As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UA...As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UAVs. Existing weapon-target assignment methods primarily focus on macro cluster constraints while neglecting individual strategy updates. This paper proposes a novel weapon-target assignment method for UAVs based on the multi-strategy threshold public goods game(PGG). By analyzing the concept mapping between weapon-target assignment for UAVs and multi-strategy threshold PGG, a weapon-target assignment model for UAVs based on the multi-strategy threshold PGG is established, which is adaptively complemented by the diverse cooperation-defection strategy library and the utility function based on the threshold mechanism. Additionally, a multi-chain Markov is formulated to quantitatively describe the stochastic evolutionary dynamics, whose evolutionary stable distribution is theoretically derived through the development of a strategy update rule based on preference-based aspiration dynamic. Numerical simulation results validate the feasibility and effectiveness of the proposed method, and the impacts of selection intensity, preference degree and threshold on the evolutionary stable distribution are analyzed. Comparative simulations show that the proposed method outperforms GWO, DE, and NSGA-II, achieving 17.18% higher expected utility than NSGA-II and reducing evolutionary stable times by 25% in large-scale scenario.展开更多
The basic concepts and models of weapon-target assignment （WTA） are introduced and the mathematical nature of the WTA models is also analyzed. A systematic survey of research on WTA problem is provided. The present ...The basic concepts and models of weapon-target assignment （WTA） are introduced and the mathematical nature of the WTA models is also analyzed. A systematic survey of research on WTA problem is provided. The present research on WTA is focused on models and algorithms. In the research on models of WTA, the static WTA models are mainly studied and the dynamic WTA models are not fully studied in deed. In the research on algorithms of WTA, the intelligent algorithms are often used to solve the WTA problem. The small scale of static WTA problems has been solved very well, however, the large scale of dynamic WTA problems has not been solved effectively so far. Finally, the characteristics of dynamic WTA are analyzed and directions for the future research on dynamic WTA are discussed.展开更多
Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model base...Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.展开更多
The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-d...The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-dominated sorting genetic algorithm-II(NSGA-II)called the non-dominated shuffled frog leaping algorithm(NSFLA)is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints.In NSFLA,the shuffled frog leaping algorithm(SFLA)is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm(GA),displaying low optimization speed and heterogeneous space search defects.Two improvements have also been raised to promote the internal optimization performance of SFLA.Firstly,the local evolution scheme,a novel crossover mechanism,ensures that each individual participates in updating instead of only the worst ones,which can expand the diversity of the population.Secondly,a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search.Finally,the scheme is verified in various air combat scenarios.The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency,especially in many aircraft and the dynamic air combat environment.展开更多
Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decisionmaking,such as large computational amount,slow solution speed,and low calculation accuracy,combined with deep reinfo...Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decisionmaking,such as large computational amount,slow solution speed,and low calculation accuracy,combined with deep reinforcement learning theory,an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed,which uses a double noise mechanism to expand the search range of the action,and introduces a priority experience playback mechanism to effectively achieve data utilization.Finally,the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield.The results of the experiment show that,under the framework of the deep neural network for intelligent weapon-target assignment proposed in this paper,compared to the traditional RELU algorithm,the agent trained with reinforcement learning algorithms,such asDeepDeterministic Policy Gradient algorithm,Asynchronous Advantage Actor-Critic algorithm,Deep Q Network algorithm performs better.It shows that the use of deep reinforcement learning algorithms to solve the weapon-target assignment problem in the field of air defense operations is scientific.In contrast to other reinforcement learning algorithms,the agent trained by the improved Deep Deterministic Policy Gradient algorithm has a higher win rate and reward in confrontation,and the use of weapon resources is more efficient.It shows that the model and algorithm have certain superiority and rationality.The results of this paper provide new ideas for solving the problemof weapon-target assignment in air defense combat command decisions.展开更多
The allocation of heterogeneous battlefield resources is crucial in Command and Control(C2).Balancing multiple competing objectives under complex constraints so as to provide decisionmakers with diverse feasible candi...The allocation of heterogeneous battlefield resources is crucial in Command and Control(C2).Balancing multiple competing objectives under complex constraints so as to provide decisionmakers with diverse feasible candidate decision schemes remains an urgent challenge.Based on these requirements,a constrained multi-objective multi-stage weapon-target assignment(CMOMWTA)model is established in this paper.To solve this problem,three constraint-feature-guided multi-objective evolutionary algorithms(CFG-MOEAs)are proposed under three typical multi-objective evolutionary frameworks(i.e.,NSGA-Ⅱ,NSGA-Ⅲ,and MOEA/D)to obtain various high-quality candidate decision schemes.Firstly,a constraint-feature-guided reproduction strategy incorporating crossover,mutation,and repair is developed to handle complex constraints.It extracts common row and column features from different linear constraints to generate the feasible offspring population.Then,a variable-length integer encoding method is adopted to concisely denote the decision schemes.Moreover,a hybrid initialization method incorporating both heuristic methods and random sampling is designed to better guide the population.Systemic experiments are conducted on three CFG-MOEAs to verify their effectiveness.The superior algorithm CFG-NSGA-Ⅱamong three CFG-MOEAs is compared with two state-of-the-art CMOMWTA algorithms,and extensive experimental results demonstrate the effectiveness and superiority of CFG-NSGA-Ⅱ.展开更多
INTRODUCTION Reports indicating that culturally and linguistically diverse(CALD)people-often with migrant backgrounds-in Australia and New Zealand are more likely to be placed in compulsory community treatment(CCT)hav...INTRODUCTION Reports indicating that culturally and linguistically diverse(CALD)people-often with migrant backgrounds-in Australia and New Zealand are more likely to be placed in compulsory community treatment(CCT)have rightlyraised concernsthat such action might be discriminatory.展开更多
With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,whic...With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient workers.In this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few workers.Specifically,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each worker.Then,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate tasks.Only when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start issue.More importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker pool.Finally,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.展开更多
Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to en...Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.展开更多
The multi-target assignment(MTA)problem,a crucial challenge in command control,mission planning,and a fundamental research focus in military operations,has garnered significant attention over the years.Extensively stu...The multi-target assignment(MTA)problem,a crucial challenge in command control,mission planning,and a fundamental research focus in military operations,has garnered significant attention over the years.Extensively studied across various domains such as land,sea,air,space,and electronics,the MTA problem has led to the emergence of numerous models and algorithms.To delve deeper into this field,this paper starts by conducting a bibliometric analysis on 463 Scopus database papers using CiteSpace software.The analysis includes examining keyword clustering,co-occurrence,and burst,with visual representations of the results.Following this,the paper provides an overview of current classification and modeling techniques for addressing the MTA problem,distinguishing between static multi-target assignment(SMTA)and dynamic multi-target assignment(DMTA).Subsequently,existing solution algorithms for the MTA problem are reviewed,generally falling into three categories:exact algorithms,heuristic algorithms,and machine learning algorithms.Finally,a development framework is proposed based on the"HIGH"model(high-speed,integrated,great,harmonious)to guide future research and intelligent weapon system development concerning the MTA problem.This framework emphasizes application scenarios,modeling mechanisms,solution algorithms,and system efficiency to offer a roadmap for future exploration in this area.展开更多
To adapt to the unique demand-supply features of accessory parking lots at passenger transport hubs,a mixed parking demand assignment method based on regression modeling is proposed.First,an optimal model aiming to mi...To adapt to the unique demand-supply features of accessory parking lots at passenger transport hubs,a mixed parking demand assignment method based on regression modeling is proposed.First,an optimal model aiming to minimize total time expenditure is constructed.It incorporates parking search time,walking time,and departure time,focusing on short-term parking features.Then,the information dimensions that the parking lot can obtain are evaluated,and three assignment strategies based on three types of regression models-linear regression(LR),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP)-are proposed.A parking process simulation model is built using the traffic simulation package SUMO to facilitate data collection,model training,and case studies.Finally,the performance of the three strategies is com-pared,revealing that the XGBoost-based strategy performs the best in case parking lots,which reduces time expendi-ture by 29.3%and 37.2%,respectively,compared with the MLP-based strategy and LR-based strategy.This method offers diverse options for practical parking manage-ment.展开更多
Permanent Magnet Synchronous Motors(PMSMs)are widely employed in high-performance drive applications due to their superior efficiency and dynamic capabilities.However,their control remains challenging owing to nonline...Permanent Magnet Synchronous Motors(PMSMs)are widely employed in high-performance drive applications due to their superior efficiency and dynamic capabilities.However,their control remains challenging owing to nonlinear dynamics,parameter variations,and unmeasurable external disturbances,particularly load torquefluctuations.This study proposes an enhanced Interconnection and Damp-ing Assignment Passivity-Based Control(IDA-PBC)scheme,formulated within the port-controlled Hamiltonian(PCH)framework,to address these limitations.A nonlinear disturbance observer is embedded to estimate and compensate,in real time,for lumped mis-matched disturbances arising from parameter uncertainties and external loads.Additionally,aflatness-based control strategy is employed to generate the desired current references within the nonlinear drive system,ensuring accurate tracking of time-varying speed commands.This integrated approach preserves the system’s energy-based structure,enabling systematic stability analysis while enhancing robustness.The proposed control architecture also maintains low complexity with a limited number of tunable parameters,facilitating practical implementation.Simulation and experimental results under various operating conditions demonstrate the effectiveness and robustness of the proposed method.Comparative analysis with conventional proportional-integral(PI)control and standard IDA-PBC strategies confirms its capability to handle disturbances and maintain dynamic performance.展开更多
Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and miss...Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.展开更多
The dynamic weapon-target assignment （DWTA） problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, ...The dynamic weapon-target assignment （DWTA） problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general ＂virtual＂ representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.展开更多
It is of great significance for headquarters in warfare to address the weapon-target assignment(WTA)problem with distributed computing nodes to attack targets simultaneously from different weapon units.However,the com...It is of great significance for headquarters in warfare to address the weapon-target assignment(WTA)problem with distributed computing nodes to attack targets simultaneously from different weapon units.However,the computing nodes on the battlefield are vulnerable to be attacked and the communication environment is usually unreliable.To solve the WTA problems in unreliable environments,this paper proposes a scheme based on decentralized peer-to-peer architecture and adapted artificial bee colony(ABC)optimization algorithm.In the decentralized architecture,the peer computing node is distributed to each weapon units and the packet loss rate is used to simulate the unreliable communication environment.The decisions made in each peer node will be merged into the decision set to carry out the optimal decision in the decentralized system by adapted ABC algorithm.The experimental results demonstrate that the decentralized peer-to-peer architecture perform an extraordinary role in the unreliable communication environment.The proposed scheme preforms outstanding results of enemy residual value(ERV)with the packet loss rate in the range from 0 to 0.9.展开更多
The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment pro...The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.展开更多
Acanthopagrus latus is an essential aquaculture species on the south coast of China.However,there is a lack of systematic breeding of A.latus,which considerably limits the sustainable development of A.latus.As a resul...Acanthopagrus latus is an essential aquaculture species on the south coast of China.However,there is a lack of systematic breeding of A.latus,which considerably limits the sustainable development of A.latus.As a result,genetic improvements are urgently needed to breed new strains of A.latus with rapid growth and strong resistance to disease.During selective breeding,it is necessary to estimate the genetic parameters of the target trait,which in turn depends on an accurate disentangled pedigree for the selective population.Therefore,it is necessary to establish the parentage assignment technique for A.latus.In this study,95 individuals selected from their parents and their 14 families were used as experimental material.SNPs were developed by genome resequencing,and highly polymorphic SNPs were screened on the basis of optimized filtering parameters.A total of 14392738 SNPs were discovered and 205 SNPs were selected for parentage assignment using the CERVUS software.In the model where the gender of the parents is known,the assignment success rate is 98.61%for the male parent,97.22%for the female parent,and 95.83%for the parent pair.In the model where the gender of the parents is unknown,the assignment success rate is 100%for a single parent and 90.28%for the parent pair.The results of this study were expected to serve as a reference for the breeding of new varieties of A.latus.展开更多
In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver u...In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.展开更多
The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to d...The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to databit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determinethe optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantizationcan effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In thispaper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bitwidth is proposed, and reinforcement learning is used to automatically predict the mixed precision that meets theconstraints of hardware resources. In the state-space design, the standard deviation of weights is used to measurethe distribution difference of data, the execution speed feedback of simulated neural network accelerator inferenceis used as the environment to limit the action space of the agent, and the accuracy of the quantization model afterretraining is used as the reward function to guide the agent to carry out deep reinforcement learning training. Theexperimental results show that the proposed method obtains a suitable model layer-by-layer quantization strategyunder the condition that the computational resources are satisfied, and themodel accuracy is effectively improved.The proposed method has strong intelligence and certain universality and has strong application potential in thefield of mixed precision quantization and embedded neural network model deployment.展开更多
One of the keys in time-dependent routing is determining the weight of each road network link based on traffic information.To facilitate the estimation of the road's weight,Global Position System(GPS)data are comm...One of the keys in time-dependent routing is determining the weight of each road network link based on traffic information.To facilitate the estimation of the road's weight,Global Position System(GPS)data are commonly used in obtaining real-time traffic information.However,the information obtained by taxi-GPS does not cover the entire road network.Aiming at incomplete traffic information on urban roads,this paper proposes a novel fuzzy inference method.It considers the combined effect of road grade,traffic information,and other spatial factors.Taking the third law of geography as the basic premise,that is,the more similar the geographical environment,the more similar the characteristics of the geographical target will be.This method uses a Typical Link Pattern(TLP)model to describe the geographical environment.The TLP represents typical road sections with complete information.Then,it determines the relationship between roads lacking traffic information and the TLPs according to their related factors.After obtaining the TLPs,this method ascertains the weight of road links by calculating their similarities with TLPs based on the theory of fuzzy inference.Aiming at road links at different places,the dividing-conquering strategy and globe algorithm are also introduced to calculate the weight.These two strategies are used to address the excessively fragmented or lengthy links.The experimental results with the case of Newcastle show robustness in that the average Root Mean Square Error(RMSE)is 1.430 mph,and the bias is 0.2%;the overall RMSE is 11.067 mph,and the bias is 0.6%.This article is the first to combine the third law of geography with fuzzy inference,which significantly improves the estimation accuracy of road weights with incomplete information.Empirical application and validation show that the method can accurately predict vehicle speed under incomplete information.展开更多
基金supported by the National Natural Science Foundation of China (No. 62073267)。
文摘As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UAVs. Existing weapon-target assignment methods primarily focus on macro cluster constraints while neglecting individual strategy updates. This paper proposes a novel weapon-target assignment method for UAVs based on the multi-strategy threshold public goods game(PGG). By analyzing the concept mapping between weapon-target assignment for UAVs and multi-strategy threshold PGG, a weapon-target assignment model for UAVs based on the multi-strategy threshold PGG is established, which is adaptively complemented by the diverse cooperation-defection strategy library and the utility function based on the threshold mechanism. Additionally, a multi-chain Markov is formulated to quantitatively describe the stochastic evolutionary dynamics, whose evolutionary stable distribution is theoretically derived through the development of a strategy update rule based on preference-based aspiration dynamic. Numerical simulation results validate the feasibility and effectiveness of the proposed method, and the impacts of selection intensity, preference degree and threshold on the evolutionary stable distribution are analyzed. Comparative simulations show that the proposed method outperforms GWO, DE, and NSGA-II, achieving 17.18% higher expected utility than NSGA-II and reducing evolutionary stable times by 25% in large-scale scenario.
基金This project was supported by the National Defense Pre-Research Foundation of China
文摘The basic concepts and models of weapon-target assignment （WTA） are introduced and the mathematical nature of the WTA models is also analyzed. A systematic survey of research on WTA problem is provided. The present research on WTA is focused on models and algorithms. In the research on models of WTA, the static WTA models are mainly studied and the dynamic WTA models are not fully studied in deed. In the research on algorithms of WTA, the intelligent algorithms are often used to solve the WTA problem. The small scale of static WTA problems has been solved very well, however, the large scale of dynamic WTA problems has not been solved effectively so far. Finally, the characteristics of dynamic WTA are analyzed and directions for the future research on dynamic WTA are discussed.
文摘Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.
基金supported by the National Natural Science Foundation of China(61673209,71971115)。
文摘The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-dominated sorting genetic algorithm-II(NSGA-II)called the non-dominated shuffled frog leaping algorithm(NSFLA)is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints.In NSFLA,the shuffled frog leaping algorithm(SFLA)is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm(GA),displaying low optimization speed and heterogeneous space search defects.Two improvements have also been raised to promote the internal optimization performance of SFLA.Firstly,the local evolution scheme,a novel crossover mechanism,ensures that each individual participates in updating instead of only the worst ones,which can expand the diversity of the population.Secondly,a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search.Finally,the scheme is verified in various air combat scenarios.The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency,especially in many aircraft and the dynamic air combat environment.
基金funded by the Project of the National Natural Science Foundation of China,Grant Number 62106283.
文摘Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decisionmaking,such as large computational amount,slow solution speed,and low calculation accuracy,combined with deep reinforcement learning theory,an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed,which uses a double noise mechanism to expand the search range of the action,and introduces a priority experience playback mechanism to effectively achieve data utilization.Finally,the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield.The results of the experiment show that,under the framework of the deep neural network for intelligent weapon-target assignment proposed in this paper,compared to the traditional RELU algorithm,the agent trained with reinforcement learning algorithms,such asDeepDeterministic Policy Gradient algorithm,Asynchronous Advantage Actor-Critic algorithm,Deep Q Network algorithm performs better.It shows that the use of deep reinforcement learning algorithms to solve the weapon-target assignment problem in the field of air defense operations is scientific.In contrast to other reinforcement learning algorithms,the agent trained by the improved Deep Deterministic Policy Gradient algorithm has a higher win rate and reward in confrontation,and the use of weapon resources is more efficient.It shows that the model and algorithm have certain superiority and rationality.The results of this paper provide new ideas for solving the problemof weapon-target assignment in air defense combat command decisions.
基金supported by the National Science Fund of China under Grant Nos.62425304 and 62025301the Basic Science Center Programs of NSFC under Grant No.62088101+1 种基金Shanghai Municipal Science and Technology Major Project under Grant No.2021SHZDZX0100Shanghai Municipal Commission of Science and Technology Project under Grant No.19511132101。
文摘The allocation of heterogeneous battlefield resources is crucial in Command and Control(C2).Balancing multiple competing objectives under complex constraints so as to provide decisionmakers with diverse feasible candidate decision schemes remains an urgent challenge.Based on these requirements,a constrained multi-objective multi-stage weapon-target assignment(CMOMWTA)model is established in this paper.To solve this problem,three constraint-feature-guided multi-objective evolutionary algorithms(CFG-MOEAs)are proposed under three typical multi-objective evolutionary frameworks(i.e.,NSGA-Ⅱ,NSGA-Ⅲ,and MOEA/D)to obtain various high-quality candidate decision schemes.Firstly,a constraint-feature-guided reproduction strategy incorporating crossover,mutation,and repair is developed to handle complex constraints.It extracts common row and column features from different linear constraints to generate the feasible offspring population.Then,a variable-length integer encoding method is adopted to concisely denote the decision schemes.Moreover,a hybrid initialization method incorporating both heuristic methods and random sampling is designed to better guide the population.Systemic experiments are conducted on three CFG-MOEAs to verify their effectiveness.The superior algorithm CFG-NSGA-Ⅱamong three CFG-MOEAs is compared with two state-of-the-art CMOMWTA algorithms,and extensive experimental results demonstrate the effectiveness and superiority of CFG-NSGA-Ⅱ.
文摘INTRODUCTION Reports indicating that culturally and linguistically diverse(CALD)people-often with migrant backgrounds-in Australia and New Zealand are more likely to be placed in compulsory community treatment(CCT)have rightlyraised concernsthat such action might be discriminatory.
基金supported by the National Natural Science Foundation of China under Grant No.62072475 and No.62302062in part by the Hunan Provincial Natural Science Foundation of China under Grant Number 2023JJ40081。
文摘With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient workers.In this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few workers.Specifically,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each worker.Then,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate tasks.Only when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start issue.More importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker pool.Finally,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.
基金supported by the National Natural Science Foundation of China(No.92371206)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX2023063).
文摘Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.
基金the financial support provided by the National Natural Science Foundation of China(NSFC)(Grant No.62173274)the National Key R&D Program of China(Grant No.2019YFA0405300)+4 种基金the Natural Science Foundation of Hunan Province of China(Grant No.2021JJ10045)the Practice and Innovation Funds for Graduate Students of Northwestern Polytechnical University(Grant No.PF2023046)the Open Research Subject of State Key Laboratory of Intelligent Game(Grant No.ZBKF-24-01)the Postdoctoral Fellowship Program of CPSF(No.GZB20240989)the China Postdoctoral Science Foundation(Grant No.2024M754304)。
文摘The multi-target assignment(MTA)problem,a crucial challenge in command control,mission planning,and a fundamental research focus in military operations,has garnered significant attention over the years.Extensively studied across various domains such as land,sea,air,space,and electronics,the MTA problem has led to the emergence of numerous models and algorithms.To delve deeper into this field,this paper starts by conducting a bibliometric analysis on 463 Scopus database papers using CiteSpace software.The analysis includes examining keyword clustering,co-occurrence,and burst,with visual representations of the results.Following this,the paper provides an overview of current classification and modeling techniques for addressing the MTA problem,distinguishing between static multi-target assignment(SMTA)and dynamic multi-target assignment(DMTA).Subsequently,existing solution algorithms for the MTA problem are reviewed,generally falling into three categories:exact algorithms,heuristic algorithms,and machine learning algorithms.Finally,a development framework is proposed based on the"HIGH"model(high-speed,integrated,great,harmonious)to guide future research and intelligent weapon system development concerning the MTA problem.This framework emphasizes application scenarios,modeling mechanisms,solution algorithms,and system efficiency to offer a roadmap for future exploration in this area.
基金The National Natural Science Foundation of China(No.52302388)the Natural Science Foundation of Jiangsu Province(No.BK20230853).
文摘To adapt to the unique demand-supply features of accessory parking lots at passenger transport hubs,a mixed parking demand assignment method based on regression modeling is proposed.First,an optimal model aiming to minimize total time expenditure is constructed.It incorporates parking search time,walking time,and departure time,focusing on short-term parking features.Then,the information dimensions that the parking lot can obtain are evaluated,and three assignment strategies based on three types of regression models-linear regression(LR),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP)-are proposed.A parking process simulation model is built using the traffic simulation package SUMO to facilitate data collection,model training,and case studies.Finally,the performance of the three strategies is com-pared,revealing that the XGBoost-based strategy performs the best in case parking lots,which reduces time expendi-ture by 29.3%and 37.2%,respectively,compared with the MLP-based strategy and LR-based strategy.This method offers diverse options for practical parking manage-ment.
基金supported in part by an International Research Partnership“Electrical Engineering-Thai French Research Center(EE-TFRC)”under the project framework of the Lorraine Universite´d’Excellence(LUE)in cooperation between Universite´de Lorraine(France)and King Mongkut’s University of Technology North Bangkok(year 2021-2024/2025-28)by the National Research Council of Thailand(NRCT)under Research Team Promotion Grant(Senior Research Scholar Program)under Grant No.N42A 680561by the NSRF via the Program Management Unit for Human Resources&Institutional Development,Research and Innovation under Research project Grant No.B41G680025.
文摘Permanent Magnet Synchronous Motors(PMSMs)are widely employed in high-performance drive applications due to their superior efficiency and dynamic capabilities.However,their control remains challenging owing to nonlinear dynamics,parameter variations,and unmeasurable external disturbances,particularly load torquefluctuations.This study proposes an enhanced Interconnection and Damp-ing Assignment Passivity-Based Control(IDA-PBC)scheme,formulated within the port-controlled Hamiltonian(PCH)framework,to address these limitations.A nonlinear disturbance observer is embedded to estimate and compensate,in real time,for lumped mis-matched disturbances arising from parameter uncertainties and external loads.Additionally,aflatness-based control strategy is employed to generate the desired current references within the nonlinear drive system,ensuring accurate tracking of time-varying speed commands.This integrated approach preserves the system’s energy-based structure,enabling systematic stability analysis while enhancing robustness.The proposed control architecture also maintains low complexity with a limited number of tunable parameters,facilitating practical implementation.Simulation and experimental results under various operating conditions demonstrate the effectiveness and robustness of the proposed method.Comparative analysis with conventional proportional-integral(PI)control and standard IDA-PBC strategies confirms its capability to handle disturbances and maintain dynamic performance.
文摘Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.
基金Supported by the National Natural Science Foundation of China (Grant No. 60374069)the Foundation of the Key Laboratory of Complex Systems and Intelligent Science, Institute of Automation, Chinese Academy of Sciences (Grant No. 20060104)
文摘The dynamic weapon-target assignment （DWTA） problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general ＂virtual＂ representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.
基金This work was supported by the Foundation for Distinguished Young Scholars of Fujian Agriculture and Forestry University(xjq201809)MOST of Taiwan(107-2623-E-009-006-D).
文摘It is of great significance for headquarters in warfare to address the weapon-target assignment(WTA)problem with distributed computing nodes to attack targets simultaneously from different weapon units.However,the computing nodes on the battlefield are vulnerable to be attacked and the communication environment is usually unreliable.To solve the WTA problems in unreliable environments,this paper proposes a scheme based on decentralized peer-to-peer architecture and adapted artificial bee colony(ABC)optimization algorithm.In the decentralized architecture,the peer computing node is distributed to each weapon units and the packet loss rate is used to simulate the unreliable communication environment.The decisions made in each peer node will be merged into the decision set to carry out the optimal decision in the decentralized system by adapted ABC algorithm.The experimental results demonstrate that the decentralized peer-to-peer architecture perform an extraordinary role in the unreliable communication environment.The proposed scheme preforms outstanding results of enemy residual value(ERV)with the packet loss rate in the range from 0 to 0.9.
基金supported by the Basic Scientific Research Business Expenses of Central Universities(3072022QBZ0806)。
文摘The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.
基金Fujian Province science and technology plan project under contract No.2023N0011。
文摘Acanthopagrus latus is an essential aquaculture species on the south coast of China.However,there is a lack of systematic breeding of A.latus,which considerably limits the sustainable development of A.latus.As a result,genetic improvements are urgently needed to breed new strains of A.latus with rapid growth and strong resistance to disease.During selective breeding,it is necessary to estimate the genetic parameters of the target trait,which in turn depends on an accurate disentangled pedigree for the selective population.Therefore,it is necessary to establish the parentage assignment technique for A.latus.In this study,95 individuals selected from their parents and their 14 families were used as experimental material.SNPs were developed by genome resequencing,and highly polymorphic SNPs were screened on the basis of optimized filtering parameters.A total of 14392738 SNPs were discovered and 205 SNPs were selected for parentage assignment using the CERVUS software.In the model where the gender of the parents is known,the assignment success rate is 98.61%for the male parent,97.22%for the female parent,and 95.83%for the parent pair.In the model where the gender of the parents is unknown,the assignment success rate is 100%for a single parent and 90.28%for the parent pair.The results of this study were expected to serve as a reference for the breeding of new varieties of A.latus.
基金supported by the Key Research and Development Program of China(No.2022YFC3005401)Key Research and Development Program of China,Yunnan Province(No.202203AA080009,202202AF080003)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX21_0482).
文摘In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.
文摘The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to databit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determinethe optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantizationcan effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In thispaper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bitwidth is proposed, and reinforcement learning is used to automatically predict the mixed precision that meets theconstraints of hardware resources. In the state-space design, the standard deviation of weights is used to measurethe distribution difference of data, the execution speed feedback of simulated neural network accelerator inferenceis used as the environment to limit the action space of the agent, and the accuracy of the quantization model afterretraining is used as the reward function to guide the agent to carry out deep reinforcement learning training. Theexperimental results show that the proposed method obtains a suitable model layer-by-layer quantization strategyunder the condition that the computational resources are satisfied, and themodel accuracy is effectively improved.The proposed method has strong intelligence and certain universality and has strong application potential in thefield of mixed precision quantization and embedded neural network model deployment.
基金supported by the National Key Research and Development Program of China[grant number 2019YFC1804304]the National Natural Science Foundation of China[grant number 41771478]the Fundamental Research Funds for the Central Universities[grant number 2019B02514].
文摘One of the keys in time-dependent routing is determining the weight of each road network link based on traffic information.To facilitate the estimation of the road's weight,Global Position System(GPS)data are commonly used in obtaining real-time traffic information.However,the information obtained by taxi-GPS does not cover the entire road network.Aiming at incomplete traffic information on urban roads,this paper proposes a novel fuzzy inference method.It considers the combined effect of road grade,traffic information,and other spatial factors.Taking the third law of geography as the basic premise,that is,the more similar the geographical environment,the more similar the characteristics of the geographical target will be.This method uses a Typical Link Pattern(TLP)model to describe the geographical environment.The TLP represents typical road sections with complete information.Then,it determines the relationship between roads lacking traffic information and the TLPs according to their related factors.After obtaining the TLPs,this method ascertains the weight of road links by calculating their similarities with TLPs based on the theory of fuzzy inference.Aiming at road links at different places,the dividing-conquering strategy and globe algorithm are also introduced to calculate the weight.These two strategies are used to address the excessively fragmented or lengthy links.The experimental results with the case of Newcastle show robustness in that the average Root Mean Square Error(RMSE)is 1.430 mph,and the bias is 0.2%;the overall RMSE is 11.067 mph,and the bias is 0.6%.This article is the first to combine the third law of geography with fuzzy inference,which significantly improves the estimation accuracy of road weights with incomplete information.Empirical application and validation show that the method can accurately predict vehicle speed under incomplete information.
