Natural Language Processing(NLP)has become essential in text classification,sentiment analysis,machine translation,and speech recognition applications.As these tasks become complex,traditionalmachine learning and deep...Natural Language Processing(NLP)has become essential in text classification,sentiment analysis,machine translation,and speech recognition applications.As these tasks become complex,traditionalmachine learning and deep learning models encounter challenges with optimization,parameter tuning,and handling large-scale,highdimensional data.Bio-inspired algorithms,which mimic natural processes,offer robust optimization capabilities that can enhance NLP performance by improving feature selection,optimizing model parameters,and integrating adaptive learning mechanisms.This review explores the state-of-the-art applications of bio-inspired algorithms—such as Genetic Algorithms(GA),Particle Swarm Optimization(PSO),and Ant Colony Optimization(ACO)—across core NLP tasks.We analyze their comparative advantages,discuss their integration with neural network models,and address computational and scalability limitations.Through a synthesis of existing research,this paper highlights the unique strengths and current challenges of bio-inspired approaches in NLP,offering insights into hybrid models and lightweight,resource-efficient adaptations for real-time processing.Finally,we outline future research directions that emphasize the development of scalable,effective bio-inspired methods adaptable to evolving data environments.展开更多
Metaheuristics are commonly used in various fields,including real-life problem-solving and engineering applications.The present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System A...Metaheuristics are commonly used in various fields,including real-life problem-solving and engineering applications.The present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm(ACSA).The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this process.The work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions,identified as classical benchmark functions.The method was subsequently examined by application to 12 CEC 2022 benchmark problems of different complexities.Furthermore,the paper evaluates ACSA in comparison to 64 metaheuristic methods that are derived from different approaches,including evolutionary,human,physics,and swarm-based.Subsequently,a sequence of statistical tests was undertaken to examine the superiority of the suggested algorithm in comparison to the 7 most widely used algorithms in the existing literature.The results show that the ACSA strategy can quickly reach the global optimum,avoid getting trapped in local optima,and effectively maintain a balance between exploration and exploitation.ACSA outperformed 42 algorithms statistically,according to post-hoc tests.It also outperformed 9 algorithms quantitatively.The study concludes that ACSA offers competitive solutions in comparison to popüler methods.展开更多
Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps.However,these approaches endure performance degradation as problem complexity increases,often resu...Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps.However,these approaches endure performance degradation as problem complexity increases,often resulting in lengthy search times to find an optimal solution.This limitation is particularly critical for real-world applications like autonomous off-road vehicles,where highquality path computation is essential for energy efficiency.To address these challenges,this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm,improved seagull optimization algorithm(iSOA)for rapid path planning of autonomous robots.A modified Douglas–Peucker(mDP)algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains.The resulting mDPderived graph is then modeled using a Maklink graph theory.By applying the iSOA approach,the trajectory of an autonomous robot in the workspace is optimized.Additionally,a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints.The proposed model is validated through simulated experiments undertaken in various real-world settings,and its performance is compared with state-of-the-art algorithms.The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.展开更多
Network Intrusion Detection System(IDS)aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls.The features s...Network Intrusion Detection System(IDS)aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls.The features selection approach plays an important role in constructing effective network IDS.Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy.Therefore,this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack.The proposed model has two objectives;The first one is to reduce the number of selected features for Network IDS.This objective was met through the hybridization of bioinspired metaheuristic algorithms with each other in a hybrid model.The algorithms used in this paper are particle swarm optimization(PSO),multiverse optimizer(MVO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),firefly algorithm(FFA),and bat algorithm(BAT).The second objective is to detect the generic attack using machine learning classifiers.This objective was met through employing the support vector machine(SVM),C4.5(J48)decision tree,and random forest(RF)classifiers.UNSW-NB15 dataset used for assessing the effectiveness of the proposed hybrid model.UNSW-NB15 dataset has nine attacks type.The generic attack is the highest among them.Therefore,the proposed model aims to identify generic attacks.My data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the model.In terms of features reduction for the classification,my data show that the MFO-WOA and FFA-GWO models reduce the features to 15 features with close accuracy,sensitivity and F-measure of all features,whereas MVO-BAT model reduces features to 24 features with the same accuracy,sensitivity and F-measure of all features for all classifiers.展开更多
In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selec...In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.展开更多
Nowadays,meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems.In this paper,a COVID-19 prevention-inspired bionic optimization algorithm,named Corona...Nowadays,meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems.In this paper,a COVID-19 prevention-inspired bionic optimization algorithm,named Coronavirus Mask Protection Algorithm(CMPA),is proposed based on the virus transmission of COVID-19.The main inspiration for the CMPA originated from human self-protection behavior against COVID-19.In CMPA,the process of infection and immunity consists of three phases,including the infection stage,diffusion stage,and immune stage.Notably,wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves,which are similar to the exploration and exploitation in optimization algorithms.This study simulates the self-protection behavior mathematically and offers an optimization algorithm.The performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions,CEC2020 suite problems,and three truss design problems.The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms.Further,the CMPA is performed to identify the parameters of the main girder of a gantry crane.Results show that the mass and deflection of the main girder can be improved by 16.44%and 7.49%,respectively.展开更多
There is an urgent need for the application of broadband Microwave Absorption(MA)structures on the leading edges of aircraft wings,which requires the MA structures to possess both the broadband MA performance and grea...There is an urgent need for the application of broadband Microwave Absorption(MA)structures on the leading edges of aircraft wings,which requires the MA structures to possess both the broadband MA performance and great surface conformability.To meet these requirements,we designed and fabricated a flexible bioinspired meta-structure with ultra-broadband MA,thin thickness and excellent surface conformality.The carbonyl iron powder-carbon nanotubes-polydimethylsiloxane composite was synthesized by physical blending method for fabricating the MA meta-structure.Through geometry-electromagnetic optimal design by heuristic optimization algorithm,the meta-structure mimicking to the nipple photonic nanostructures on the eyes of moth can achieve ultra-broadband MA performance of 35.14 GHz MA bandwidth(reflection loss≤–10 dB),covering 4.86–40.00 GHz,with thickness of only 4.3 mm.Through simple fabrication processes,the meta-structure has been successfully fabricated and bonded on wings’leading edges,exhibiting excellent surface conformability.Furthermore,the designed flexible MA meta-structure possesses significant Radar Cross-Section(RCS)reduction capability,as demonstrated by the RCS analysis of an unmanned aerial vehicle.This flexible ultra-broadband MA meta-structure provides an outstanding candidate to meet the radar stealth requirement of variable curvature structures on aircraft.展开更多
Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and fiv...Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and five groups of constraints areproposed.A bio-mimicked Binary Bees Algorithm (BBA) is introduced to solve this multiobjective multiconstraint combinatorialoptimisation problem, in which constraint handling technique (Multiobjective Transformation, MOT), multiobjectiveevaluation method (nondominance selection), global search strategy (stochastic search in the variable space), local searchstrategy (Hamming neighbourhood exploitation), and post-processing means (feasibility selection) are the main issues.TheBBA is then demonstrated with a case study, presenting the execution process of the algorithm, and also explaining the change ofelite number in evolutionary process.Its optimisation result provides a group of feasible nondominated two-level distributionschemes.展开更多
Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered so...Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.展开更多
Combining the principle of antibody concentration with the idea of biological evolution, this paper proposes an adaptive target detection algorithm for cloud service security based on Bio-Inspired Performance Evaluati...Combining the principle of antibody concentration with the idea of biological evolution, this paper proposes an adaptive target detection algorithm for cloud service security based on Bio-Inspired Performance Evaluation Process Algebra(Bio-PEPA). The formal modelling of cloud services is formally modded by Bio-PEPA and the modules are transformed between cloud service internal structures and various components. Then, the security adaptive target detection algorithm of cloud service is divided into two processes, the short-term optimal action selection process which selects the current optimal detective action through the iterative operation of the expected function and the adaptive function, and the long-term detective strategy realized through the updates and eliminations of action planning table. The combination of the two processes reflects the self-adaptability of cloud service system to target detection. The simulating test detects three different kinds of security risks and then analyzes the relationship between the numbers of components with time in the service process. The performance of this method is compared with random detection method and three anomaly detection methods by the cloud service detection experiment. The detection time of this method is 50.1% of three kinds of detection methods and 86.3% of the random detection method. The service success rate is about 15% higher than that of random detection methods. The experimental results show that the algorithm has good time performance and high detection hit rate.展开更多
To further understand the performance of the energy harvesters under the influence of the wind force and the random excitation,this paper investigates the stochastic response of the bio-inspired energy harvesters subj...To further understand the performance of the energy harvesters under the influence of the wind force and the random excitation,this paper investigates the stochastic response of the bio-inspired energy harvesters subjected to Gaussian white noise and galloping excitation,simulating the flapping pattern of a seagull and its interaction with wind force.The equivalent linearization method is utilized to convert the original nonlinear model into the Itôstochastic differential equation by minimizing the mean squared error.Then,the second-order steady-state moments about the displacement,velocity,and voltage are derived by combining the moment analysis theory.The theoretical results are simulated numerically to analyze the stochastic response performance under different noise intensities,wind speeds,stiffness coefficients,and electromechanical coupling coefficients,time domain analysis is also conducted to study the performance of the harvester with different parameters.The results reveal that the mean square displacement and voltage increase with increasing the noise intensity and wind speed,larger absolute values of stiffness coefficient correspond to smaller mean square displacement and voltage,and larger electromechanical coupling coefficients can enhance the mean square voltage.Finally,the influence of wind speed and electromechanical coupling coefficient on the stationary probability density function(SPDF)is investigated,revealing the existence of a bimodal distribution under varying environmental conditions.展开更多
The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level m...The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level model and technology called Spatial Grasp for dealing with large distributed systems,which can provide spatial vision,awareness,management,control,and even consciousness.The technology description includes its key Spatial Grasp Language(SGL),self-evolution of recursive SGL scenarios,and implementation of SGL interpreter converting distributed networked systems into powerful spatial engines.Examples of typical spatial scenarios in SGL include finding shortest path tree and shortest path between network nodes,collecting proper information throughout the whole world,elimination of multiple targets by intelligent teams of chasers,and withstanding cyber attacks in distributed networked systems.Also this paper compares Spatial Grasp model with traditional algorithms,confirming universality of the former for any spatial systems,while the latter just tools for concrete applications.展开更多
Bio-inspired visual systems have garnered significant attention in robotics owing to their energy efficiency,rapid dynamic response,and environmental adaptability.Among these,event cameras-bio-inspired sensors that as...Bio-inspired visual systems have garnered significant attention in robotics owing to their energy efficiency,rapid dynamic response,and environmental adaptability.Among these,event cameras-bio-inspired sensors that asynchronously report pixel-level brightness changes called’events’,stand out because of their ability to capture dynamic changes with minimal energy consumption,making them suitable for challenging conditions,such as low light or high-speed motion.However,current mapping and localization methods for event cameras depend primarily on point and line features,which struggle in sparse or low-feature environments and are unsuitable for static or slow-motion scenarios.We addressed these challenges by proposing a bio-inspired vision mapping and localization method using active LED markers(ALMs)combined with reprojection error optimization and asynchronous Kalman fusion.Our approach replaces traditional features with ALMs,thereby enabling accurate tracking under dynamic and low-feature conditions.The global mapping accuracy significantly improved by minimizing the reprojection error,with corner errors reduced from 16.8 cm to 3.1 cm after 400 iterations.The asynchronous Kalman fusion of multiple camera pose estimations from ALMs ensures precise localization with a high temporal efficiency.This method achieved a mean translation error of 0.078 m and a rotational error of 5.411°while evaluating dynamic motion.In addition,the method supported an output rate of 4.5 kHz while maintaining high localization accuracy in UAV spiral flight experiments.These results demonstrate the potential of the proposed approach for real-time robot localization in challenging environments.展开更多
Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-i...Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-inspired materials which have excellent properties not present in conventional composites.To create such materials with desirable mechanical properties,the optimum structural parameters combination must be selected.Moreover,the optimal design of bio-inspired composites needs to take into account the trade-offs between various mechanical properties.In this paper,multi-objective optimization models were developed using structural parameters as design variables and mechanical properties as optimization objectives,including stiffness,strength,toughness,and dynamic damping.Using the NSGA-II optimization algorithm,a set of optimal solutions were solved.Additionally,three different structures in natural nacre were introduced in order to utilize the better structure when design bio-inspired materials.The range of optimal solutions that obtained using results from previous research were examined and explained why this collection of optimal solution ranges is better.Also,optimal solutions were compared with the structural features and mechanical properties of real nacre and artificial biomimetic composites to validate our models.Finally,the optimum design strategies can be obtained for nacre-like composites.Our research methodically proposes an optimization method for achieving load-bearing bio-inspired materials with excellent properties and creates a set of optimal solutions from which designers can select the one that best suits their preferences,allowing the fabricated materials to demonstrate preferred performance.展开更多
Inspired by the collective behaviors observed in bird flocks and fish schools,this paper proposes a novel Decentralized Model Predictive Flocking Control(DMPFC)framework to enable UAV swarms to autonomously track pred...Inspired by the collective behaviors observed in bird flocks and fish schools,this paper proposes a novel Decentralized Model Predictive Flocking Control(DMPFC)framework to enable UAV swarms to autonomously track predefined reference trajectories while avoiding collisions and maintaining a stable quasi[Math Processing Error]-lattice formation.Unlike traditional approaches that rely on switching between predefined swarm formations,this framework utilizes identical local interaction rules for each UAV,allowing them to dynamically adjust their control inputs based on the motion states of neighboring UAVs,external environmental factors,and the desired reference trajectory,thereby enabling the swarm to adapt its formation dynamically.Through iterative state updates,the UAVs achieve consensus,allowing the swarm to follow the reference trajectory while self-organizing into a cohesive and stable group structure.To enhance computational efficiency,the framework integrates a closed-form solution for the optimization process,enabling real-time implementation even on computationally constrained micro-quadrotors.Theoretical analysis demonstrates that the proposed method ensures swarm consensus,maintains desired inter-agent distances,and stabilizes the swarm formation.Extensive simulations and real-world experiments validate the approach’s effectiveness and practicality,demonstrating that the proposed method achieves velocity consensus within approximately 200 ms and forms a stable quasi[Math Processing Error]-lattice structure nearly ten times faster than traditional models,with trajectory tracking errors on the order of millimeters.This underscores its potential for robust and efficient UAV swarm coordination in complex scenarios.展开更多
This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task...This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task allocation for vigilance roles and the coverage planning of the perception ranges.Firstly,vigilance behavioral patterns and processes in animal populations within natural habitats are investigated.Inspired by these biological vigilance behaviors,an efficient vigilance task allocation model for MAS is proposed.Secondly,the subsequent optimization of task layouts can achieve efficient surveillance coverage with fewer agents,minimizing resource consumption.Thirdly,an improved particle swarm optimization(IPSO)algorithm is proposed,which incorporates fitness-driven adaptive inertia weight dynamics.According to simulation analysis and comparative studies,optimal parameter configurations for genetic algorithm(GA)and IPSO are determined.Finally,the results indicate the proposed IPSO's superior performance to both GA and standard particle swarm optimization(PSO)in vigilance task allocation optimization,with satisfying advantages in computational efficiency and solution quality.展开更多
Quantum computing offers unprecedented computational power, enabling simultaneous computations beyond traditional computers. Quantum computers differ significantly from classical computers, necessitating a distinct ap...Quantum computing offers unprecedented computational power, enabling simultaneous computations beyond traditional computers. Quantum computers differ significantly from classical computers, necessitating a distinct approach to algorithm design, which involves taming quantum mechanical phenomena. This paper extends the numbering of computable programs to be applied in the quantum computing context. Numbering computable programs is a theoretical computer science concept that assigns unique numbers to individual programs or algorithms. Common methods include Gödel numbering which encodes programs as strings of symbols or characters, often used in formal systems and mathematical logic. Based on the proposed numbering approach, this paper presents a mechanism to explore the set of possible quantum algorithms. The proposed approach is able to construct useful circuits such as Quantum Key Distribution BB84 protocol, which enables sender and receiver to establish a secure cryptographic key via a quantum channel. The proposed approach facilitates the process of exploring and constructing quantum algorithms.展开更多
This study evaluates the undrained uplift capacity of open-caisson anchors embedded in anisotropic clay using Finite Element Limit Analysis(FELA)and a hybrid machine learning framework.The FELA simulations inves-tigat...This study evaluates the undrained uplift capacity of open-caisson anchors embedded in anisotropic clay using Finite Element Limit Analysis(FELA)and a hybrid machine learning framework.The FELA simulations inves-tigate the influence of the radius ratio(R/B),anisotropic ratio(re),interface roughness factor(α),and inclination angle(β).Specifically,the results reveal that increasingβsignificantly enhances Nc,especially as soil behavior approaches isotropy.Higherαimproves resistance at steeper inclinations by mobilizing greater interface shear.Nc increases with re,reflecting enhanced strength under isotropic conditions.To enhance predictive accuracy and generalization,a hybrid machine learning model was developed by integrating Extreme Gradient Boosting(XGBoost)with Genetic Algorithm(GA)and Mutation-Based Genetic Algorithm(MGA)for hyperparameter tuning.Among the models,MGA-XGBoost outperformed GA-XGBoost,achieving higher predictive accuracy(R^(2)=0.996 training,0.993 testing).Furthermore,SHAP analysis consistently identified anisotropic ratio(re)as the most influential factor in predicting uplift capacity,followed by interface roughness factor(α),inclination angle(β),and radius ratio(R/B).The proposed framework serves as a scalable decision-support tool adaptable to various soil types and foundation geometries,offering a more efficient and data-driven approach to uplift-resistant design in anisotropic cohesive soils.展开更多
IntuiGrasp is a novel three-fingered dexterous hand that pioneers bio-inspired demonstrations with intuitive priors(BDIP)to bridge the gap between human tactile intuition and robotic execution.Unlike conven-tional pro...IntuiGrasp is a novel three-fingered dexterous hand that pioneers bio-inspired demonstrations with intuitive priors(BDIP)to bridge the gap between human tactile intuition and robotic execution.Unlike conven-tional programming,BDIP leverages human's innate priors(e.g.,“A pack of tissues requires gentle grasps,cups demand firm contact”)by enabling real-time transfer of gesture and force policies during physical demon-stration.When a human demonstrator wears IntuiGrasp,driven rings provide real-time haptic feedback on contact stress and slip,while inte-grated tactile sensors translate these human policies into image data,offering valuable data for imitation learning.In this study,human teachers use IntuiGrasp to demonstrate how to grasp three types of objects:a cup,a crumpled tissue pack,and a thin playing card.IntuiGrasp translates the policies for grasping these objects into image information that describes tactile sensations in real time.展开更多
基金supported by AIT Laboratory,FPT University,Danang Campus,Vietnam,2024.
文摘Natural Language Processing(NLP)has become essential in text classification,sentiment analysis,machine translation,and speech recognition applications.As these tasks become complex,traditionalmachine learning and deep learning models encounter challenges with optimization,parameter tuning,and handling large-scale,highdimensional data.Bio-inspired algorithms,which mimic natural processes,offer robust optimization capabilities that can enhance NLP performance by improving feature selection,optimizing model parameters,and integrating adaptive learning mechanisms.This review explores the state-of-the-art applications of bio-inspired algorithms—such as Genetic Algorithms(GA),Particle Swarm Optimization(PSO),and Ant Colony Optimization(ACO)—across core NLP tasks.We analyze their comparative advantages,discuss their integration with neural network models,and address computational and scalability limitations.Through a synthesis of existing research,this paper highlights the unique strengths and current challenges of bio-inspired approaches in NLP,offering insights into hybrid models and lightweight,resource-efficient adaptations for real-time processing.Finally,we outline future research directions that emphasize the development of scalable,effective bio-inspired methods adaptable to evolving data environments.
文摘Metaheuristics are commonly used in various fields,including real-life problem-solving and engineering applications.The present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm(ACSA).The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this process.The work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions,identified as classical benchmark functions.The method was subsequently examined by application to 12 CEC 2022 benchmark problems of different complexities.Furthermore,the paper evaluates ACSA in comparison to 64 metaheuristic methods that are derived from different approaches,including evolutionary,human,physics,and swarm-based.Subsequently,a sequence of statistical tests was undertaken to examine the superiority of the suggested algorithm in comparison to the 7 most widely used algorithms in the existing literature.The results show that the ACSA strategy can quickly reach the global optimum,avoid getting trapped in local optima,and effectively maintain a balance between exploration and exploitation.ACSA outperformed 42 algorithms statistically,according to post-hoc tests.It also outperformed 9 algorithms quantitatively.The study concludes that ACSA offers competitive solutions in comparison to popüler methods.
文摘Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps.However,these approaches endure performance degradation as problem complexity increases,often resulting in lengthy search times to find an optimal solution.This limitation is particularly critical for real-world applications like autonomous off-road vehicles,where highquality path computation is essential for energy efficiency.To address these challenges,this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm,improved seagull optimization algorithm(iSOA)for rapid path planning of autonomous robots.A modified Douglas–Peucker(mDP)algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains.The resulting mDPderived graph is then modeled using a Maklink graph theory.By applying the iSOA approach,the trajectory of an autonomous robot in the workspace is optimized.Additionally,a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints.The proposed model is validated through simulated experiments undertaken in various real-world settings,and its performance is compared with state-of-the-art algorithms.The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.
基金funded by The World Islamic Sciences and Education University。
文摘Network Intrusion Detection System(IDS)aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls.The features selection approach plays an important role in constructing effective network IDS.Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy.Therefore,this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack.The proposed model has two objectives;The first one is to reduce the number of selected features for Network IDS.This objective was met through the hybridization of bioinspired metaheuristic algorithms with each other in a hybrid model.The algorithms used in this paper are particle swarm optimization(PSO),multiverse optimizer(MVO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),firefly algorithm(FFA),and bat algorithm(BAT).The second objective is to detect the generic attack using machine learning classifiers.This objective was met through employing the support vector machine(SVM),C4.5(J48)decision tree,and random forest(RF)classifiers.UNSW-NB15 dataset used for assessing the effectiveness of the proposed hybrid model.UNSW-NB15 dataset has nine attacks type.The generic attack is the highest among them.Therefore,the proposed model aims to identify generic attacks.My data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the model.In terms of features reduction for the classification,my data show that the MFO-WOA and FFA-GWO models reduce the features to 15 features with close accuracy,sensitivity and F-measure of all features,whereas MVO-BAT model reduces features to 24 features with the same accuracy,sensitivity and F-measure of all features for all classifiers.
基金the Deputyship for Research and Innovation,“Ministry of Education”in Saudi Arabia for funding this research(IFKSUOR3-014-3).
文摘In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.
基金supported by Henan Natural Science Foundation,No.222300420168,Yongliang YuanScience and Technology Plan Project of Henan Province,No.222102210182,Jianji Ren+3 种基金National Natural Science Foundation of China,No.52005081,Xiaokai MuNatural Science Foundation of Henan Polytechnic University,B2021-31,Yongliang YuanNonlinear equipment dynamics team of Henan Polytechnic University,T2019-5,Junkai FanFundamental Research Funds for the Universities of Henan Province,NSFRF220415,Yongliang Yuan.
文摘Nowadays,meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems.In this paper,a COVID-19 prevention-inspired bionic optimization algorithm,named Coronavirus Mask Protection Algorithm(CMPA),is proposed based on the virus transmission of COVID-19.The main inspiration for the CMPA originated from human self-protection behavior against COVID-19.In CMPA,the process of infection and immunity consists of three phases,including the infection stage,diffusion stage,and immune stage.Notably,wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves,which are similar to the exploration and exploitation in optimization algorithms.This study simulates the self-protection behavior mathematically and offers an optimization algorithm.The performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions,CEC2020 suite problems,and three truss design problems.The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms.Further,the CMPA is performed to identify the parameters of the main girder of a gantry crane.Results show that the mass and deflection of the main girder can be improved by 16.44%and 7.49%,respectively.
基金supported by the Basic Research Development Program of China(No.JCKY2021607B036)the National Natural Science Foundation of China(No.52275512).
文摘There is an urgent need for the application of broadband Microwave Absorption(MA)structures on the leading edges of aircraft wings,which requires the MA structures to possess both the broadband MA performance and great surface conformability.To meet these requirements,we designed and fabricated a flexible bioinspired meta-structure with ultra-broadband MA,thin thickness and excellent surface conformality.The carbonyl iron powder-carbon nanotubes-polydimethylsiloxane composite was synthesized by physical blending method for fabricating the MA meta-structure.Through geometry-electromagnetic optimal design by heuristic optimization algorithm,the meta-structure mimicking to the nipple photonic nanostructures on the eyes of moth can achieve ultra-broadband MA performance of 35.14 GHz MA bandwidth(reflection loss≤–10 dB),covering 4.86–40.00 GHz,with thickness of only 4.3 mm.Through simple fabrication processes,the meta-structure has been successfully fabricated and bonded on wings’leading edges,exhibiting excellent surface conformability.Furthermore,the designed flexible MA meta-structure possesses significant Radar Cross-Section(RCS)reduction capability,as demonstrated by the RCS analysis of an unmanned aerial vehicle.This flexible ultra-broadband MA meta-structure provides an outstanding candidate to meet the radar stealth requirement of variable curvature structures on aircraft.
文摘Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and five groups of constraints areproposed.A bio-mimicked Binary Bees Algorithm (BBA) is introduced to solve this multiobjective multiconstraint combinatorialoptimisation problem, in which constraint handling technique (Multiobjective Transformation, MOT), multiobjectiveevaluation method (nondominance selection), global search strategy (stochastic search in the variable space), local searchstrategy (Hamming neighbourhood exploitation), and post-processing means (feasibility selection) are the main issues.TheBBA is then demonstrated with a case study, presenting the execution process of the algorithm, and also explaining the change ofelite number in evolutionary process.Its optimisation result provides a group of feasible nondominated two-level distributionschemes.
文摘Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.
基金Supported by the National Natural Science Foundation of China(61202458,61403109)the Natural Science Foundation of Heilongjiang Province of China(F2017021)and the Harbin Science and Technology Innovation Research Funds(2016RAQXJ036)
文摘Combining the principle of antibody concentration with the idea of biological evolution, this paper proposes an adaptive target detection algorithm for cloud service security based on Bio-Inspired Performance Evaluation Process Algebra(Bio-PEPA). The formal modelling of cloud services is formally modded by Bio-PEPA and the modules are transformed between cloud service internal structures and various components. Then, the security adaptive target detection algorithm of cloud service is divided into two processes, the short-term optimal action selection process which selects the current optimal detective action through the iterative operation of the expected function and the adaptive function, and the long-term detective strategy realized through the updates and eliminations of action planning table. The combination of the two processes reflects the self-adaptability of cloud service system to target detection. The simulating test detects three different kinds of security risks and then analyzes the relationship between the numbers of components with time in the service process. The performance of this method is compared with random detection method and three anomaly detection methods by the cloud service detection experiment. The detection time of this method is 50.1% of three kinds of detection methods and 86.3% of the random detection method. The service success rate is about 15% higher than that of random detection methods. The experimental results show that the algorithm has good time performance and high detection hit rate.
文摘To further understand the performance of the energy harvesters under the influence of the wind force and the random excitation,this paper investigates the stochastic response of the bio-inspired energy harvesters subjected to Gaussian white noise and galloping excitation,simulating the flapping pattern of a seagull and its interaction with wind force.The equivalent linearization method is utilized to convert the original nonlinear model into the Itôstochastic differential equation by minimizing the mean squared error.Then,the second-order steady-state moments about the displacement,velocity,and voltage are derived by combining the moment analysis theory.The theoretical results are simulated numerically to analyze the stochastic response performance under different noise intensities,wind speeds,stiffness coefficients,and electromechanical coupling coefficients,time domain analysis is also conducted to study the performance of the harvester with different parameters.The results reveal that the mean square displacement and voltage increase with increasing the noise intensity and wind speed,larger absolute values of stiffness coefficient correspond to smaller mean square displacement and voltage,and larger electromechanical coupling coefficients can enhance the mean square voltage.Finally,the influence of wind speed and electromechanical coupling coefficient on the stationary probability density function(SPDF)is investigated,revealing the existence of a bimodal distribution under varying environmental conditions.
文摘The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level model and technology called Spatial Grasp for dealing with large distributed systems,which can provide spatial vision,awareness,management,control,and even consciousness.The technology description includes its key Spatial Grasp Language(SGL),self-evolution of recursive SGL scenarios,and implementation of SGL interpreter converting distributed networked systems into powerful spatial engines.Examples of typical spatial scenarios in SGL include finding shortest path tree and shortest path between network nodes,collecting proper information throughout the whole world,elimination of multiple targets by intelligent teams of chasers,and withstanding cyber attacks in distributed networked systems.Also this paper compares Spatial Grasp model with traditional algorithms,confirming universality of the former for any spatial systems,while the latter just tools for concrete applications.
基金Supported by Beijing Natural Science Foundation(Grant No.L231004)Young Elite Scientists Sponsorship Program by CAST(Grant No.2022QNRC001)+2 种基金Fundamental Research Funds for the Central Universities(Grant No.2025JBMC039)National Key Research and Development Program(Grant No.2022YFC2805200)National Natural Science Foundation of China(Grant No.52371338).
文摘Bio-inspired visual systems have garnered significant attention in robotics owing to their energy efficiency,rapid dynamic response,and environmental adaptability.Among these,event cameras-bio-inspired sensors that asynchronously report pixel-level brightness changes called’events’,stand out because of their ability to capture dynamic changes with minimal energy consumption,making them suitable for challenging conditions,such as low light or high-speed motion.However,current mapping and localization methods for event cameras depend primarily on point and line features,which struggle in sparse or low-feature environments and are unsuitable for static or slow-motion scenarios.We addressed these challenges by proposing a bio-inspired vision mapping and localization method using active LED markers(ALMs)combined with reprojection error optimization and asynchronous Kalman fusion.Our approach replaces traditional features with ALMs,thereby enabling accurate tracking under dynamic and low-feature conditions.The global mapping accuracy significantly improved by minimizing the reprojection error,with corner errors reduced from 16.8 cm to 3.1 cm after 400 iterations.The asynchronous Kalman fusion of multiple camera pose estimations from ALMs ensures precise localization with a high temporal efficiency.This method achieved a mean translation error of 0.078 m and a rotational error of 5.411°while evaluating dynamic motion.In addition,the method supported an output rate of 4.5 kHz while maintaining high localization accuracy in UAV spiral flight experiments.These results demonstrate the potential of the proposed approach for real-time robot localization in challenging environments.
基金Supported by National Natural Science Foundation of China(Grant Nos.52222505,52321002)Shanghai Municipal Natural Science Foundation o China(Grant No.23ZR1415500)。
文摘Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-inspired materials which have excellent properties not present in conventional composites.To create such materials with desirable mechanical properties,the optimum structural parameters combination must be selected.Moreover,the optimal design of bio-inspired composites needs to take into account the trade-offs between various mechanical properties.In this paper,multi-objective optimization models were developed using structural parameters as design variables and mechanical properties as optimization objectives,including stiffness,strength,toughness,and dynamic damping.Using the NSGA-II optimization algorithm,a set of optimal solutions were solved.Additionally,three different structures in natural nacre were introduced in order to utilize the better structure when design bio-inspired materials.The range of optimal solutions that obtained using results from previous research were examined and explained why this collection of optimal solution ranges is better.Also,optimal solutions were compared with the structural features and mechanical properties of real nacre and artificial biomimetic composites to validate our models.Finally,the optimum design strategies can be obtained for nacre-like composites.Our research methodically proposes an optimization method for achieving load-bearing bio-inspired materials with excellent properties and creates a set of optimal solutions from which designers can select the one that best suits their preferences,allowing the fabricated materials to demonstrate preferred performance.
基金supported in part by the Guangdong Provincial Universities'Characteristic Innovation Project under Grant 2024KTSCX360in part by the Guangdong Educational Science Planning Project under Grant 2023GXJK837.
文摘Inspired by the collective behaviors observed in bird flocks and fish schools,this paper proposes a novel Decentralized Model Predictive Flocking Control(DMPFC)framework to enable UAV swarms to autonomously track predefined reference trajectories while avoiding collisions and maintaining a stable quasi[Math Processing Error]-lattice formation.Unlike traditional approaches that rely on switching between predefined swarm formations,this framework utilizes identical local interaction rules for each UAV,allowing them to dynamically adjust their control inputs based on the motion states of neighboring UAVs,external environmental factors,and the desired reference trajectory,thereby enabling the swarm to adapt its formation dynamically.Through iterative state updates,the UAVs achieve consensus,allowing the swarm to follow the reference trajectory while self-organizing into a cohesive and stable group structure.To enhance computational efficiency,the framework integrates a closed-form solution for the optimization process,enabling real-time implementation even on computationally constrained micro-quadrotors.Theoretical analysis demonstrates that the proposed method ensures swarm consensus,maintains desired inter-agent distances,and stabilizes the swarm formation.Extensive simulations and real-world experiments validate the approach’s effectiveness and practicality,demonstrating that the proposed method achieves velocity consensus within approximately 200 ms and forms a stable quasi[Math Processing Error]-lattice structure nearly ten times faster than traditional models,with trajectory tracking errors on the order of millimeters.This underscores its potential for robust and efficient UAV swarm coordination in complex scenarios.
基金The National Natural Science Foundation of China(62203015,62233001,62273351)The Beijing Natural Science Foundation(4242038)。
文摘This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task allocation for vigilance roles and the coverage planning of the perception ranges.Firstly,vigilance behavioral patterns and processes in animal populations within natural habitats are investigated.Inspired by these biological vigilance behaviors,an efficient vigilance task allocation model for MAS is proposed.Secondly,the subsequent optimization of task layouts can achieve efficient surveillance coverage with fewer agents,minimizing resource consumption.Thirdly,an improved particle swarm optimization(IPSO)algorithm is proposed,which incorporates fitness-driven adaptive inertia weight dynamics.According to simulation analysis and comparative studies,optimal parameter configurations for genetic algorithm(GA)and IPSO are determined.Finally,the results indicate the proposed IPSO's superior performance to both GA and standard particle swarm optimization(PSO)in vigilance task allocation optimization,with satisfying advantages in computational efficiency and solution quality.
文摘Quantum computing offers unprecedented computational power, enabling simultaneous computations beyond traditional computers. Quantum computers differ significantly from classical computers, necessitating a distinct approach to algorithm design, which involves taming quantum mechanical phenomena. This paper extends the numbering of computable programs to be applied in the quantum computing context. Numbering computable programs is a theoretical computer science concept that assigns unique numbers to individual programs or algorithms. Common methods include Gödel numbering which encodes programs as strings of symbols or characters, often used in formal systems and mathematical logic. Based on the proposed numbering approach, this paper presents a mechanism to explore the set of possible quantum algorithms. The proposed approach is able to construct useful circuits such as Quantum Key Distribution BB84 protocol, which enables sender and receiver to establish a secure cryptographic key via a quantum channel. The proposed approach facilitates the process of exploring and constructing quantum algorithms.
文摘This study evaluates the undrained uplift capacity of open-caisson anchors embedded in anisotropic clay using Finite Element Limit Analysis(FELA)and a hybrid machine learning framework.The FELA simulations inves-tigate the influence of the radius ratio(R/B),anisotropic ratio(re),interface roughness factor(α),and inclination angle(β).Specifically,the results reveal that increasingβsignificantly enhances Nc,especially as soil behavior approaches isotropy.Higherαimproves resistance at steeper inclinations by mobilizing greater interface shear.Nc increases with re,reflecting enhanced strength under isotropic conditions.To enhance predictive accuracy and generalization,a hybrid machine learning model was developed by integrating Extreme Gradient Boosting(XGBoost)with Genetic Algorithm(GA)and Mutation-Based Genetic Algorithm(MGA)for hyperparameter tuning.Among the models,MGA-XGBoost outperformed GA-XGBoost,achieving higher predictive accuracy(R^(2)=0.996 training,0.993 testing).Furthermore,SHAP analysis consistently identified anisotropic ratio(re)as the most influential factor in predicting uplift capacity,followed by interface roughness factor(α),inclination angle(β),and radius ratio(R/B).The proposed framework serves as a scalable decision-support tool adaptable to various soil types and foundation geometries,offering a more efficient and data-driven approach to uplift-resistant design in anisotropic cohesive soils.
文摘IntuiGrasp is a novel three-fingered dexterous hand that pioneers bio-inspired demonstrations with intuitive priors(BDIP)to bridge the gap between human tactile intuition and robotic execution.Unlike conven-tional programming,BDIP leverages human's innate priors(e.g.,“A pack of tissues requires gentle grasps,cups demand firm contact”)by enabling real-time transfer of gesture and force policies during physical demon-stration.When a human demonstrator wears IntuiGrasp,driven rings provide real-time haptic feedback on contact stress and slip,while inte-grated tactile sensors translate these human policies into image data,offering valuable data for imitation learning.In this study,human teachers use IntuiGrasp to demonstrate how to grasp three types of objects:a cup,a crumpled tissue pack,and a thin playing card.IntuiGrasp translates the policies for grasping these objects into image information that describes tactile sensations in real time.
