse facilitation (PPF) is observed using partial short-term memory. Furthermore, 3- and 4-bit weight tuning with auto-pulse-tuning algorithms was achieved to improve the controllability of the synapse weight for the neuromorphic system, maintaining retention times exceeding 10³ s in the multiple states. Neuromorphic simulation with an MNIST dataset is conducted to evaluate the synaptic device. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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22 pages, 1097 KB

Open AccessArticle

Virtual Simulation-Based Optimization for Assembly Flow Shop Scheduling Using Migratory Bird Algorithm

by Wen-Bin Zhao, Jun-Han Hu and Zi-Qiao Tang

Biomimetics 2024, 9(9), 571; https://doi.org/10.3390/biomimetics9090571 - 21 Sep 2024

Cited by 4 | Viewed by 1784

Abstract

As industrial informatization progresses, virtual simulation technologies are increasingly demonstrating their potential in industrial applications. These systems utilize various sensors to capture real-time factory data, which are then transmitted to servers via communication interfaces to construct corresponding digital models. This integration facilitates tasks [...] Read more.

As industrial informatization progresses, virtual simulation technologies are increasingly demonstrating their potential in industrial applications. These systems utilize various sensors to capture real-time factory data, which are then transmitted to servers via communication interfaces to construct corresponding digital models. This integration facilitates tasks such as monitoring and prediction, enabling more accurate and convenient production scheduling and forecasting. This is particularly significant for flexible or mixed-flow production modes. Bionic optimization algorithms have demonstrated strong performance in factory scheduling and operations. Centered around these algorithms, researchers have explored various strategies to enhance efficiency and optimize processes within manufacturing environments.This study introduces an efficient migratory bird optimization algorithm designed to address production scheduling challenges in an assembly shop with mold quantity constraints. The research aims to minimize the maximum completion time in a batch flow mixed assembly flow shop scheduling problem, incorporating variable batch partitioning strategies. A tailored virtual simulation framework supports this objective. The algorithm employs a two-stage encoding mechanism for batch partitioning and sequencing, adapted to the unique constraints of each production stage. To enhance the search performance of the neighborhood structure, the study identifies and analyzes optimization strategies for batch partitioning and sequencing, and incorporates an adaptive neighborhood structure adjustment strategy. A competition mechanism is also designed to enhance the algorithm’s optimization efficiency. Simulation experiments of varying scales demonstrate the effectiveness of the variable batch partitioning strategy, showing a 5–6% improvement over equal batch strategies. Results across different scales and parameters confirm the robustness of the algorithm. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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21 pages, 1175 KB

Open AccessArticle

Two Acceleration-Layer Configuration Amendment Schemes of Redundant Robot Arms Based on Zhang Neurodynamics Equivalency

by Zanyu Tang, Mingzhi Mao, Yunong Zhang and Ning Tan

Biomimetics 2024, 9(7), 435; https://doi.org/10.3390/biomimetics9070435 - 17 Jul 2024

Cited by 5 | Viewed by 1577

Abstract

Two innovative acceleration-layer configuration amendment (CA) schemes are proposed to achieve the CA of constrained redundant robot arms. Specifically, by applying the Zhang neurodynamics equivalency (ZNE) method, an acceleration-layer CA performance indicator is derived theoretically. To obtain a unified-layer inequality constraint by transforming [...] Read more.

Two innovative acceleration-layer configuration amendment (CA) schemes are proposed to achieve the CA of constrained redundant robot arms. Specifically, by applying the Zhang neurodynamics equivalency (ZNE) method, an acceleration-layer CA performance indicator is derived theoretically. To obtain a unified-layer inequality constraint by transforming from angle-layer and velocity-layer constraints to acceleration-layer constraints, five theorems and three corollaries are theoretically derived and rigorously proved. Then, together with the unified acceleration-layer bound constraint, an enhanced acceleration-layer CA scheme specially considering three-layer time-variant physical limits is proposed, and a simplified acceleration-layer CA scheme considering three-layer time-invariant physical limits is also proposed. The proposed CA schemes are finally formulated in the form of standard quadratic programming and are solved by a projection neurodynamics solver. Moreover, comparative simulative experiments based on a four-link planar arm and a UR3 spatial arm are performed to verify the efficacy and superiority of the proposed CA schemes. At last, physical experiments are conducted on a real Kinova Jaco2 arm to substantiate the practicability of the proposed CA schemes. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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Review

Jump to: Research

31 pages, 555 KB

Open AccessReview

Advances in Zeroing Neural Networks: Bio-Inspired Structures, Performance Enhancements, and Applications

by Yufei Wang, Cheng Hua and Ameer Hamza Khan

Biomimetics 2025, 10(5), 279; https://doi.org/10.3390/biomimetics10050279 - 29 Apr 2025

Cited by 3 | Viewed by 1403

Abstract

Zeroing neural networks (ZNN), as a specialized class of bio-Iinspired neural networks, emulate the adaptive mechanisms of biological systems, allowing for continuous adjustments in response to external variations. Compared to traditional numerical methods and common neural networks (such as gradient-based and recurrent neural [...] Read more.

Zeroing neural networks (ZNN), as a specialized class of bio-Iinspired neural networks, emulate the adaptive mechanisms of biological systems, allowing for continuous adjustments in response to external variations. Compared to traditional numerical methods and common neural networks (such as gradient-based and recurrent neural networks), this adaptive capability enables the ZNN to rapidly and accurately solve time-varying problems. By leveraging dynamic zeroing error functions, the ZNN exhibits distinct advantages in addressing complex time-varying challenges, including matrix inversion, nonlinear equation solving, and quadratic optimization. This paper provides a comprehensive review of the evolution of ZNN model formulations, with a particular focus on single-integral and double-integral structures. Additionally, we systematically examine existing nonlinear activation functions, which play a crucial role in determining the convergence speed and noise robustness of ZNN models. Finally, we explore the diverse applications of ZNN models across various domains, including robot path planning, motion control, multi-agent coordination, and chaotic system regulation. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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22 pages, 1932 KB

Open AccessReview

Smart Nursing Wheelchairs: A New Trend in Assisted Care and the Future of Multifunctional Integration

by Zhewen Zhang, Peng Xu, Chengjia Wu and Hongliu Yu

Biomimetics 2024, 9(8), 492; https://doi.org/10.3390/biomimetics9080492 - 14 Aug 2024

Cited by 15 | Viewed by 6058

Abstract

As a significant technological innovation in the fields of medicine and geriatric care, smart care wheelchairs offer a novel approach to providing high-quality care services and improving the quality of care. The aim of this review article is to examine the development, applications [...] Read more.

As a significant technological innovation in the fields of medicine and geriatric care, smart care wheelchairs offer a novel approach to providing high-quality care services and improving the quality of care. The aim of this review article is to examine the development, applications and prospects of smart nursing wheelchairs, with particular emphasis on their assistive nursing functions, multiple-sensor fusion technology, and human–machine interaction interfaces. First, we describe the assistive functions of nursing wheelchairs, including position changing, transferring, bathing, and toileting, which significantly reduce the workload of nursing staff and improve the quality of care. Second, we summarized the existing multiple-sensor fusion technology for smart nursing wheelchairs, including LiDAR, RGB-D, ultrasonic sensors, etc. These technologies give wheelchairs autonomy and safety, better meeting patients’ needs. We also discussed the human–machine interaction interfaces of intelligent care wheelchairs, such as voice recognition, touch screens, and remote controls. These interfaces allow users to operate and control the wheelchair more easily, improving usability and maneuverability. Finally, we emphasized the importance of multifunctional-integrated care wheelchairs that integrate assistive care, navigation, and human–machine interaction functions into a comprehensive care solution for users. We are looking forward to the future and assume that smart nursing wheelchairs will play an increasingly important role in medicine and geriatric care. By integrating advanced technologies such as enhanced artificial intelligence, intelligent sensors, and remote monitoring, we expect to further improve patients’ quality of care and quality of life. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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