A multi-start central force optimization for global optimization

Central force optimization (CFO) is an efficient and powerful population-based intelligence algorithm for optimization problems. CFO is deterministic in nature, unlike the most widely used metaheuristics. CFO, however, is not completely free from the problems of premature convergence. One way to overcome local optimality is to utilize the multi-start strategy. By combining the respective advantages of CFO and the multi-start strategy, a multi-start central force optimization (MCFO) algorithm is proposed in this paper. The performance of the MCFO approach is evaluated on a comprehensive set of benchmark functions. The experimental results demonstrate that MCFO not only saves the computational cost, but also performs better than some state-of-the-art CFO algorithms. MCFO is also compared with representative evolutionary algorithms. The results show that MCFO is highly competitive, achieving promising performance.     

Mapping streaming applications on multiprocessors with time-division-multiplexed network-on-chip

This paper addresses mapping of streaming applications (such as MPEG) on multiprocessor platforms with time-division-multiplexed network-on-chip. In particular, we solve processor selection, path selection and router configuration problems. Given the complexity of these problems, state of the art approaches in this area largely rely on greedy heuristics, which do not guarantee optimality. Our approach is based on a constraint programming formulation that merges a number of steps, usually tackled in sequence in classic approaches. Thus, our method has the potential of finding optimal solutions with respect to resource usage under throughput constraints. The experimental evaluation presented in here shows that our approach is capable of exploring a range of solutions while giving the designer the opportunity to emphasize the importance of various design metrics.     

空间光通信ATP系统粗跟踪平台的混合自适应控制

ATP系统的粗跟踪平台外框架对内框架的耦合作用以及内框架自身的不平衡所引起的加速度干扰是影响空间光通信系统光信号跟踪性能的重要因素。针对方位跟踪通道中的内框架系统结构,提出了应用一种混合自适应控制方法,该方法在设计自适应控制器时采用离散与连续相结合的方法,并将耦合作用和加速度干扰转换为对象的未建模动态进行处理。最终的仿真结果表明,被控对象在具有未建模动态和干扰的情况下,采用混合自适应控制算法能够获得较高的跟踪精度。     

A stick-slip piezoelectric actuator with an integrated sensing unit for the measurement and active control of the contact force

Output characteristics of the stick-slip piezoelectric actuators are intrinsically affected by the contact force between the driving foot and the mover. Therefore, it would be quite meaningful to measure and control the contact force. In this study, by integrating a group of strain gauges into the driving compliant mechanism, a stick-slip piezoelectric actuator with measured contact force was proposed. The main structural parameters of the driving compliant mechanism were designed and analyzed. Through experiments, the sensing unit was calibrated, and the results showed that it had good linearity, and the noise level was 3.5 mN. The contact force was measured by the sensing unit, and accordingly, the relationship between the contact force and the stepping characteristic was explored. It was shown that the change in contact force would lead to the evolution in stepping characteristic, which finally resulted in the motion nonlinearity of the actuator in large stroke output. Accordingly, by actively controlling the contact force, a method was proposed to tune the stepping characteristic. The comparative results demonstrated that this method could significantly improve the motion linearity of the actuator in large stroke output under various preloads and even very rough surface.     

考虑负荷提前通知时间的配电网两阶段云边协同调控方法

随着光伏等新能源并网规模不断增大,其波动性与不确定性给系统带来了巨大挑战,通过需求侧响应技术充分调动用户侧柔性资源可以提高配电网调控弹性和系统运行稳定性。针对激励型可控负荷提前通知时间的不同,将其划分为日前型与日内型,提出配电网两阶段云边协同调控方法。首先,基于云边协同调控架构,提出日前预测日内修正的云边协同调控策略。然后,在日前阶段,建立考虑日内资源预测的日前调控模型,基于日前调控方案,在日内阶段建立日内修正调控模型。最后,采用交替方向乘子法对所建模型进行求解。算例证明了上述方法在减小系统峰谷差、提高配电网调控弹性上的有效性,并验证了所提模型具有较好的经济性。     

Formation and obstacle avoidance control for multiagent systems

This paper considers the problems of formation and obstacle avoidance for multiagent systems.The objective is to design a term of agents that can reach a desired formation while avoiding collision with obstacles.To reduce the amount of information interaction between agents and target,we adopt the leader-follower formation strategy.By using the receding horizon control (RHC),an optimal problem is formulated in terms of cost minimization under constraints.Information on obstacles is incorporated online as sensed in a limited sensing range.The communication requirements between agents are that the followers should obtain the previous optimal control trajectory of the leader to each update time.The stability is guaranteed by adding a terminal-state penalty to the cost function and a terminal-state region to optimal problem.Finally,simulation studies are provided to verify the effectiveness of the proposed approach.     

Interference aware resource allocation for hybrid hierarchical wireless networks

This paper addresses the problem of interference aware resource allocation for OFDMA based hybrid hierarchical wireless networks. We develop two resource allocation algorithms considering the impact of wireless interference constraints using a weighted SINR conflict graph to quantify the interference among the various nodes: (1) interference aware routing using maximum concurrent flow optimization; and (2) rate adaptive joint subcarrier and power allocation algorithm under interference and QoS constraints. We exploit spatial reuse to allocate subcarriers in the network and show that an intelligent reuse of resources can improve throughput while mitigating interference. We provide a sub-optimal heuristic to solve the rate adaptive resource allocation problem. We demonstrate that aggressive spatial reuse and fine tuned-interference modeling garner advantages in terms of throughput, end-to-end delay and power distribution.     

A power- and coverage-aware clustering scheme for wireless sensor networks

A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many upper layer network functions. This study concentrates on how to form clusters with high uniformity while prolonging the network lifetime. A novel clustering scheme named power- and coverage- aware clustering (PCC) is proposed, which can adaptively select cluster heads according to a hybrid of the nodes' residual energy and loyalty degree. Additionally, the PCC scheme is independent of node distribution or density, and it is free of node hardware limitations, such as self-locating capability and time synchronization. Experiment results show that the scheme performs well in terms of cluster size (and its standard deviation), number of nodes alive over time, total energy consumption, etc.