蚁群元胞优化算法在人群疏散路径规划中的应用

针对疏散路径规划问题,以栅格化地图为背景的基础上,提出了蚁群元胞优化算法.首先为统一仿真时间步长,建立以六边形元胞为基础的栅格地图;然后利用静态势场对启发函数进行优化,利用分段更新规则优化信息素更新方式;最后,将模型参数作为粒子群优化算法的粒子位置信息进行优化,求解参数的最优组合值.仿真结果表明:采用蚁群元胞优化模型进行疏散路径规划时,不仅加快了搜索速度,而且增大了解空间,提高了搜索能力,可以有效避免陷入局部最优解.     

含时量子蒙特卡罗方法研究两电子原子在强激光作用下电子的动力学行为

通过改进含时量子蒙特卡罗方法研究了一维模型双电子原 子在强激光作用下的电子动力学过程. 与准确的数值积分求解含时薛定谔方程相比, 计算得到的波包对应的量子系综中粒子的动力学变化和含时波包演化结果定性一致, 且大幅度地提高了计算效率. 根据计算得到的经典粒子系综的动力学演化行为, 分析了原子在强激光作用下的激发、电离、重散射等非线性过程.     

基于量子并行粒子群优化算法的分数阶混沌系统参数估计

分数阶混沌系统参数估计的本质是多维参数优化问题, 其对于实现分数阶混沌控制与同步至关重要. 提出一种基于量子并行特性的粒子群优化新算法, 用于解决分数阶混沌的系统参数估计问题. 利用量子计算的并行特性, 设计出了一种新的量子编码, 使每代运算的可计算次数呈指数增加. 在此基础上, 构建了由量子当前旋转角、个体最优旋转角和全局最优旋转角共同组成的粒子演化方程, 以约束粒子在量子空间中的运动行为, 使算法的搜索能力得到了较大提高. 以分数阶Lorenz混沌系统和分数阶Chen混沌系统的参数估计为例, 进行了未知参数估计的数值仿真, 结果显示本算法具有良好的有效性、鲁棒性和通用性.     

士兵可穿戴下肢外骨骼机器人多元感知方法研究

下肢外骨骼机器人是一种可穿戴且融合了多种机器人技术的复杂人-机系统。它将人类的智慧与机器人强壮的能力有效地结合起来,最大限度地提高人体的机动力和耐力,这为提升单兵作战系统的能力创造了条件。鉴于下肢外骨骼机器人在作战、后勤保障时可能遇到的复杂地形、多变随机的任务等,仅通过基于既定的典型步态规划程序驱动执行已知的特定动作,难以保证人机间的耦合性和动作的高随意性切换。为此,模拟并提炼出士兵常见的六种下肢动作作为后续研究,然后分析了下肢外骨骼机器人的感知控制原理,并提出了基于脑电预判感知、肌电精确感知和光纤实时校正的多信息融合的感知方法,强调将人的智能参与到机器人控制中,以期推进士兵可穿戴下肢外骨骼机器人的实用化。     

基于粒子群并行优化的煤矿井下机器人路径规划

路径规划是煤矿井下搜救探测机器人自主导航的关键步骤,矿井是三维的非机构化的环境,机器人行走过程应该具有高度智能的路径规划,传统的自适应能力与处理非线性的问题能力较差,路径规划误差较大,提出基于粒子群并行优化的煤矿井下机器人路径规划方法,充分考虑井下的环境高低变化,采用栅格法对环境建模,将粒子群独立分布在不同容器中分别进行路径建模,不同容器中粒子分别进行优化操作;因为速度和最优子群被分别保留,在机器人路径规划实验阶段,路径规划的时间较传统方法降低20%,避障成功率高达95%,最优路径的出现概率能保持在99%,这种方法具有很强的指导性与实用价值。     

基于PSO的工业机器人时间-脉动最优轨迹规划

为提高工业机器人的工作效率,并且保持机器人关节平稳运动,提出一种基于粒子群优化算法的时间-脉动最优轨迹规划方案。通过权重法将多目标优化转化为单目标优化,再运用粒子群优化算法得到时间-脉动最优的运动轨迹。轨迹规划中,采用了关节空间五次非均匀B样条插值法,以确保脉动曲线的连续性。最后以GRB4016工业机器人为研究对象进行仿真实验,结果表明,该方案可以得到较理想的运动轨迹,并验证了方案的有效性。     

一种采用种群多样性监控和实时更新策略的粒子群优化算法

针对粒子群算法优化后期容易出现早熟收敛问题,建立一种具有种群多样性监测和实时更新策略的改进方法.首先建立种群健康度指标用来评价粒子群进化状态;其次提出随机扰动策略和离心搜索策略用于丰富粒子群的种群多样性,增强算法的全局搜索能力,并提出梯度搜索策略用于精确、高效地搜寻当前邻域内的局部极值点,提高算法的计算效率.最后建立种群健康度反馈机制,使粒子可以实时感知种群的健康程度,并自适应地采用不同的粒子更新策略,保证粒子群处于健康进化水平.将新方法应用于优化实例,并与其它改进方法进行性能比较,结果验证了新方法的有效性.     

两模微腔中四能级原子粒子布居数随时间的演化

在相互作用绘景中利用微扰法求解了两模微腔的腔模与注入其内的四能级原子^40Ca构成的相互作用系统的态函数随时间的演化关系,在此基础上导出了原子的约化密度算符及其粒子布居数随时间的演化关系,由此可进一步研究四能级原子在腔场中的动力学行为。     

Melting Behaviour of Shell-symmetric Aluminum Nanoparticles: Molecular Dynamics Simulation

用基于镶嵌原子方法势能的分子动力学模拟研究了含有561个原子的铝纳米粒子. 利用总势能和比热来计算铝纳米粒子的熔点：二十面体、十面体、切去顶端的八面体铝纳米粒子的熔点分别是540±10、500±10和520±10 K. 均方位移、键参数和回转半径的变化趋势与势能和比热的变化一致. 通过拟合均方位移得到了Kohlraush-William-Watts弛豫法则中的弛豫时间和伸缩参数,计算表明在高温区域弛豫时间和温度之间遵循标准阿伦尼乌斯关系.     

基于旁瓣级最小化的平面螺旋阵列设计的粒子群优化方法

基于粒子群优化算法,以最大旁瓣级最小化为代价函数,在阿基米德螺旋线上搜索最优平面螺旋阵列。数值仿真计算利用粒子群优化算法搜索得到旁瓣级最低的最优阵列,与其他阵型比较,并在半消声室对各种声源进行声场测量和声源定位实验,验证了粒子群优化方法进行阵型优化的有效性。     

The evolution of cooperation within the multigame environment based on the Particle Swarm Optimization algorithm

This paper studies the evolution of cooperation in a so-called multigame environment based on the Particle Swarm Optimization (PSO) algorithm. In a multigame environment, players use different game payoff matrices and acquire their utilities from their interactions with their neighbors. According to the PSO algorithm, each player updates its strategy according to both the strategy adopted by the player with the highest utility in its neighborhood and the most profitable strategy in their own past actions. Simulation results show that the multigame environment is conducive to the promotion of cooperation. Besides, within the multigame environment, for any player, imitating the most profitable strategy in its past actions promotes cooperation more effectively than imitating the strategy adopted by the player with the highest utility in its neighborhood.     

Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment

An Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in the agricultural plant protection such as watering, sowing, and pesticide spraying. It is essential to develop a Decision-making Support System (DSS) for UAVs to help them choose the correct action in states according to the policy. In an unknown environment, the method of formulating rules for UAVs to help them choose actions is not applicable, and it is a feasible solution to obtain the optimal policy through reinforcement learning. However, experiments show that the existing reinforcement learning algorithms cannot get the optimal policy for a UAV in the agricultural plant protection environment. In this work we propose an improved Q-learning algorithm based on similar state matching, and we prove theoretically that there has a greater probability for UAV choosing the optimal action according to the policy learned by the algorithm we proposed than the classic Q-learning algorithm in the agricultural plant protection environment. This proposed algorithm is implemented and tested on datasets that are evenly distributed based on real UAV parameters and real farm information. The performance evaluation of the algorithm is discussed in detail. Experimental results show that the algorithm we proposed can efficiently learn the optimal policy for UAVs in the agricultural plant protection environment.     

无人机多阶段航迹预测协同任务规划

针对多无人机(Unmanned Aerial Vehicles, UAVs)协同控制问题,提出了一种UAVs多阶段航迹预测分布式任务规划方法。定义从一次任务分配开始到其中一项任务完成为一个任务周期。在每个规划周期,首先,各UAV使用A*算法快速预测到所有任务目标的路径,提供至任务分配;然后,采用聚类算法修改目标价值向量,协商分配结果,并实时计算探测范围内的最短路径;最后,采用三次B样条曲线平滑所分配的最短路径,在线规划出满足飞行约束的飞行航迹。通过仿真实验对算法的有效性进行了验证,结果表明,提出的算法能够实时获得近似最优的任务分配结果并规划出可飞行航迹,并有效处理突发任务。     

Dynamics of moving agents with memory

The problem of emergence of collective behavior in a system of two dimensional interacting moving agents is considered. The individual agent architecture is hybrid in the sense that the response is reactive but the agent is capable to choose between two different rules in a probabilistic way: one rule depends on the information from the environment and the other depends on the information imprinted in the agents memory. The dynamics of the system is studied through the characterization of the equilibrium state and the non-equilibrium properties of the system.     

Transfer effects on communication and collision avoidance behavior from playing a three-dimensional auditory game based on a virtual auditory display

Transfer effects on two everyday auditory skills from playing an auditory game using a virtual auditory display (VAD) were investigated using two groups of 20 normally sighted participants. Representative auditory skills of blindfolded participants were pre-tested using communication and collision avoidance tasks. They performed the tasks two weeks later. Participants in a training group played the VAD-based game on seven days (30 min/day) during the two weeks: control group participants did not. Playing the VAD-based game significantly increased face-contacts in the communication task and significantly improved participants’ collision avoidance. No significant differences were found between the groups’ subjective ratings of the rated tension levels during the communication task or between rated levels for the collision threat. Performance revealed transfer effects of playing the VAD-based game on communication behaviors in social interaction and on avoidance of approaching objects: VAD-based games are effective training tools for auditory skills used in daily life.     

Evolutionary ultimatum game on complex networks under incomplete information

This paper studies the evolutionary ultimatum game on networks when agents have incomplete information about the strategies of their neighborhood agents. Our model assumes that agents may initially display low fairness behavior, and therefore, may have to learn and develop their own strategies in this unknown environment. The Genetic Algorithm Learning Classifier System (GALCS) is used in the model as the agent strategy learning rule. Aside from the Watts-Strogatz (WS) small-world network and its variations, the present paper also extends the spatial ultimatum game to the Barabási-Albert (BA) scale-free network. Simulation results show that the fairness level achieved is lower than in situations where agents have complete information about other agents’ strategies. The research results display that fairness behavior will always emerge regardless of the distribution of the initial strategies. If the strategies are randomly distributed on the network, then the long-term agent fairness levels achieved are very close given unchanged learning parameters. Neighborhood size also has little effect on the fairness level attained. The simulation results also imply that WS small-world and BA scale-free networks have different effects on the spatial ultimatum game. In ultimatum game on networks with incomplete information, the WS small-world network and its variations favor the emergence of fairness behavior slightly more than the BA network where agents are heterogeneously structured.     

基于代价优化的云工作流调度改进PSO算法

云计算可以通过即付即用的方式向用户工作流提供资源。为了解决资源服务代价异构环境下的云工作流任务调度代价问题,提出一种基于改进粒子群算法的云工作流任务调度算法WSA-IPSO。通过综合考虑任务的执行代价和依赖任务间发生数据传输时的通信代价,算法将总代价优化问题形式化为有向无环图DAG中的任务调度模型,并提出基于改进粒子群算法的优化模型对其进行求解。通过改进传统粒子群算法的粒子速度更新策略和惯性权重更新策略,算法可以以更快的收敛速度得到代价最小化的调度方案。通过仿真实验,与MCT算法及标准粒子群算法进行性能比较。实验结果表明,WSA-IPSO算法在降低总代价、任务分布的负载均衡以及算法收敛性方面比较同类算法均表现出更好的性能。     

Network evolution based on minority game with herding behavior

The minority game (MG) is used as a source of information to design complex networks where the nodes represent the playing agents. Differently from classical MG consisting of independent agents, the current model rules that connections between nodes are dynamically inserted or removed from the network according to the most recent game outputs. This way, preferential attachment based on the concept of social distance is controlled by the agents wealth. The time evolution of the network topology, quantitatively measured by usual parameters, is characterized by a transient phase followed by a steady state, where the network properties remain constant. Changes in the local landscapes around individual nodes depend on the parameters used to control network links. If agents are allowed to access the strategies of their network neighbors, a feedback effect on the network structure and game outputs is observed. Such effect, known as herding behavior, considerably changes the dependence of volatility σ on memory size: it is shown that the absolute value of σ as well as the corresponding value of memory size depend both on the network topology and on the way along which the agents make their playing decisions in each game round.     

Implications of human performance and perception under tonal noise conditions on indoor noise criteria

This research investigated differences in task performance and perception under six non-time-varying ventilation-type background noise spectra with differing tonality. The results were related to five indoor noise criteria systems: noise criteria, balanced noise criteria, room criteria, room criteria mark II, and the A-weighted equivalent sound pressure level (L(Aeq)). These criteria systems are commonly used in the U.S. building industry, but concerns exist over whether they are appropriate for all noise situations. Thirty test subjects completed three types of performance tasks (typing, reasoning, and math) and answered questions about their perception of the indoor environment under each noise condition. Results showed that performance scores did not change significantly across the six noise conditions, but there were differences in subjective perception. For example, perception trends for tonality, annoyance, and distraction changed based on the frequency and prominence of discrete tones in noise. However, these perceptual changes were not fully reflected in the criteria level or spectral quality ratings. Additionally, task performance was related to subjective perception but not to criteria level predictions. As a result, the authors suggest that the current criteria should be modified to account for the frequency and prominence of tones in background noise.     

Cooperative behavior in evolutionary snowdrift game with bounded rationality

An evolutionary snowdrift game (SG) that incorporates bounded rationality and limited information in the evolutionary process is proposed and studied. Based on SG in a well-mixed population and defining the winning action at a turn to be the one that gets a higher payoff, the most recent m winning actions can be used as a public information based on which the competing agents decide their next actions. This defines a strategy pool from which each agent picks a number of strategies as their tool in adapting to the competing environment. The payoff parameter r in SG serves to set the maximum number of winners per turn. Due to the bounded rationality and limited information, the cooperative frequency shows steps and plateaux as a function of r and these features tend to be smoothed out as m increases. These features are results of an interplay between a restricted subset of m-bit histories that the system can visit at a value of r and the limited capacity that agents can adapt. The standard deviation in the number of agents taking the cooperative action is also studied. For general values of r, our model generates a realization of the binary-agent-resource model. The idea of introducing bounded rationality into a two-person game to realize the minority game or binary-agent-resource model could be a useful tool for future research.