共查询到20条相似文献，搜索用时 921 毫秒
1.
《Journal of The Franklin Institute》2022,359(2):1483-1502
In this paper, the distributed optimization problem is investigated by employing a continuous-time multi-agent system. The objective of agents is to cooperatively minimize the sum of local objective functions subject to a convex set. Unlike most of the existing works on distributed convex optimization, here we consider the case where the objective function is pseudoconvex. In order to solve this problem, we propose a continuous-time distributed project gradient algorithm. When running the presented algorithm, each agent uses only its own objective function and its own state information and the relative state information between itself and its adjacent agents to update its state value. The communication topology is represented by a time-varying digraph. Under mild assumptions on the graph and the objective function, it shows that the multi-agent system asymptotically reaches consensus and the consensus state is the solution to the optimization problem. Finally, several simulations are carried out to verify the correctness of our theoretical achievements. 相似文献
2.
《Journal of The Franklin Institute》2022,359(8):3692-3716
This paper considers a nonsmooth constrained distributed convex optimization over multi-agent systems. Each agent in the multi-agent system only has access to the information of its objective function and constraint, and cooperatively minimizes the global objective function, which is composed of the sum of local objective functions. A novel continuous-time algorithm is proposed to solve the distributed optimization problem and effectively characterize the appropriate gain of the penalty function. It should be noted that the proposed algorithm is based on an adaptive strategy to avoid introducing the primal-dual variables and estimating the related exact penalty parameters. Additional, it is demonstrated that the state solution of the proposed algorithm achieves consensus and converges to an optimal solution of the optimization problem. Finally, numerical simulations are given and the proposed algorithm is applied to solve the optimal placement problem and energy consumption problem. 相似文献
3.
《Journal of The Franklin Institute》2019,356(17):10196-10215
This paper deals with the large category of convex optimization problems on the framework of second-order multi-agent systems, where each distinct agent is assigned with a local objective function, and the overall optimization problem is defined as minimizing the sum of all the local objective functions. To solve this problem, two distributed optimization algorithms are proposed, namely, a time-triggered algorithm and an event-triggered algorithm, to make all agents converge to the optimal solution of the optimization problem cooperatively. The main advantage of our algorithms is to remove unnecessary communications, and hence reduce communication costs and energy consumptions in real-time applications. Moreover, in the proposed algorithms, each agent uses only the position information from its neighbors. With the design of the Lyapunov function, the criteria about the controller parameters are derived to ensure the algorithms converge to the optimal solution. Finally, numerical examples are given to illustrate the effectiveness of the proposed algorithms. 相似文献
4.
《Journal of The Franklin Institute》2021,358(13):6741-6758
In this paper, a distributed time-varying convex optimization problem with inequality constraints is discussed based on neurodynamic system. The goal is to minimize the sum of agents’ local time-varying objective functions subject to some time-varying inequality constraints, each of which is known only to an individual agent. Here, the optimal solution is time-varying instead of constant. Under an undirected and connected graph, a distributed continuous-time consensus algorithm is designed by using neurodynamic system, signum functions and log-barrier penalty functions. The proposed algorithm can be understood through two parts: one part is used to reach consensus and the other is used to achieve gradient descent to track the optimal solution. Theoretical studies indicate that all agents will achieve consensus and the proposed algorithm can track the optimal solution of the time-varying convex problem. Two numerical examples are provided to validate the theoretical results. 相似文献
5.
《Journal of The Franklin Institute》2023,360(14):10706-10727
Distributed optimization over networked agents has emerged as an advanced paradigm to address large-scale control, optimization, and signal-processing problems. In the last few years, the distributed first-order gradient methods have witnessed significant progress and enrichment due to the simplicity of using only the first derivatives of local functions. An exact first-order algorithm is developed in this work for distributed optimization over general directed networks with only row-stochastic weighted matrices. It employs the rescaling gradient method to address unbalanced information diffusion among agents, where the weights on the received information can be arbitrarily assigned. Moreover, uncoordinated step-sizes are employed to magnify the autonomy of agents, and an error compensation term and a heavy-ball momentum are incorporated to accelerate convergency. A linear convergence rate is rigorously proven for strongly-convex objective functions with Lipschitz continuous gradients. Explicit upper bounds of step-size and momentum parameter are provided. Finally, simulations illustrate the performance of the proposed algorithm. 相似文献
6.
《Journal of The Franklin Institute》2023,360(13):9329-9354
In this paper, we study the problem of decentralized optimization to minimize a finite sum of local convex cost functions over an undirected network. Compared with the existing works, we focus on improving the communication efficiency of the stochastic gradient tracking method and propose an effective event-triggering decentralized stochastic gradient tracking algorithm, namely, ET-DSGT. ET-DSGT utilizes the event-triggering mechanism in which each agent only broadcasts its estimators at the event time to effectively avoid real-time communication, thus improving communication efficiency. In addition, we present a theoretical analysis to show that ET-DSGT with a decaying step-size can converge to the exact global minimum. Moreover, we show that for each agent, the time interval between two successive triggering times is greater than the iteration interval under certain conditions. Finally, we provide several simulations to demonstrate the effectiveness of ET-DSGT. 相似文献
7.
《Journal of The Franklin Institute》2022,359(14):7482-7509
This paper concentrates on a class of decentralized convex optimization problems subject to local feasible sets, equality and inequality constraints, where the global objective function consists of a sum of locally smooth convex functions and non-smooth regularization terms. To address this problem, a synchronous full-decentralized primal-dual proximal splitting algorithm (Syn-FdPdPs) is presented, which avoids the unapproximable property of the proximal operator with respect to inequality constraints via logarithmic barrier functions. Following the proposed decentralized protocol, each agent carries out local information exchange without any global coordination and weight balancing strategies introduced in most consensus algorithms. In addition, a randomized version of the proposed algorithm (Rand-FdPdPs) is conducted through subsets of activated agents, which further removes the global clock coordinator. Theoretically, with the help of asymmetric forward-backward-adjoint (AFBA) splitting technique, the convergence results of the proposed algorithms are provided under the same local step-size conditions. Finally, the effectiveness and practicability of the proposed algorithms are demonstrated by numerical simulations on the least-square and least absolute deviation problems. 相似文献
8.
This paper studies adaptive optimization problem of continuous-time multi-agent systems. Multi-agents with second-order dynamics are considered. Each agent is equipped with a time-varying cost function which is known only to an individual agent. The objective is to make multi-agents velocities minimize the sum of local functions by local interaction. First, a distributed adaptive algorithm is presented, in which each agent depends only on its own velocity and neighbors velocities. It is indicated that all agents can track the optimal velocity. Then we apply the distributed adaptive algorithm to flocking of multi-agents. It is proved that all agents can track the optimal trajectory. The agents will maintain connectivity and avoid the inter-agent collision. Finally, two simulations are included to illustrate the results. 相似文献
9.
In a multi-agent framework, distributed optimization problems are generally described as the minimization of a global objective function, where each agent can get information only from a neighborhood defined by a network topology. To solve the problem, this work presents an information-constrained strategy based on population dynamics, where payoff functions and tasks are assigned to each node in a connected graph. We prove that the so-called distributed replicator equation (DRE) converges to an optimal global outcome by means of the local-information exchange subject to the topological constraints of the graph. To show the application of the proposed strategy, we implement the DRE to solve an economic dispatch problem with distributed generation. We also present some simulation results to illustrate the theoretic optimality and stability of the equilibrium points and the effects of typical network topologies on the convergence rate of the algorithm. 相似文献
10.
给出一种结合梯度法和正交遗传算法的混合算法。实验表明,它通过对问题的解空间交替进行全局和局部搜索,能更有效地求解函数优化问题。 相似文献
11.
Tianrong Weng Lei Wang Zhikun She Quanyi Liang 《Journal of The Franklin Institute》2019,356(2):883-893
In this paper, a distributed projection algorithm based on the subgradient method is presented to solve the distributed optimization problem with a constrained set over a directed multi-agent network, where the designed protocol is scaled by the left eigenvector associated with the weighted adjacency matrix. By using the property of the projection operation and nonnegative almost supermartingales, we give the convergence analysis of our algorithm and show that the optimal solution is the ultimate consensus state of all agents to be reached. A numerical simulation for a specific optimization problem is given to verify the effectiveness of our algorithm. 相似文献
12.
Francesca Boem Lorenzo Sabattini Cristian Secchi 《Journal of The Franklin Institute》2019,356(2):860-882
The paper proposes a decentralized state estimation method for the control of network systems, where a cooperative objective has to be achieved. The nodes of the network are partitioned into independent nodes, providing the control inputs, and dependent nodes, controlled by local interaction laws. The proposed state estimation algorithm allows the independent nodes to estimate the state of the dependent nodes in a completely decentralized way. To do that, it is necessary for each independent node of the network to estimate the control input components computed by the other independent nodes, without requiring communication among the independent nodes. The decentralized state estimator, including an input estimator, is developed and the convergence properties are studied. Simulation results show the effectiveness of the proposed approach. 相似文献
13.
提出了一种新的基于差分进化和粗糙集理论的多目标寻优算法。应用差分进化作为的搜索引擎,尝试将它在单一目标优化中展现出的良好收敛作用转换到多目标优化问题中。在搜索的第二阶段中,为了提高迄今为止已有的非支配解决方案的普遍性,应用到了粗糙集理论。对于专用文献中通常采纳应用标准的测试函数和尺度的检验,本文的混合方法是有效的。 相似文献
14.
为了改善协同进化多目标优化算法性能,引入了聚集密度对超级个体集合进行更新。其基本思想是：首先计算种群中各个体的聚集密度,再定义一个偏序集,然后根据一定的比例依次从偏序集中选择个体更新。根据数值试验和量化指标测试了新算法的收敛性与分布性。结果表明,新算法在收敛性方面与常规协同进化多目标算法相当,但其分布性获得了一定程度的改善。 相似文献
15.
In this paper, we investigate the optimal local sensor decision rule based on non-ideal transmission channels between local sensors and the fusion center for distributed target detection system. The optimality of a likelihood-ratio test (LRT)-based local decision rule at local sensor, which requires only the knowledge of channel statistics instead of instantaneous channel state information (CSI), is established. The coupled local decision rule at each sensor is derived in a closed-form for coherent BPSK and OOK and non-coherent OOK. The iterative person-by-person optimization (PBPO) algorithm is employed to solve the coupled local thresholds. Simulation analysis reveals that the derived thresholds according to the local decision rule are consistent to the exhaustive searching. Furthermore, the detection performance of the system with the proposed optimal local decision rule for different reception modes and modulations is analyzed and compared. 相似文献
16.
In this paper, the event-triggered distributed H∞ state estimation problem is investigated for a class of state-saturated systems with randomly occurring mixed delays over sensor networks. The mixed delays, which comprise both discrete and distributed delays, are allowed to occur in a random manner governed by two mutually independent Bernoulli distributed random variables. In order to alleviate the communication burden, an event-triggered mechanism is utilized for each sensor node to decide whether or not its current information should be broadcasted to its neighbors. The aim of this paper is to design event-triggered state estimators such that the error dynamics of state estimation is exponentially mean-square stable with a prescribed H∞ performance index. By resorting to intensive stochastic analysis, sufficient conditions are first derived to guarantee the existence of the desired estimators, and the parameters of the desired distributed estimators are then obtained in light of the feasibility of a certain set of matrix inequalities. A numerical example is employed to illustrate the usefulness of the proposed distributed estimation algorithm. 相似文献
17.
Li Sheng Yichun Niu Lei Zou Yurong Liu Fuad E. Alsaadi 《Journal of The Franklin Institute》2018,355(15):7417-7442
In this paper, the problem of finite-horizon H∞ state estimation is investigated for a class of discrete time-varying complex networks with multiplicative noises and random coupling strengths. The network nodes and estimators are connected via a constrained communication network which allows only one node to send measurement data at each transmission instant. The Round-Robin protocol is introduced to determine which node obtains the access to the network at certain transmission instant. The aim of the addressed problem is to design a set of time-varying estimator parameters such that the prescribed H∞ performance is guaranteed over a finite horizon. By using the stochastic analysis approach and completing-the-square method, sufficient conditions are derived for the existence of the desired estimators in terms of the solution to backward recursive Riccati difference equations. Finally, a numerical example is provided to validate the feasibility and effectiveness of the proposed results. 相似文献
18.
传统的基于最小方差原理的反演结果依赖于初始模型选择,易陷入局部极小,针对以上问题,文章利用完全非线性反演方法-粒子群反演算法,对核磁共振探测地下水的数据资料进行反演解释,该算法具有操作简单,并行处理,不要求被优化的目标函数具有可微、可导、连续等性质的优点。将基本粒子群算法与模拟退火算法结合,加入非线性约束优化条件,使其适用于核磁共振探测地下水数据资料的反演解释。试验结果表明,混合粒子群反演算法反演结果精度较高,收敛速度较快,验证了粒子群优化算法在核磁共振反演应用中的可行性。 相似文献
19.
Application of non-dominated sorting genetic algorithm-II technique for optimal FACTS-based controller design 总被引：1，自引：0，他引：1
Sidhartha Panda 《Journal of The Franklin Institute》2010,347(7):1047-1064
Design of an optimal controller requires optimization of multiple performance measures that are often noncommensurable and competing with each other. Design of such a controller is indeed a multi-objective optimization problem. Non-dominated sorting in genetic algorithms-II (NSGA-II) is a popular non-domination based genetic algorithm for solving multi-objective optimization problems. This paper investigates the application of NSGA-II technique for the design of a flexible AC transmission system (FACTS)-based controller. The design objective is to improve the stability of the power system with minimum control effort. The proposed technique is applied to generate Pareto set of global optimal solutions to the given multi-objective optimization problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Further, a detailed analysis on the selection of control signals (both local and remote signals) on the effectiveness of the proposed controller is carried out and simulation results are presented under various loading conditions and disturbances to show the effectiveness and robustness of the proposed approach. 相似文献