To improve the energy efficiency and load-balance in large-scale multi-agent systems, a large-scale distributed cluster algorithm is proposed. At first, a parameter describing the spatial distribution of agents is designed to assess the information spreading capability of an agent. Besides, a competition resolution mechanism is proposed to tackle the competition problem in large-scale multiagent systems. Thus, the proposed algorithm can balance the load, adjust the system network locally and dynamically, reduce system energy consumption. Finally, simulations are presented to demonstrate the superiority of the proposed algorithm.
Given m facilities each with an opening cost, n demands, and distance between every demand and facility, the Facility Location problem finds a solution which opens some facilities to connect every demand to an opened facility such that the total cost of the solution is minimized. The κ-Facility Location problem further requires that the number of opened facilities is at most κ, where κ is a parameter given in the instance of the problem. We consider the Facility Location problems satisfying that for every demand the ratio of the longest distance to facilities and the shortest distance to facilities is at most ω, where ω is a predefined constant. Using the local search approach with scaling technique and error control technique, for any arbitrarily small constant ε 〉 0, we give a polynomial-time approximation algorithm for the ω-constrained Facility Location problem with approximation ratio 1 ＋ √ω ＋ ε, which significantly improves the previous best known ratio （ω ＋ 1）/α for some 1 ≤ α ≤2, and a polynomial-time approximation algorithm for the ω-constrained κ- Facility Location problem with approximation ratio ω ＋ 1 ＋ ε. On the aspect of approximation hardness, we prove that unless NP C DTIME（n^O（log log n））, the ω-constrained Facility Location problem cannot be approximated within 1 ＋ln √ω 1, which slightly improves the previous best known hardness result 1.243 ＋ 0.316 ln（ω - 1）. The experimental results on the standard test instances of Facility Location problem show that our algorithm also has good performance in practice.
One qubit subjected to the effect of phase damping in a two-level quantum system with arbitrary pure initial state is studied in this paper. The aim of this paper is to find the optimal control scheme to correct the qubit back as close as possible to its initial state. The strength-dependent measurements and control correction rotation in different bases are designed to protect the arbitrary pure state of qubit. The authors design the optimal weak measurement strength to achieve the best trade-off between gaining the information of the system and the disturbance through measurement.The authors study the suppression of phase damping in two cases: There is and isn’t the y component in initial state. The authors deduce the optimal parameters and performances of the control schemes for the various initial state situations. Simulation results demonstrate the effectiveness of the proposed control schemes.
This paper addresses a channel scheduling problem for group of dynamically decoupled nonlinear subsystems with actuators connected through digital communication channels and controlled by a centralized controller. Due to the limited communication capacity, only one channel can be activated and hence there is only one pair of sensor and actuator can communicate with the controller at each time instant. In addition, the communication channels are not reliable so Markovian packed dropout is introduced. A predictive control framework is adopted for controller/scheduler co-design to alleviate the performance loss caused by the limited communication capacity. Instead of sending a single control value, the controller sends a sequence of predicted control values to a selected actuator so that there are control input candidates which can be fed to the subsystem when the actuator does not communicate with the controller. A stochastic algorithm is proposed to schedule the usage of the communication medium and sufficient conditions on stochastic stability are given under some mild assumptions.
This paper studies a distributed robust resource allocation problem with nonsmooth objective functions under polyhedral uncertain allocation parameters. In the considered distributed robust resource allocation problem, the（nonsmooth） objective function is a sum of local convex objective functions assigned to agents in a multi-agent network. Each agent has a private feasible set and decides a local variable, and all the local variables are coupled with a global affine inequality constraint,which is subject to polyhedral uncertain parameters. With the duality theory of convex optimization,the authors derive a robust counterpart of the robust resource allocation problem. Based on the robust counterpart, the authors propose a novel distributed continuous-time algorithm, in which each agent only knows its local objective function, local uncertainty parameter, local constraint set, and its neighbors＇ information. Using the stability theory of differential inclusions, the authors show that the algorithm is able to find the optimal solution under some mild conditions. Finally, the authors give an example to illustrate the efficacy of the proposed algorithm.
This paper presents two aggregation strategies in convex intersection region for the distributed mobile sensor network(MSN) with heterogeneous dynamics. First, the authors analyze individual local perception model and dynamics model, set the intersection of all the local perceptions as the region of interest(ROI). The MSN consists of sensors with first-order dynamics and second-order dynamics. Then, the authors design a control strategy to ensure that individuals aggregate at a point in the ROI relying on their local perceptions and the locations of neighbors within their communication scope. The authors describe this situation of aggregation as rendezvous. In addition, the authors introduce artificial potential field to make sensors deploy dispersedly in a bounded range near the ROI,which the authors call dispersed deployment. Finally, the authors prove the stability of the proposed strategies and validate the theoretical results by simulations. This research is applied for the cooperative deployment and data collection of mobile platforms with different dynamics under the condition of inaccurate perception.
