The application of multi-material topology optimization affords greater design flexibility compared to traditional single-material methods.However,density-based topology optimization methods encounter three unique cha...The application of multi-material topology optimization affords greater design flexibility compared to traditional single-material methods.However,density-based topology optimization methods encounter three unique challenges when inertial loads become dominant:non-monotonous behavior of the objective function,possible unconstrained characterization of the optimal solution,and parasitic effects.Herein,an improved Guide-Weight approach is introduced,which effectively addresses the structural topology optimization problem when subjected to inertial loads.Smooth and fast convergence of the compliance is achieved by the approach,while also maintaining the effectiveness of the volume constraints.The rational approximation of material properties model and smooth design are utilized to guarantee clear boundaries of the final structure,facilitating its seamless integration into manufacturing processes.The framework provided by the alternating active-phase algorithm is employed to decompose the multi-material topological problem under inertial loading into a set of sub-problems.The optimization of multi-material under inertial loads is accomplished through the effective resolution of these sub-problems using the improved Guide-Weight method.The effectiveness of the proposed approach is demonstrated through numerical examples involving two-phase and multi-phase materials.展开更多
The quantum alternating operator ansatz algorithm(QAOA+)is widely used for constrained combinatorial optimization problems(CCOPs)due to its ability to construct feasible solution spaces.In this paper,we propose a prog...The quantum alternating operator ansatz algorithm(QAOA+)is widely used for constrained combinatorial optimization problems(CCOPs)due to its ability to construct feasible solution spaces.In this paper,we propose a progressive quantum algorithm(PQA)to reduce qubit requirements for QAOA+in solving the maximum independent set(MIS)problem.PQA iteratively constructs a subgraph likely to include the MIS solution of the original graph and solves the problem on it to approximate the global solution.Specifically,PQA starts with a small-scale subgraph and progressively expands its graph size utilizing heuristic expansion strategies.After each expansion,PQA solves the MIS problem on the newly generated subgraph using QAOA+.In each run,PQA repeats the expansion and solving process until a predefined stopping condition is reached.Simulation results show that PQA achieves an approximation ratio of 0.95 using only 5.57%(2.17%)of the qubits and 17.59%(6.43%)of the runtime compared with directly solving the original problem with QAOA+on Erd?s-Rényi(3-regular)graphs,highlighting the efficiency and scalability of PQA.展开更多
In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algor...In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algorithm does not need a central node. Therefore, it has the characteristics of low communication burden and high privacy. In addition, numerical experiments are provided to validate the effectiveness of the proposed algorithm.展开更多
In this paper,we consider the NP-hard problem of finding the minimum dominant resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of dista...In this paper,we consider the NP-hard problem of finding the minimum dominant resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B.A resolving set is dominating if every vertex of G that does not belong to B is a neighbor to some vertices in B.The dominant metric dimension of G is the cardinality number of the minimum dominant resolving set.The dominant metric dimension is computed by a binary version of the Archimedes optimization algorithm(BAOA).The objects of BAOA are binary encoded and used to represent which one of the vertices of the graph belongs to the dominant resolving set.The feasibility is enforced by repairing objects such that an additional vertex generated from vertices of G is added to B and this repairing process is iterated until B becomes the dominant resolving set.This is the first attempt to determine the dominant metric dimension problem heuristically.The proposed BAOA is compared to binary whale optimization(BWOA)and binary particle optimization(BPSO)algorithms.Computational results confirm the superiority of the BAOA for computing the dominant metric dimension.展开更多
When deploying Reconfigurable Intelligent Surface(RIS)to improve System Sum-Rate(SSR),the timeliness and accuracy of SSR optimization methods are difficult to achieve simultaneously through a single algorithm.Some alg...When deploying Reconfigurable Intelligent Surface(RIS)to improve System Sum-Rate(SSR),the timeliness and accuracy of SSR optimization methods are difficult to achieve simultaneously through a single algorithm.Some algorithms focus on timeliness,while some focus on accuracy.In this paper,in order to take into account the timeliness and accuracy of the system comprehensively,we construct SSR analysis model of RIS-assisted multiuser downlink communication system and propose several new optimization methods.The goal is to maximize SSR by using the proposed algorithms to jointly optimize power allocation and reflection coefficients.To solve this comprehensive problem,two sets of Alternating Optimization(AO)-based timeliness algorithms and one set of Monotonic Optimization(MO)-based accuracy algorithms are proposed separately to jointly optimize system performance.First,the Water-Filling(WF)-based and penalty-based low complexity algorithms are developed to optimize power allocation and reflection coefficients respectively.To improve the reality of the calculation,penalty-based algorithm cleverly considers residual noise that is difficult to calculate.Then,for further improve the timeliness,a new Successive Convex Approximation(SCA)-based low complexity algorithm is designed to further optimize reflection coefficients and its convergence is proved.Third,in order to verify the effectiveness of the proposed timeliness algorithms,we further propose MO-based accuracy algorithms,in which,the Polyblock Outer Approximation(POA)algorithm,the Semidefinite Relaxation(SDR)method,and the bisection search algorithm are combined in a novel way.Numerical results confirm the timeliness of AO-based algorithms and the accuracy of MO-based algorithms.They supervise and complement each other.展开更多
Dykstra’s alternating projection algorithm was proposed to treat the problem of finding the projection of a given point onto the intersection of some closed convex sets. In this paper, we first apply Dykstra’s alter...Dykstra’s alternating projection algorithm was proposed to treat the problem of finding the projection of a given point onto the intersection of some closed convex sets. In this paper, we first apply Dykstra’s alternating projection algorithm to compute the optimal approximate symmetric positive semidefinite solution of the matrix equations AXB = E, CXD = F. If we choose the initial iterative matrix X<sub>0</sub> = 0, the least Frobenius norm symmetric positive semidefinite solution of these matrix equations is obtained. A numerical example shows that the new algorithm is feasible and effective.展开更多
新能源随机性使得电力系统潮流复杂多变,加之大量新能源需要远距离输送消纳,输电阻塞问题日益严重。动态热定值(dynamic line rating,DTR)技术能够提升既有架空线路的输电能力,充分发挥系统的灵活调节能力。特别是在N-1事故场景下,采用...新能源随机性使得电力系统潮流复杂多变,加之大量新能源需要远距离输送消纳,输电阻塞问题日益严重。动态热定值(dynamic line rating,DTR)技术能够提升既有架空线路的输电能力,充分发挥系统的灵活调节能力。特别是在N-1事故场景下,采用DTR技术提升线路输送能力,能够缓解严重输电阻塞。然而,传统方法在考虑N-1事故时存在维数灾难问题,因此应用DTR技术仍然存在挑战性。为此,提出了一种两阶段分布鲁棒优化(distributionally robust optimization,DRO)方法以提升架空线路的输电能力。首先,构建了架空线路暂态温度计算模型并做适当简化处理,从而保证后续优化模型的凸性。随后,建立了考虑DTR和N-1安全准则的两阶段DRO模型以避免N-1事故下的持续停电,考虑无功与网损的线性化交流潮流模型能够更准确地计算线路潮流。最后,使用IEEE-24节点系统和IEEE-118节点系统验证了所提方法的有效性。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52172356)the Hunan Provincial Natural Science Foundation of China(Grant No.2022JJ10012).
文摘The application of multi-material topology optimization affords greater design flexibility compared to traditional single-material methods.However,density-based topology optimization methods encounter three unique challenges when inertial loads become dominant:non-monotonous behavior of the objective function,possible unconstrained characterization of the optimal solution,and parasitic effects.Herein,an improved Guide-Weight approach is introduced,which effectively addresses the structural topology optimization problem when subjected to inertial loads.Smooth and fast convergence of the compliance is achieved by the approach,while also maintaining the effectiveness of the volume constraints.The rational approximation of material properties model and smooth design are utilized to guarantee clear boundaries of the final structure,facilitating its seamless integration into manufacturing processes.The framework provided by the alternating active-phase algorithm is employed to decompose the multi-material topological problem under inertial loading into a set of sub-problems.The optimization of multi-material under inertial loads is accomplished through the effective resolution of these sub-problems using the improved Guide-Weight method.The effectiveness of the proposed approach is demonstrated through numerical examples involving two-phase and multi-phase materials.
基金supported by the National Natural Science Foundation of China(Grant Nos.62371069,62372048,and 62272056)BUPT Excellent Ph.D.Students Foundation(Grant No.CX2023123)。
文摘The quantum alternating operator ansatz algorithm(QAOA+)is widely used for constrained combinatorial optimization problems(CCOPs)due to its ability to construct feasible solution spaces.In this paper,we propose a progressive quantum algorithm(PQA)to reduce qubit requirements for QAOA+in solving the maximum independent set(MIS)problem.PQA iteratively constructs a subgraph likely to include the MIS solution of the original graph and solves the problem on it to approximate the global solution.Specifically,PQA starts with a small-scale subgraph and progressively expands its graph size utilizing heuristic expansion strategies.After each expansion,PQA solves the MIS problem on the newly generated subgraph using QAOA+.In each run,PQA repeats the expansion and solving process until a predefined stopping condition is reached.Simulation results show that PQA achieves an approximation ratio of 0.95 using only 5.57%(2.17%)of the qubits and 17.59%(6.43%)of the runtime compared with directly solving the original problem with QAOA+on Erd?s-Rényi(3-regular)graphs,highlighting the efficiency and scalability of PQA.
文摘In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algorithm does not need a central node. Therefore, it has the characteristics of low communication burden and high privacy. In addition, numerical experiments are provided to validate the effectiveness of the proposed algorithm.
文摘In this paper,we consider the NP-hard problem of finding the minimum dominant resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B.A resolving set is dominating if every vertex of G that does not belong to B is a neighbor to some vertices in B.The dominant metric dimension of G is the cardinality number of the minimum dominant resolving set.The dominant metric dimension is computed by a binary version of the Archimedes optimization algorithm(BAOA).The objects of BAOA are binary encoded and used to represent which one of the vertices of the graph belongs to the dominant resolving set.The feasibility is enforced by repairing objects such that an additional vertex generated from vertices of G is added to B and this repairing process is iterated until B becomes the dominant resolving set.This is the first attempt to determine the dominant metric dimension problem heuristically.The proposed BAOA is compared to binary whale optimization(BWOA)and binary particle optimization(BPSO)algorithms.Computational results confirm the superiority of the BAOA for computing the dominant metric dimension.
基金supported in part by Natural Science Foundation of China(92367102)in part by National Science and Technology Major Project(2024ZD1300400).
文摘When deploying Reconfigurable Intelligent Surface(RIS)to improve System Sum-Rate(SSR),the timeliness and accuracy of SSR optimization methods are difficult to achieve simultaneously through a single algorithm.Some algorithms focus on timeliness,while some focus on accuracy.In this paper,in order to take into account the timeliness and accuracy of the system comprehensively,we construct SSR analysis model of RIS-assisted multiuser downlink communication system and propose several new optimization methods.The goal is to maximize SSR by using the proposed algorithms to jointly optimize power allocation and reflection coefficients.To solve this comprehensive problem,two sets of Alternating Optimization(AO)-based timeliness algorithms and one set of Monotonic Optimization(MO)-based accuracy algorithms are proposed separately to jointly optimize system performance.First,the Water-Filling(WF)-based and penalty-based low complexity algorithms are developed to optimize power allocation and reflection coefficients respectively.To improve the reality of the calculation,penalty-based algorithm cleverly considers residual noise that is difficult to calculate.Then,for further improve the timeliness,a new Successive Convex Approximation(SCA)-based low complexity algorithm is designed to further optimize reflection coefficients and its convergence is proved.Third,in order to verify the effectiveness of the proposed timeliness algorithms,we further propose MO-based accuracy algorithms,in which,the Polyblock Outer Approximation(POA)algorithm,the Semidefinite Relaxation(SDR)method,and the bisection search algorithm are combined in a novel way.Numerical results confirm the timeliness of AO-based algorithms and the accuracy of MO-based algorithms.They supervise and complement each other.
文摘Dykstra’s alternating projection algorithm was proposed to treat the problem of finding the projection of a given point onto the intersection of some closed convex sets. In this paper, we first apply Dykstra’s alternating projection algorithm to compute the optimal approximate symmetric positive semidefinite solution of the matrix equations AXB = E, CXD = F. If we choose the initial iterative matrix X<sub>0</sub> = 0, the least Frobenius norm symmetric positive semidefinite solution of these matrix equations is obtained. A numerical example shows that the new algorithm is feasible and effective.
文摘新能源随机性使得电力系统潮流复杂多变,加之大量新能源需要远距离输送消纳,输电阻塞问题日益严重。动态热定值(dynamic line rating,DTR)技术能够提升既有架空线路的输电能力,充分发挥系统的灵活调节能力。特别是在N-1事故场景下,采用DTR技术提升线路输送能力,能够缓解严重输电阻塞。然而,传统方法在考虑N-1事故时存在维数灾难问题,因此应用DTR技术仍然存在挑战性。为此,提出了一种两阶段分布鲁棒优化(distributionally robust optimization,DRO)方法以提升架空线路的输电能力。首先,构建了架空线路暂态温度计算模型并做适当简化处理,从而保证后续优化模型的凸性。随后,建立了考虑DTR和N-1安全准则的两阶段DRO模型以避免N-1事故下的持续停电,考虑无功与网损的线性化交流潮流模型能够更准确地计算线路潮流。最后,使用IEEE-24节点系统和IEEE-118节点系统验证了所提方法的有效性。
