Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convol...Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.展开更多
It is evident that complex optimization problems are becoming increasingly prominent,metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional,nonlinear problems.However,the traditional ...It is evident that complex optimization problems are becoming increasingly prominent,metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional,nonlinear problems.However,the traditional Sparrow Search Algorithm(SSA)suffers from limited global search capability,insufficient population diversity,and slow convergence,which often leads to premature stagnation in local optima.Despite the proposal of various enhanced versions,the effective balancing of exploration and exploitation remains an unsolved challenge.To address the previously mentioned problems,this study proposes a multi-strategy collaborative improved SSA,which systematically integrates four complementary strategies:(1)the Northern Goshawk Optimization(NGO)mechanism enhances global exploration through guided prey-attacking dynamics;(2)an adaptive t-distribution mutation strategy balances the transition between exploration and exploitation via dynamic adjustment of the degrees of freedom;(3)a dual chaotic initialization method(Bernoulli and Sinusoidal maps)increases population diversity and distribution uniformity;and(4)an elite retention strategy maintains solution quality and prevents degradation during iterations.These strategies cooperate synergistically,forming a tightly coupled optimization framework that significantly improves search efficiency and robustness.Therefore,this paper names it NTSSA:A Novel Multi-Strategy Enhanced Sparrow Search Algorithm with Northern Goshawk Optimization and Adaptive t-Distribution for Global Optimization.Extensive experiments on the CEC2005 benchmark set demonstrate that NTSSA achieves theoretical optimal accuracy on unimodal functions and significantly enhances global optimum discovery for multimodal functions by 2–5 orders of magnitude.Compared with SSA,GWO,ISSA,and CSSOA,NTSSA improves solution accuracy by up to 14.3%(F8)and 99.8%(F12),while accelerating convergence by approximately 1.5–2×.The Wilcoxon rank-sum test(p<0.05)indicates that NTSSA demonstrates a statistically substantial performance advantage.Theoretical analysis demonstrates that the collaborative synergy among adaptive mutation,chaos-based diversification,and elite preservation ensures both high convergence accuracy and global stability.This work bridges a key research gap in SSA by realizing a coordinated optimization mechanism between exploration and exploitation,offering a robust and efficient solution framework for complex high-dimensional problems in intelligent computation and engineering design.展开更多
At present, salient object detection (SOD) has achieved considerable progress. However, the methods that perform well still face the issue of inadequate detection accuracy. For example, sometimes there are problems of...At present, salient object detection (SOD) has achieved considerable progress. However, the methods that perform well still face the issue of inadequate detection accuracy. For example, sometimes there are problems of missed and false detections. Effectively optimizing features to capture key information and better integrating different levels of features to enhance their complementarity are two significant challenges in the domain of SOD. In response to these challenges, this study proposes a novel SOD method based on multi-strategy feature optimization. We propose the multi-size feature extraction module (MSFEM), which uses the attention mechanism, the multi-level feature fusion, and the residual block to obtain finer features. This module provides robust support for the subsequent accurate detection of the salient object. In addition, we use two rounds of feature fusion and the feedback mechanism to optimize the features obtained by the MSFEM to improve detection accuracy. The first round of feature fusion is applied to integrate the features extracted by the MSFEM to obtain more refined features. Subsequently, the feedback mechanism and the second round of feature fusion are applied to refine the features, thereby providing a stronger foundation for accurately detecting salient objects. To improve the fusion effect, we propose the feature enhancement module (FEM) and the feature optimization module (FOM). The FEM integrates the upper and lower features with the optimized features obtained by the FOM to enhance feature complementarity. The FOM uses different receptive fields, the attention mechanism, and the residual block to more effectively capture key information. Experimental results demonstrate that our method outperforms 10 state-of-the-art SOD methods.展开更多
In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature sel...In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.展开更多
Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,...Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,it also has certain drawbacks for the exploration stage and the egg hatch process,resulting in slow convergence speed and inferior solution quality.To address the above issues,a novel multi-strategy improved SO(MISO)with the assistance of population crowding analysis is proposed in this article.In the algorithm,a novel multi-strategy operator is designed for the exploration stage,which not only focuses on using the information of better performing individuals to improve the quality of solution,but also focuses on maintaining population diversity.To boost the efficiency of the egg hatch process,the multi-strategy egg hatch process is proposed to regenerate individuals according to the results of the population crowding analysis.In addition,a local search method is employed to further enhance the convergence speed and the local search capability.MISO is first compared with three sets of algorithms in the CEC2020 benchmark functions,including SO with its two recently discussed variants,ten advanced MAs,and six powerful CEC competition algorithms.The performance of MISO is then verified on five practical engineering design problems.The experimental results show that MISO provides a promising performance for the above optimization cases in terms of convergence speed and solution quality.展开更多
Self-serving,rational agents sometimes cooperate to their mutual benefit.The two-player iterated prisoner′s dilemma game is a model for including the emergence of cooperation.It is generally believed that there is no...Self-serving,rational agents sometimes cooperate to their mutual benefit.The two-player iterated prisoner′s dilemma game is a model for including the emergence of cooperation.It is generally believed that there is no simple ultimatum strategy which a player can control the return of the other participants.The zero-determinant strategy in the iterated prisoner′s dilemma dramatically expands our understanding of the classic game by uncovering strategies that provide a unilateral advantage to sentient players pitted against unwitting opponents.However,strategies in the prisoner′s dilemma game are only two strategies.Are there these results for general multi-strategy games?To address this question,the paper develops a theory for zero-determinant strategies for multi-strategy games,with any number of strategies.The analytical results exhibit a similar yet different scenario to the case of two-strategy games.The results are also applied to the Snowdrift game,the Hawk-Dove game and the Chicken game.展开更多
Dynamic spectrum access(DSA) based on cognitive radios(CR) technique is an effective approach to address the "spectrum scarcity" issue. However, traditional CR-enabled DSA system employs only single DSA stra...Dynamic spectrum access(DSA) based on cognitive radios(CR) technique is an effective approach to address the "spectrum scarcity" issue. However, traditional CR-enabled DSA system employs only single DSA strategy, which might not be suited to the dynamic network environment. In this paper, we propose a multi-strategy DSA(MS-DSA) system, where the primary and the secondary system share spectrum resources with multiple DSA strategies simultaneously. To analyze the performance of the proposed MS-DSA system, we model it as a continuous-time Markov chain(CTMC) and derive the expressions to compute the corresponding performance metrics. Based on this, we define a utility function involving the concerns of effective throughput, interference quantity on primary users, and spectrum leasing cost. Two optimization schemes, named as spectrum allocation and false alarm probability selection, are proposed to maximize the utility function. Finally, numerical simulations are provided to validate our analysis and demonstrate that the performance can be significantly improved caused by virtues of the proposed MS-DSA system.展开更多
This paper studies the difference algorithm parameters characteristic of the multicast routing problem, and to compare it with genetic algorithms. The algorithm uses the path of individual coding, combined with the di...This paper studies the difference algorithm parameters characteristic of the multicast routing problem, and to compare it with genetic algorithms. The algorithm uses the path of individual coding, combined with the differential cross-choice strategy and operations optimization. Finally, we simulated 30 node networks, and compared the performance of genetic algorithm and differential evolution algorithm. Experimental results show that multi-strategy Differential Evolution algorithm converges faster and better global search ability and stability.展开更多
The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization problem.Existing optimization approaches often suffer from slow convergence and a propensity to converg...The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization problem.Existing optimization approaches often suffer from slow convergence and a propensity to converge to local optima,limiting their effectiveness in achieving optimal power efficiency.First,we propose a novel multi-strategy fusion memetic algorithm(MFMA).MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor(COA-OLA),complemented by population management through truncation selection.Second,leveraging MFMA,we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit power.Experimental results based on Microelectronics Center of North Carolina(MCNC)benchmark circuits demonstrate significant improvements over existing power optimization approaches.MFMA achieves a maximum power saving rate of 72.30%and an average optimization rate of 43.37%;it searches for solutions faster and with higher quality,validating its effectiveness and superiority in power optimization.展开更多
TheHoney Badger Algorithm(HBA)is a novelmeta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers.The dynamic search behavior of honey badgers with sniffing and wandering is divided...TheHoney Badger Algorithm(HBA)is a novelmeta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers.The dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA,which has been applied in photovoltaic systems and optimization problems effectively.However,HBA tends to suffer from the local optimum and low convergence.To alleviate these challenges,an improved HBA(IHBA)through fusing multi-strategies is presented in the paper.It introduces Tent chaotic mapping and composite mutation factors to HBA,meanwhile,the random control parameter is improved,moreover,a diversified updating strategy of position is put forward to enhance the advantage between exploration and exploitation.IHBA is compared with 7 meta-heuristic algorithms in 10 benchmark functions and 5 engineering problems.The Wilcoxon Rank-sum Test,Friedman Test and Mann-WhitneyU Test are conducted after emulation.The results indicate the competitiveness and merits of the IHBA,which has better solution quality and convergence traits.The source code is currently available from:https://github.com/zhaotao789/IHBA.展开更多
In this paper,a multi-strategy improved coati optimization algorithm(MICOA)for engineering applications is proposed to improve the performance of the coati optimization algorithm(COA)in terms of convergence speed and ...In this paper,a multi-strategy improved coati optimization algorithm(MICOA)for engineering applications is proposed to improve the performance of the coati optimization algorithm(COA)in terms of convergence speed and convergence accuracy.First,a chaotic mapping is applied to initial-ize the population in order to improve the quality of the population and thus the convergence speed of the algorithm.Second,the prey’s position is improved during the prey-hunting phase.Then,the COA is combined with the particle swarm optimization(PSO)and the golden sine algorithm(Gold-SA),and the position is updated with probabilities to avoid local extremes.Finally,a population decreasing strategy is applied as a way to improve the performance of the algorithm in a comprehen-sive approach.The paper compares the proposed algorithm MICOA with 7 well-known meta-heuristic optimization algorithms and evaluates the algorithm in 23 test functions as well as engineering appli-cation.Experimental results show that the MICOA proposed in this paper has good effectiveness and superiority,and has a strong competitiveness compared with the comparison algorithms.展开更多
Active traffic management(ATM)enhances highway capacity,yet single strategies are inadequate for increasing traffic demands,and poorly integrated multi-strategy approaches can degrade performance.This paper introduces...Active traffic management(ATM)enhances highway capacity,yet single strategies are inadequate for increasing traffic demands,and poorly integrated multi-strategy approaches can degrade performance.This paper introduces a coupled multi-strategy ATM framework leveraging a multi-agent deep deterministic policy gradient with a communication protocol(MADDPGCP).This method stabilizes training via shared agent observations.The study also investigates the influence of compliance degree on control performance in mixed traffic comprising connected/automated vehicles(CAVs)and human-driven vehicles(HDVs).Numerical experiments with real-world data confirm MADDPG-CP's stable convergence.The proposed MADDPG-CPbased multi-strategy ATM,validated against traditional methods,significantly improves highway capacity utilization by elucidating individual strategy mechanisms and verifying control efficiency.展开更多
The high-nickel layered cathodes Li[Ni_(x)Co_(y)Mn_(1-x-y)]O_(2)(x≥0.8)with high specific capacity and long cycle life are considered as prospective cathodes for lithium-ion batteries.However,the microcrack formation...The high-nickel layered cathodes Li[Ni_(x)Co_(y)Mn_(1-x-y)]O_(2)(x≥0.8)with high specific capacity and long cycle life are considered as prospective cathodes for lithium-ion batteries.However,the microcrack formation and poor structural stability give rise to inferior rate performance and undesirable cycling life.Herein,we propose a dual modification strategy combining primary particle structure design and element doping to modify Li[Ni_(0.95)Co_(0.025)Mn_(0.025)]O_(2) cathode by tungsten and fluorine co-doped(W-F-NCM95).The doping of W can convert the microstructure of primary particles to the unique rod-like shape,which is beneficial to enhance the reversibility of phase transition and alleviate the generation of microcracks.F doping is conducive to alleviating the surface side reactions.Thus,due to the synergistic effect of W,F codoping,the obtained W-F-NCM95 cathodes deliver a high initial capacity of 236.1 mA h g^(-1) at 0.1 C and superior capacity retention of 88.7%over 100 cycles at 0.5 C.Moreover,the capacity still maintains73.8%after 500 cycles at 0.5 C and the texture of primary particle is intact.This work provides an available strategy by W and F co-doping to enhance the electrochemistry performance of high-nickel cathodes for practical application.展开更多
Ontology mapping is a key interoperability enabler for the semantic web. In this paper,a new ontology mapping approach called ontology mapping based on Bayesian network( OM-BN) is proposed. OM-BN combines the models o...Ontology mapping is a key interoperability enabler for the semantic web. In this paper,a new ontology mapping approach called ontology mapping based on Bayesian network( OM-BN) is proposed. OM-BN combines the models of ontology and Bayesian Network,and applies the method of Multi-strategy to computing similarity. In OM-BN,the characteristics of ontology,such as tree structure and semantic inclusion relations among concepts,are used during the process of translation from ontology to ontology Bayesian network( OBN). Then the method of Multi-strategy is used to create similarity table( ST) for each concept-node in OBN. Finally,the iterative process of mapping reasoning is used to deduce new mappings from STs,repeatedly.展开更多
Construction site layout planning(CSLP)involves strategically placing various facilities to optimize a project.However,real construction sites are complex,making it challenging to consider all construction activities ...Construction site layout planning(CSLP)involves strategically placing various facilities to optimize a project.However,real construction sites are complex,making it challenging to consider all construction activities and facilities comprehensively.Addressing multi-objective layout optimization is crucial for CSLP.Previous optimization results often lacked precision,imposed stringent boundary constraints,and had limited applications in prefabricated construction.Traditional heuristic algorithms still require improvements in region search strategies and computational efficiency when tackling multi-objective optimization problems.This paper optimizes the prefabricated component construction site layout planning(PCCSLP)by treating construction efficiency and safety risk as objectives within a multi-objective CSLP model.A novel heuristic algorithm,the Hybrid Multi-Strategy Improvement Dung Beetle Optimizer(HMSIDBO),was applied to solve the model due to its balanced capabilities in global exploration and local development.The practicality and effectiveness of this approach were validated through a case study in prefabricated residential construction.The research findings indicate that the HMSIDBO-PCCSLP optimization scheme improved each objective by 18%to 75%compared to the original layout.Compared to Genetic Algorithm(GA),the HMSIDBO demonstrates significantly faster computational speed and higher resolution accuracy.Additionally,in comparison with the Dung Beetle Optimizer(DBO),Particle Swarm Optimization(PSO),and Whale Optimization Algorithm(WOA),HMSIDBO exhibits superior iterative speed and an enhanced ability for global exploration.This paper completes the framework from data collection to multi-objective optimization in-site layout,laying the foundation for implementing intelligent construction site layout practices.展开更多
Portable energy solutions are highly desired in the era of the Internet of Things for powering various distributed micro-electronic devices.At the same time,the energy crisis and catastrophic global warming are becomi...Portable energy solutions are highly desired in the era of the Internet of Things for powering various distributed micro-electronic devices.At the same time,the energy crisis and catastrophic global warming are becoming serious problems in the world,emphasizing the urgent need for clean and renewable energy.Here,we report a low-cost,high-performance,and portable hand-driven whirligig structured triboelectric–electromagnetic hybrid nanogenerator(whirligig-HNG)for multi-strategy energy harvesting.The whirligig-HNG comprises a dynamic supercoiling TENG via the pulling-strings and inner-distributed EMGs(variable number)in the rotator.The whirligig structure can readily convert linear displacement in low frequency into rotary motion in extremely high frequency.Based on this ingenious design,the whirligig-HNG is capable to harvest the triboelectric energy from the supercoiling/uncoiling process from the pulling strings and simultaneously utilize the high-frequency rotation energy via electromagnetic induction.We have systematically investigated the working mecha-nism of the whirligig-HNG for coupled energy harvesting and compared the individual characteristics of TENG and EMG.The whirligig-HNG is successfully demonstrated to light up more than 100 commercial light-emitting diodes(LEDs)and drive portable electronics.This research presents the enormous potential of whirligig-HNG as a manual and portable power supply for powering various portable electronics.展开更多
基金supported by Science and Technology Innovation Programfor Postgraduate Students in IDP Subsidized by Fundamental Research Funds for the Central Universities(Project No.ZY20240335)support of the Research Project of the Key Technology of Malicious Code Detection Based on Data Mining in APT Attack(Project No.2022IT173)the Research Project of the Big Data Sensitive Information Supervision Technology Based on Convolutional Neural Network(Project No.2022011033).
文摘Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.
文摘It is evident that complex optimization problems are becoming increasingly prominent,metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional,nonlinear problems.However,the traditional Sparrow Search Algorithm(SSA)suffers from limited global search capability,insufficient population diversity,and slow convergence,which often leads to premature stagnation in local optima.Despite the proposal of various enhanced versions,the effective balancing of exploration and exploitation remains an unsolved challenge.To address the previously mentioned problems,this study proposes a multi-strategy collaborative improved SSA,which systematically integrates four complementary strategies:(1)the Northern Goshawk Optimization(NGO)mechanism enhances global exploration through guided prey-attacking dynamics;(2)an adaptive t-distribution mutation strategy balances the transition between exploration and exploitation via dynamic adjustment of the degrees of freedom;(3)a dual chaotic initialization method(Bernoulli and Sinusoidal maps)increases population diversity and distribution uniformity;and(4)an elite retention strategy maintains solution quality and prevents degradation during iterations.These strategies cooperate synergistically,forming a tightly coupled optimization framework that significantly improves search efficiency and robustness.Therefore,this paper names it NTSSA:A Novel Multi-Strategy Enhanced Sparrow Search Algorithm with Northern Goshawk Optimization and Adaptive t-Distribution for Global Optimization.Extensive experiments on the CEC2005 benchmark set demonstrate that NTSSA achieves theoretical optimal accuracy on unimodal functions and significantly enhances global optimum discovery for multimodal functions by 2–5 orders of magnitude.Compared with SSA,GWO,ISSA,and CSSOA,NTSSA improves solution accuracy by up to 14.3%(F8)and 99.8%(F12),while accelerating convergence by approximately 1.5–2×.The Wilcoxon rank-sum test(p<0.05)indicates that NTSSA demonstrates a statistically substantial performance advantage.Theoretical analysis demonstrates that the collaborative synergy among adaptive mutation,chaos-based diversification,and elite preservation ensures both high convergence accuracy and global stability.This work bridges a key research gap in SSA by realizing a coordinated optimization mechanism between exploration and exploitation,offering a robust and efficient solution framework for complex high-dimensional problems in intelligent computation and engineering design.
文摘At present, salient object detection (SOD) has achieved considerable progress. However, the methods that perform well still face the issue of inadequate detection accuracy. For example, sometimes there are problems of missed and false detections. Effectively optimizing features to capture key information and better integrating different levels of features to enhance their complementarity are two significant challenges in the domain of SOD. In response to these challenges, this study proposes a novel SOD method based on multi-strategy feature optimization. We propose the multi-size feature extraction module (MSFEM), which uses the attention mechanism, the multi-level feature fusion, and the residual block to obtain finer features. This module provides robust support for the subsequent accurate detection of the salient object. In addition, we use two rounds of feature fusion and the feedback mechanism to optimize the features obtained by the MSFEM to improve detection accuracy. The first round of feature fusion is applied to integrate the features extracted by the MSFEM to obtain more refined features. Subsequently, the feedback mechanism and the second round of feature fusion are applied to refine the features, thereby providing a stronger foundation for accurately detecting salient objects. To improve the fusion effect, we propose the feature enhancement module (FEM) and the feature optimization module (FOM). The FEM integrates the upper and lower features with the optimized features obtained by the FOM to enhance feature complementarity. The FOM uses different receptive fields, the attention mechanism, and the residual block to more effectively capture key information. Experimental results demonstrate that our method outperforms 10 state-of-the-art SOD methods.
基金supported in part by the Natural Science Youth Foundation of Hebei Province under Grant F2019403207in part by the PhD Research Startup Foundation of Hebei GEO University under Grant BQ2019055+3 种基金in part by the Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing under Grant KLIGIP-2021A06in part by the Fundamental Research Funds for the Universities in Hebei Province under Grant QN202220in part by the Science and Technology Research Project for Universities of Hebei under Grant ZD2020344in part by the Guangxi Natural Science Fund General Project under Grant 2021GXNSFAA075029.
文摘In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.
基金supported by Grant(42271391 and 62006214)from National Natural Science Foundation of Chinaby Grant(8091B022148)from Joint Funds of Equipment Pre-Research and Ministry of Education of China+1 种基金by Grant(2023BIB015)from Special Project of Hubei Key Research and Development Programby Grant(KLIGIP-2021B03)from Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing.
文摘Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,it also has certain drawbacks for the exploration stage and the egg hatch process,resulting in slow convergence speed and inferior solution quality.To address the above issues,a novel multi-strategy improved SO(MISO)with the assistance of population crowding analysis is proposed in this article.In the algorithm,a novel multi-strategy operator is designed for the exploration stage,which not only focuses on using the information of better performing individuals to improve the quality of solution,but also focuses on maintaining population diversity.To boost the efficiency of the egg hatch process,the multi-strategy egg hatch process is proposed to regenerate individuals according to the results of the population crowding analysis.In addition,a local search method is employed to further enhance the convergence speed and the local search capability.MISO is first compared with three sets of algorithms in the CEC2020 benchmark functions,including SO with its two recently discussed variants,ten advanced MAs,and six powerful CEC competition algorithms.The performance of MISO is then verified on five practical engineering design problems.The experimental results show that MISO provides a promising performance for the above optimization cases in terms of convergence speed and solution quality.
文摘Self-serving,rational agents sometimes cooperate to their mutual benefit.The two-player iterated prisoner′s dilemma game is a model for including the emergence of cooperation.It is generally believed that there is no simple ultimatum strategy which a player can control the return of the other participants.The zero-determinant strategy in the iterated prisoner′s dilemma dramatically expands our understanding of the classic game by uncovering strategies that provide a unilateral advantage to sentient players pitted against unwitting opponents.However,strategies in the prisoner′s dilemma game are only two strategies.Are there these results for general multi-strategy games?To address this question,the paper develops a theory for zero-determinant strategies for multi-strategy games,with any number of strategies.The analytical results exhibit a similar yet different scenario to the case of two-strategy games.The results are also applied to the Snowdrift game,the Hawk-Dove game and the Chicken game.
基金supported in part by the National Natural Sciences Foundation of China (NSFC) under Grant 61525103the National Natural Sciences Foundation of China under Grant 61501140the Shenzhen Fundamental Research Project under Grant JCYJ20150930150304185
文摘Dynamic spectrum access(DSA) based on cognitive radios(CR) technique is an effective approach to address the "spectrum scarcity" issue. However, traditional CR-enabled DSA system employs only single DSA strategy, which might not be suited to the dynamic network environment. In this paper, we propose a multi-strategy DSA(MS-DSA) system, where the primary and the secondary system share spectrum resources with multiple DSA strategies simultaneously. To analyze the performance of the proposed MS-DSA system, we model it as a continuous-time Markov chain(CTMC) and derive the expressions to compute the corresponding performance metrics. Based on this, we define a utility function involving the concerns of effective throughput, interference quantity on primary users, and spectrum leasing cost. Two optimization schemes, named as spectrum allocation and false alarm probability selection, are proposed to maximize the utility function. Finally, numerical simulations are provided to validate our analysis and demonstrate that the performance can be significantly improved caused by virtues of the proposed MS-DSA system.
文摘This paper studies the difference algorithm parameters characteristic of the multicast routing problem, and to compare it with genetic algorithms. The algorithm uses the path of individual coding, combined with the differential cross-choice strategy and operations optimization. Finally, we simulated 30 node networks, and compared the performance of genetic algorithm and differential evolution algorithm. Experimental results show that multi-strategy Differential Evolution algorithm converges faster and better global search ability and stability.
基金Project supported by the National Natural Science Foundation of China(No.62102130)Central Government Guides Local Science and Technology Development Fund Project+4 种基金China(No.226Z0201G)Natural Science Foundation of Hebei ProvinceChina(Nos.F2020204003 and F2024204001)Science and Technology Research Projects of Higher Education Institutions in Hebei ProvinceChina(No.QN2024138)。
文摘The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization problem.Existing optimization approaches often suffer from slow convergence and a propensity to converge to local optima,limiting their effectiveness in achieving optimal power efficiency.First,we propose a novel multi-strategy fusion memetic algorithm(MFMA).MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor(COA-OLA),complemented by population management through truncation selection.Second,leveraging MFMA,we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit power.Experimental results based on Microelectronics Center of North Carolina(MCNC)benchmark circuits demonstrate significant improvements over existing power optimization approaches.MFMA achieves a maximum power saving rate of 72.30%and an average optimization rate of 43.37%;it searches for solutions faster and with higher quality,validating its effectiveness and superiority in power optimization.
基金supported by National Science Foundation of China(Grant No.52075152)Xining Big Data Service Administration.
文摘TheHoney Badger Algorithm(HBA)is a novelmeta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers.The dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA,which has been applied in photovoltaic systems and optimization problems effectively.However,HBA tends to suffer from the local optimum and low convergence.To alleviate these challenges,an improved HBA(IHBA)through fusing multi-strategies is presented in the paper.It introduces Tent chaotic mapping and composite mutation factors to HBA,meanwhile,the random control parameter is improved,moreover,a diversified updating strategy of position is put forward to enhance the advantage between exploration and exploitation.IHBA is compared with 7 meta-heuristic algorithms in 10 benchmark functions and 5 engineering problems.The Wilcoxon Rank-sum Test,Friedman Test and Mann-WhitneyU Test are conducted after emulation.The results indicate the competitiveness and merits of the IHBA,which has better solution quality and convergence traits.The source code is currently available from:https://github.com/zhaotao789/IHBA.
基金Supported by the National Key R&D Program of China(2022ZD0119001).
文摘In this paper,a multi-strategy improved coati optimization algorithm(MICOA)for engineering applications is proposed to improve the performance of the coati optimization algorithm(COA)in terms of convergence speed and convergence accuracy.First,a chaotic mapping is applied to initial-ize the population in order to improve the quality of the population and thus the convergence speed of the algorithm.Second,the prey’s position is improved during the prey-hunting phase.Then,the COA is combined with the particle swarm optimization(PSO)and the golden sine algorithm(Gold-SA),and the position is updated with probabilities to avoid local extremes.Finally,a population decreasing strategy is applied as a way to improve the performance of the algorithm in a comprehen-sive approach.The paper compares the proposed algorithm MICOA with 7 well-known meta-heuristic optimization algorithms and evaluates the algorithm in 23 test functions as well as engineering appli-cation.Experimental results show that the MICOA proposed in this paper has good effectiveness and superiority,and has a strong competitiveness compared with the comparison algorithms.
基金supported by the National Natural Science Foundation of China under Grant[72431009,72171210,72350710798]Hainan Provincial Natural Science Foundation of China under Grant[522MS036]Zhejiang Provincial Natural Science Foundation of China under Grant[LZ23E080002].
文摘Active traffic management(ATM)enhances highway capacity,yet single strategies are inadequate for increasing traffic demands,and poorly integrated multi-strategy approaches can degrade performance.This paper introduces a coupled multi-strategy ATM framework leveraging a multi-agent deep deterministic policy gradient with a communication protocol(MADDPGCP).This method stabilizes training via shared agent observations.The study also investigates the influence of compliance degree on control performance in mixed traffic comprising connected/automated vehicles(CAVs)and human-driven vehicles(HDVs).Numerical experiments with real-world data confirm MADDPG-CP's stable convergence.The proposed MADDPG-CPbased multi-strategy ATM,validated against traditional methods,significantly improves highway capacity utilization by elucidating individual strategy mechanisms and verifying control efficiency.
基金supported by the National Key R&D Program of China(2018YFB0905600)。
文摘The high-nickel layered cathodes Li[Ni_(x)Co_(y)Mn_(1-x-y)]O_(2)(x≥0.8)with high specific capacity and long cycle life are considered as prospective cathodes for lithium-ion batteries.However,the microcrack formation and poor structural stability give rise to inferior rate performance and undesirable cycling life.Herein,we propose a dual modification strategy combining primary particle structure design and element doping to modify Li[Ni_(0.95)Co_(0.025)Mn_(0.025)]O_(2) cathode by tungsten and fluorine co-doped(W-F-NCM95).The doping of W can convert the microstructure of primary particles to the unique rod-like shape,which is beneficial to enhance the reversibility of phase transition and alleviate the generation of microcracks.F doping is conducive to alleviating the surface side reactions.Thus,due to the synergistic effect of W,F codoping,the obtained W-F-NCM95 cathodes deliver a high initial capacity of 236.1 mA h g^(-1) at 0.1 C and superior capacity retention of 88.7%over 100 cycles at 0.5 C.Moreover,the capacity still maintains73.8%after 500 cycles at 0.5 C and the texture of primary particle is intact.This work provides an available strategy by W and F co-doping to enhance the electrochemistry performance of high-nickel cathodes for practical application.
基金National Natural Science Foundation of China(No.61204127)Natural Science Foundations of Heilongjiang Province,China(Nos.F2015024,F201334)Young Foundation of Qiqihar University,China(No.2014k-M08)
文摘Ontology mapping is a key interoperability enabler for the semantic web. In this paper,a new ontology mapping approach called ontology mapping based on Bayesian network( OM-BN) is proposed. OM-BN combines the models of ontology and Bayesian Network,and applies the method of Multi-strategy to computing similarity. In OM-BN,the characteristics of ontology,such as tree structure and semantic inclusion relations among concepts,are used during the process of translation from ontology to ontology Bayesian network( OBN). Then the method of Multi-strategy is used to create similarity table( ST) for each concept-node in OBN. Finally,the iterative process of mapping reasoning is used to deduce new mappings from STs,repeatedly.
基金supported by the National Key R&D projects(Grant No.2018YFC0704301)Science and Technology Project of Wuhan Urban and Rural Construction Bureau,China(201943)+2 种基金Research on theory and application of prefabricated building construction management(20201h0439)Wuhan Modou Construction Consulting Co.,Ltd.(20201h0414)Preliminary Study on the Preparation of the 14th Five-Year Plan for Housing and Urban-Rural Development in Hubei Province,China(20202s002).
文摘Construction site layout planning(CSLP)involves strategically placing various facilities to optimize a project.However,real construction sites are complex,making it challenging to consider all construction activities and facilities comprehensively.Addressing multi-objective layout optimization is crucial for CSLP.Previous optimization results often lacked precision,imposed stringent boundary constraints,and had limited applications in prefabricated construction.Traditional heuristic algorithms still require improvements in region search strategies and computational efficiency when tackling multi-objective optimization problems.This paper optimizes the prefabricated component construction site layout planning(PCCSLP)by treating construction efficiency and safety risk as objectives within a multi-objective CSLP model.A novel heuristic algorithm,the Hybrid Multi-Strategy Improvement Dung Beetle Optimizer(HMSIDBO),was applied to solve the model due to its balanced capabilities in global exploration and local development.The practicality and effectiveness of this approach were validated through a case study in prefabricated residential construction.The research findings indicate that the HMSIDBO-PCCSLP optimization scheme improved each objective by 18%to 75%compared to the original layout.Compared to Genetic Algorithm(GA),the HMSIDBO demonstrates significantly faster computational speed and higher resolution accuracy.Additionally,in comparison with the Dung Beetle Optimizer(DBO),Particle Swarm Optimization(PSO),and Whale Optimization Algorithm(WOA),HMSIDBO exhibits superior iterative speed and an enhanced ability for global exploration.This paper completes the framework from data collection to multi-objective optimization in-site layout,laying the foundation for implementing intelligent construction site layout practices.
基金supported by the National Key Research and Development Program of China(2021YFB3200304)the National Natural Science Foundation of China(52073031)+2 种基金Beijing Nova Program(Z191100001119047,Z211100002121148)Fundamental Research Funds for the Central Universities(E0EG6801X2)the“Hundred Talents Program”of the Chinese Academy of Science.
文摘Portable energy solutions are highly desired in the era of the Internet of Things for powering various distributed micro-electronic devices.At the same time,the energy crisis and catastrophic global warming are becoming serious problems in the world,emphasizing the urgent need for clean and renewable energy.Here,we report a low-cost,high-performance,and portable hand-driven whirligig structured triboelectric–electromagnetic hybrid nanogenerator(whirligig-HNG)for multi-strategy energy harvesting.The whirligig-HNG comprises a dynamic supercoiling TENG via the pulling-strings and inner-distributed EMGs(variable number)in the rotator.The whirligig structure can readily convert linear displacement in low frequency into rotary motion in extremely high frequency.Based on this ingenious design,the whirligig-HNG is capable to harvest the triboelectric energy from the supercoiling/uncoiling process from the pulling strings and simultaneously utilize the high-frequency rotation energy via electromagnetic induction.We have systematically investigated the working mecha-nism of the whirligig-HNG for coupled energy harvesting and compared the individual characteristics of TENG and EMG.The whirligig-HNG is successfully demonstrated to light up more than 100 commercial light-emitting diodes(LEDs)and drive portable electronics.This research presents the enormous potential of whirligig-HNG as a manual and portable power supply for powering various portable electronics.
