Multi-objective games(MOGs)have received much attention in recent years as a class of games with vector payoffs.Based on the semi-tensor product(STP),this paper discusses the MOG,including the existence,finite-step re...Multi-objective games(MOGs)have received much attention in recent years as a class of games with vector payoffs.Based on the semi-tensor product(STP),this paper discusses the MOG,including the existence,finite-step reachability,and finite-step controllability of Pareto equilibrium of this model,from both static and dynamic perspectives.First,the MOG concept is presented using multi-layer graphs,and STP is used to convert the payoff function into its algebraic form.Then,from the static perspective,two necessary and sufficient conditions are proposed to verify whether all players can meet their expectations and whether the strategy profile is a Pareto equilibrium,separately.Furthermore,from the dynamic perspective,a strategy updating rule is designed to investigate the finite-step reachability of the evolutionary MOG.Finally,the finite-step controllability of the evolutionary MOG is analyzed by adding pseudo-players,and a backward search algorithm is provided to find the shortest evolutionary process and control sequence.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f...Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.展开更多
To solve the choice of multi-objective game's equilibria,we construct general bargaining games called self-bargaining games,and define their individual welfare functions with three appropriate axioms.According to ...To solve the choice of multi-objective game's equilibria,we construct general bargaining games called self-bargaining games,and define their individual welfare functions with three appropriate axioms.According to the individual welfare functions,we transform the multi–objective game into a single-objective game and define its bargaining equilibrium,which is a Nash equilibrium of the single-objective game.And then,based on certain continuity and concavity of the multi-objective game's payoff function,we proof the bargaining equilibrium still exists and is also a weakly Pareto-Nash equilibrium.Moreover,we analyze several special bargaining equilibria,and compare them in a few examples.展开更多
Multi-objective optimization for the optimum shape design is introduced in aerodynamics using the Game theory. Based on the control theory, the employed optimizer and the negative feedback are used to implement the co...Multi-objective optimization for the optimum shape design is introduced in aerodynamics using the Game theory. Based on the control theory, the employed optimizer and the negative feedback are used to implement the constraints. All the constraints are satisfied implicitly and automatically in the design. Furthermore,the above methodology is combined with a formulation derived from the Game theory to treat multi-point airfoil optimization. Airfoil shapes are optimized according to various aerodynamics criteria. In the symmetric Nash game, each “player” is responsible for one criterion, and the Nash equilibrium provides a solution to the multipoint optimization. Design results confirm the efficiency of the method.展开更多
There are currently three different game strategies originated in economics： （1） Cooperative games （Pareto front）, （2） Competitive games （Nash game） and （3） Hierarchical games （Stackelberg game）. Each gam...There are currently three different game strategies originated in economics： （1） Cooperative games （Pareto front）, （2） Competitive games （Nash game） and （3） Hierarchical games （Stackelberg game）. Each game achieves different equilibria with different performance, and their players play different roles in the games. Here, we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multi- criteria aerodynamic optimization problems. The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments. We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method. The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front. Non-dominated Pareto front solutions are obtained, however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.展开更多
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition...The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.展开更多
With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm impro...With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.展开更多
This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balanc...This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.展开更多
In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.T...In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.The designed distributed controller consists of two parts:a dynamic average consensus part that asymptotically reproduces the unknown NE,and an adaptive reference-tracking module responsible for steering EL systems’positions to track a desired trajectory.The generalized Barbalat’s Lemma is used to overcome the discontinuity of the closed-loop system caused by the switching networks.The proposed algorithm is illustrated by a sensor network deployment problem.展开更多
In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimizatio...In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.展开更多
For the negative impact of large-scale electric vehicles (EVs) disorderly charging on the power grid, a multi-objective optimization strategy for coordinated charging and discharging of EVs based on Stackelberg game i...For the negative impact of large-scale electric vehicles (EVs) disorderly charging on the power grid, a multi-objective optimization strategy for coordinated charging and discharging of EVs based on Stackelberg game is proposed. As the leader, the grid company aims to stabilize load fluctuations and formulate a reasonable electricity price strategy to guide EVs to participate in vehicle-to-grid (V2G);As followers, EV users optimize their charging plans based on electricity price information with the objective of reducing costs and obtaining good comfort. This paper uses the MOPSO algorithm to solve the proposed multi-objective Stackelberg problem, and calculates the optimization results under various preferences, which proves the effectiveness of the proposed model and method.展开更多
The Stackelberg prediction game(SPG)is a bilevel optimization frame-work for modeling strategic interactions between a learner and a follower.Existing meth-ods for solving this problem with general loss functions are ...The Stackelberg prediction game(SPG)is a bilevel optimization frame-work for modeling strategic interactions between a learner and a follower.Existing meth-ods for solving this problem with general loss functions are computationally expensive and scarce.We propose a novel hyper-gradient type method with a warm-start strategy to address this challenge.Particularly,we first use a Taylor expansion-based approach to obtain a good initial point.Then we apply a hyper-gradient descent method with an ex-plicit approximate hyper-gradient.We establish the convergence results of our algorithm theoretically.Furthermore,when the follower employs the least squares loss function,our method is shown to reach an e-stationary point by solving quadratic subproblems.Numerical experiments show our algorithms are empirically orders of magnitude faster than the state-of-the-art.展开更多
The rapid advance of Connected-Automated Vehicles(CAVs)has led to the emergence of diverse delaysensitive and energy-constrained vehicular applications.Given the high dynamics of vehicular networks,unmanned aerial veh...The rapid advance of Connected-Automated Vehicles(CAVs)has led to the emergence of diverse delaysensitive and energy-constrained vehicular applications.Given the high dynamics of vehicular networks,unmanned aerial vehicles-assisted mobile edge computing(UAV-MEC)has gained attention in providing computing resources to vehicles and optimizing system costs.We model the computing offloading problem as a multi-objective optimization challenge aimed at minimizing both task processing delay and energy consumption.We propose a three-stage hybrid offloading scheme called Dynamic Vehicle Clustering Game-based Multi-objective Whale Optimization Algorithm(DVCG-MWOA)to address this problem.A novel dynamic clustering algorithm is designed based on vehiclemobility and task offloading efficiency requirements,where each UAV independently serves as the cluster head for a vehicle cluster and adjusts its position at the end of each timeslot in response to vehiclemovement.Within eachUAV-led cluster,cooperative game theory is applied to allocate computing resourceswhile respecting delay constraints,ensuring efficient resource utilization.To enhance offloading efficiency,we improve the multi-objective whale optimization algorithm(MOWOA),resulting in the MWOA.This enhanced algorithm determines the optimal allocation of pending tasks to different edge computing devices and the resource utilization ratio of each device,ultimately achieving a Pareto-optimal solution set for delay and energy consumption.Experimental results demonstrate that the proposed joint offloading scheme significantly reduces both delay and energy consumption compared to existing approaches,offering superior performance for vehicular networks.展开更多
The two-player nonzero-sum linear-exponential-quadratic stochastic differential game is studied.The game takes into account the players'attitudes to risk.The nonlinear transformations and change of probability mea...The two-player nonzero-sum linear-exponential-quadratic stochastic differential game is studied.The game takes into account the players'attitudes to risk.The nonlinear transformations and change of probability measure techniques are used to study the existence of both open-loop and closed-loop Nash equilibria for the game.Some examples are constructed to illustrate their differences.Furthermore,theoretical results are applied to solve the risk-sensitive portfolio game problem in the financial market and show the effects of risk attitudes and economic performance on equilibria.展开更多
成果名称:Shapley's Conjecture on the Cores of Abstract Market Games主要作者:曹志刚,秦承忠,杨晓光奖项类别:著作论文奖获奖等级:二等奖获奖论文《Shapley's Conjecture on the Cores of Abstract Market Games》发表于博...成果名称:Shapley's Conjecture on the Cores of Abstract Market Games主要作者:曹志刚,秦承忠,杨晓光奖项类别:著作论文奖获奖等级:二等奖获奖论文《Shapley's Conjecture on the Cores of Abstract Market Games》发表于博弈论领域顶级期刊《Games and Economic Behavior》2018年第2期。论文研究成果初步解决了诺贝尔经济学奖获得者罗伊德·沙普利(Lloyd S. Shapley)提出的抽象市场博弈核非空的猜想。展开更多
This paper proposes a multi-objective optimization design method based on the coalition cooperative game theory where the three design goals have been seen as three game players. By calculating the affecting factors a...This paper proposes a multi-objective optimization design method based on the coalition cooperative game theory where the three design goals have been seen as three game players. By calculating the affecting factors and fuzzy clustering, the design variables are divided into different strategic spaces which belong to each player, then it constructs a payoff function based on the coalition mechanism. Each game player takes its own revenue function as a target and obtains the best strategy versus other players. The best strategies of all players consist of the strategy permutation of a round game and it obtains the final game solutions through multi-round games according to the convergence criterion. A multi-objective optimization example of the luff mechanism of compensative sheave block shows the effectiveness of the coalition cooperative game method.展开更多
This paper presents a comprehensive overview of distributed Nash equilibrium(NE)seeking algorithms in non-cooperative games for multiagent systems(MASs),with a distinct emphasis on the dynamic control perspective.It s...This paper presents a comprehensive overview of distributed Nash equilibrium(NE)seeking algorithms in non-cooperative games for multiagent systems(MASs),with a distinct emphasis on the dynamic control perspective.It specifically focuses on the research addressing distributed NE seeking problems in which agents are governed by heterogeneous dynamics.The paper begins by introducing fundamental concepts of general non-cooperative games and the NE,along with definitions of specific game structures such as aggregative games and multi-cluster games.It then systematically reviews existing studies on distributed NE seeking for various classes of MASs from the viewpoint of agent dynamics,including first-order,second-order,high-order,linear,and Euler-Lagrange(EL)systems.Furthermore,the paper highlights practical applications of these theoretical advances in cooperative control scenarios involving autonomous systems with complex dynamics,such as autonomous surface vessels,autonomous aerial vehicles,and other autonomous vehicles.Finally,the paper outlines several promising directions for future research.展开更多
Background Autism spectrum disorder(ASD)is a pervasive developmental disorder characterized by difficulties in social communication and restricted,repetitive behaviors.Early intervention is essential to improve develo...Background Autism spectrum disorder(ASD)is a pervasive developmental disorder characterized by difficulties in social communication and restricted,repetitive behaviors.Early intervention is essential to improve developmental outcomes in children with ASD.Serious games,which combine educational objectives with game based interactions,have shown potential as tools for early intervention in patients with ASD.However,in China,the development of serious games specifically designed for children with ASD remains in its infancy,with significant gaps in technical frameworks and effective data management methods.Method This paper proposes a framework aimed at facilitating the development of multimodal serious games designed for ASD interventions.We demonstrated the feasibility of the framework by developing and integrating several components,such as web applications,mobile games,and augmented reality games.These tools are interconnected to achieve data connectivity and management.Additionally,adaptive mechanics were employed within the framework to analyze real-time player data,which allowed the game difficulty to be dynamically adjusted and provide a personalized experience for each child.Results The framework successfully integrated various multimodal games,ensuring that real-time data management supported personalized game experiences.This approach ensured that the interventions remained appropriately challenging while still achievable.Conclusion The results indicate that the proposed framework enhances collaboration among therapists,parents,and developers while also improving the effectiveness of ASD interventions.By delivering personalized gameplay experiences that are both challenging and achievable,the framework offers a scalable platform for the future development of serious games.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.62273201 and 62350037)the Taishan Scholar Project of Shandong Province of China(No.TSTP20221103)。
文摘Multi-objective games(MOGs)have received much attention in recent years as a class of games with vector payoffs.Based on the semi-tensor product(STP),this paper discusses the MOG,including the existence,finite-step reachability,and finite-step controllability of Pareto equilibrium of this model,from both static and dynamic perspectives.First,the MOG concept is presented using multi-layer graphs,and STP is used to convert the payoff function into its algebraic form.Then,from the static perspective,two necessary and sufficient conditions are proposed to verify whether all players can meet their expectations and whether the strategy profile is a Pareto equilibrium,separately.Furthermore,from the dynamic perspective,a strategy updating rule is designed to investigate the finite-step reachability of the evolutionary MOG.Finally,the finite-step controllability of the evolutionary MOG is analyzed by adding pseudo-players,and a backward search algorithm is provided to find the shortest evolutionary process and control sequence.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
基金supported by the National Natural Science Foundation of China(No.11271098)by the Science and Technology Fund Program of Guizhou Province(No.7425)。
文摘To solve the choice of multi-objective game's equilibria,we construct general bargaining games called self-bargaining games,and define their individual welfare functions with three appropriate axioms.According to the individual welfare functions,we transform the multi–objective game into a single-objective game and define its bargaining equilibrium,which is a Nash equilibrium of the single-objective game.And then,based on certain continuity and concavity of the multi-objective game's payoff function,we proof the bargaining equilibrium still exists and is also a weakly Pareto-Nash equilibrium.Moreover,we analyze several special bargaining equilibria,and compare them in a few examples.
文摘Multi-objective optimization for the optimum shape design is introduced in aerodynamics using the Game theory. Based on the control theory, the employed optimizer and the negative feedback are used to implement the constraints. All the constraints are satisfied implicitly and automatically in the design. Furthermore,the above methodology is combined with a formulation derived from the Game theory to treat multi-point airfoil optimization. Airfoil shapes are optimized according to various aerodynamics criteria. In the symmetric Nash game, each “player” is responsible for one criterion, and the Nash equilibrium provides a solution to the multipoint optimization. Design results confirm the efficiency of the method.
基金The project supported by the National Natural Science Foundation of China (10372040)Scientific Research Foundation (SRF) for Returned Oversea's Chinese Scholars (ROCS) (2003-091). The English text was polished by Yunming Chen
文摘There are currently three different game strategies originated in economics： （1） Cooperative games （Pareto front）, （2） Competitive games （Nash game） and （3） Hierarchical games （Stackelberg game）. Each game achieves different equilibria with different performance, and their players play different roles in the games. Here, we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multi- criteria aerodynamic optimization problems. The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments. We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method. The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front. Non-dominated Pareto front solutions are obtained, however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.
基金supported by National Natural Science Foundations of China(nos.12271326,62102304,61806120,61502290,61672334,61673251)China Postdoctoral Science Foundation(no.2015M582606)+2 种基金Industrial Research Project of Science and Technology in Shaanxi Province(nos.2015GY016,2017JQ6063)Fundamental Research Fund for the Central Universities(no.GK202003071)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JM-354).
文摘The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.
基金supported by the Open Fund of Guangxi Key Laboratory of Building New Energy and Energy Conservation(Project Number:Guike Energy 17-J-21-3).
文摘With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.
基金supported by the National Natural Science Foundation of China(Project No.5217232152102391)+2 种基金Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.
基金supported by the Research Grants Council of the Hong Kong Special Administration Region under the Grant No.14201621。
文摘In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.The designed distributed controller consists of two parts:a dynamic average consensus part that asymptotically reproduces the unknown NE,and an adaptive reference-tracking module responsible for steering EL systems’positions to track a desired trajectory.The generalized Barbalat’s Lemma is used to overcome the discontinuity of the closed-loop system caused by the switching networks.The proposed algorithm is illustrated by a sensor network deployment problem.
基金sponsored by R&D Program of Beijing Municipal Education Commission(KM202410009013).
文摘In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.
文摘For the negative impact of large-scale electric vehicles (EVs) disorderly charging on the power grid, a multi-objective optimization strategy for coordinated charging and discharging of EVs based on Stackelberg game is proposed. As the leader, the grid company aims to stabilize load fluctuations and formulate a reasonable electricity price strategy to guide EVs to participate in vehicle-to-grid (V2G);As followers, EV users optimize their charging plans based on electricity price information with the objective of reducing costs and obtaining good comfort. This paper uses the MOPSO algorithm to solve the proposed multi-objective Stackelberg problem, and calculates the optimization results under various preferences, which proves the effectiveness of the proposed model and method.
文摘The Stackelberg prediction game(SPG)is a bilevel optimization frame-work for modeling strategic interactions between a learner and a follower.Existing meth-ods for solving this problem with general loss functions are computationally expensive and scarce.We propose a novel hyper-gradient type method with a warm-start strategy to address this challenge.Particularly,we first use a Taylor expansion-based approach to obtain a good initial point.Then we apply a hyper-gradient descent method with an ex-plicit approximate hyper-gradient.We establish the convergence results of our algorithm theoretically.Furthermore,when the follower employs the least squares loss function,our method is shown to reach an e-stationary point by solving quadratic subproblems.Numerical experiments show our algorithms are empirically orders of magnitude faster than the state-of-the-art.
基金funded by Shandong University of Technology Doctoral Program in Science and Technology,grant number 4041422007.
文摘The rapid advance of Connected-Automated Vehicles(CAVs)has led to the emergence of diverse delaysensitive and energy-constrained vehicular applications.Given the high dynamics of vehicular networks,unmanned aerial vehicles-assisted mobile edge computing(UAV-MEC)has gained attention in providing computing resources to vehicles and optimizing system costs.We model the computing offloading problem as a multi-objective optimization challenge aimed at minimizing both task processing delay and energy consumption.We propose a three-stage hybrid offloading scheme called Dynamic Vehicle Clustering Game-based Multi-objective Whale Optimization Algorithm(DVCG-MWOA)to address this problem.A novel dynamic clustering algorithm is designed based on vehiclemobility and task offloading efficiency requirements,where each UAV independently serves as the cluster head for a vehicle cluster and adjusts its position at the end of each timeslot in response to vehiclemovement.Within eachUAV-led cluster,cooperative game theory is applied to allocate computing resourceswhile respecting delay constraints,ensuring efficient resource utilization.To enhance offloading efficiency,we improve the multi-objective whale optimization algorithm(MOWOA),resulting in the MWOA.This enhanced algorithm determines the optimal allocation of pending tasks to different edge computing devices and the resource utilization ratio of each device,ultimately achieving a Pareto-optimal solution set for delay and energy consumption.Experimental results demonstrate that the proposed joint offloading scheme significantly reduces both delay and energy consumption compared to existing approaches,offering superior performance for vehicular networks.
文摘The two-player nonzero-sum linear-exponential-quadratic stochastic differential game is studied.The game takes into account the players'attitudes to risk.The nonlinear transformations and change of probability measure techniques are used to study the existence of both open-loop and closed-loop Nash equilibria for the game.Some examples are constructed to illustrate their differences.Furthermore,theoretical results are applied to solve the risk-sensitive portfolio game problem in the financial market and show the effects of risk attitudes and economic performance on equilibria.
文摘成果名称:Shapley's Conjecture on the Cores of Abstract Market Games主要作者:曹志刚,秦承忠,杨晓光奖项类别:著作论文奖获奖等级:二等奖获奖论文《Shapley's Conjecture on the Cores of Abstract Market Games》发表于博弈论领域顶级期刊《Games and Economic Behavior》2018年第2期。论文研究成果初步解决了诺贝尔经济学奖获得者罗伊德·沙普利(Lloyd S. Shapley)提出的抽象市场博弈核非空的猜想。
文摘This paper proposes a multi-objective optimization design method based on the coalition cooperative game theory where the three design goals have been seen as three game players. By calculating the affecting factors and fuzzy clustering, the design variables are divided into different strategic spaces which belong to each player, then it constructs a payoff function based on the coalition mechanism. Each game player takes its own revenue function as a target and obtains the best strategy versus other players. The best strategies of all players consist of the strategy permutation of a round game and it obtains the final game solutions through multi-round games according to the convergence criterion. A multi-objective optimization example of the luff mechanism of compensative sheave block shows the effectiveness of the coalition cooperative game method.
基金National Natural Science Foundation of China(62325304).
文摘This paper presents a comprehensive overview of distributed Nash equilibrium(NE)seeking algorithms in non-cooperative games for multiagent systems(MASs),with a distinct emphasis on the dynamic control perspective.It specifically focuses on the research addressing distributed NE seeking problems in which agents are governed by heterogeneous dynamics.The paper begins by introducing fundamental concepts of general non-cooperative games and the NE,along with definitions of specific game structures such as aggregative games and multi-cluster games.It then systematically reviews existing studies on distributed NE seeking for various classes of MASs from the viewpoint of agent dynamics,including first-order,second-order,high-order,linear,and Euler-Lagrange(EL)systems.Furthermore,the paper highlights practical applications of these theoretical advances in cooperative control scenarios involving autonomous systems with complex dynamics,such as autonomous surface vessels,autonomous aerial vehicles,and other autonomous vehicles.Finally,the paper outlines several promising directions for future research.
基金Supported by the Public Welfare Technology Application Research Project of Zhejiang Province(No.LTGY23F020001)the Provincial Construction Programme for First-Class Online and Offline Blended Courses(No.Z202Y22513)the Higher Education Teaching Reform Research Programme of Communication University of Zhejiang“Research on Contextualized Teaching Mode for the New Generation of Engineering Students Based on Convergence Media”。
文摘Background Autism spectrum disorder(ASD)is a pervasive developmental disorder characterized by difficulties in social communication and restricted,repetitive behaviors.Early intervention is essential to improve developmental outcomes in children with ASD.Serious games,which combine educational objectives with game based interactions,have shown potential as tools for early intervention in patients with ASD.However,in China,the development of serious games specifically designed for children with ASD remains in its infancy,with significant gaps in technical frameworks and effective data management methods.Method This paper proposes a framework aimed at facilitating the development of multimodal serious games designed for ASD interventions.We demonstrated the feasibility of the framework by developing and integrating several components,such as web applications,mobile games,and augmented reality games.These tools are interconnected to achieve data connectivity and management.Additionally,adaptive mechanics were employed within the framework to analyze real-time player data,which allowed the game difficulty to be dynamically adjusted and provide a personalized experience for each child.Results The framework successfully integrated various multimodal games,ensuring that real-time data management supported personalized game experiences.This approach ensured that the interventions remained appropriately challenging while still achievable.Conclusion The results indicate that the proposed framework enhances collaboration among therapists,parents,and developers while also improving the effectiveness of ASD interventions.By delivering personalized gameplay experiences that are both challenging and achievable,the framework offers a scalable platform for the future development of serious games.
