Hybrid particle swarm optimization for multiobjective resource allocation

Resource allocation （RA） is the problem of allocating resources among various artifacts or business units to meet one or more expected goals, such a.s maximizing the profits, minimizing the costs, or achieving the best qualities. A complex multiobjective RA is addressed, and a multiobjective mathematical model is used to find solutions efficiently. Then, all improved particie swarm algorithm （mO_PSO） is proposed combined with a new particle diversity controller policies and dissipation operation. Meanwhile, a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented. The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm.     

Multi-objective fuzzy particle swarm optimization based on elite archiving and its convergence

A fuzzy particle swarm optimization （PSO） on the basis of elite archiving is proposed for solving multi-objective optimization problems. First, a new perturbation operator is designed, and the concepts of fuzzy global best and fuzzy personal best are given on basis of the new operator. After that, particle updating equations are revised on the basis of the two new concepts to discourage the premature convergence and enlarge the potential search space; second, the elite archiving technique is used during the process of evolution, namely, the elite particles are introduced into the swarm, whereas the inferior particles are deleted. Therefore, the quality of the swarm is ensured. Finally, the convergence of this swarm is proved. The experimental results show that the nondominated solutions found by the proposed algorithm are uniformly distributed and widely spread along the Pareto front.     

基于神经网络和PSO的机器人路径规划研究

提出一种神经网络和粒子群算法相结合的移动机器人路径规划方法。采用小波网络和RBF网络相结合的四层神经网络结构,克服了传统神经网络方法进行路径规划时对每个障碍均设计一些特定的隐节点,当障碍较多且环境动态时,网络结构庞大且神经元的阈值随时间的变化而需要不断改变的缺点。利用粒子群对神经网络的参数进行训练,在规定的代数内对网络参数优化,使得机器人在移动过程中能够快速响应环境的变化。通过对移动机器人在动、静态不同环境下的仿真实验,证明了方法的有效性。     

三维粒子测速系统中遗传匹配法的设计

提出了基于遗传算法的三维粒子图像匹配方法及实际中的优化设计。该方法以视差为编码、结合SSD(sum of square difference)与SAD(sum of absolute difference)法对结果进行评估,并通过单点、多点的双交叉、变异与序列的混沌化处理达到对分析空间的搜索;最后,局部处理结合全局唯一性迭代检测进一步增加了匹配结果的可信度。利用优化前后所提算法对空间粒子图进行了对比及误差分析。结果表明:所提算法适用于粒子测速系统的立体匹配,能够给出较准确的视差信息。     

基于粒子群算法的微纳米铁粉燃烧实验研究

为减少化石燃料的使用,开发清洁、低碳、可再生的新型能源,对微纳米铁粉燃料开展燃烧实验研究。通过微纳米铁粉的比表面积实验、热重分析实验、X射线衍射实验,得到不同粒径铁粉的比表面积、热重曲线以及X射线衍射图谱。分析粒径对比表面积、热重曲线的影响,研究不同粒径铁粉在40 K/min升温速率下的燃烧特性参数和燃烧动力学参数。并用粒子群算法拟合出微纳米铁粉的燃烧速率微分方程,建立微纳米铁粉的燃烧模型。结果表明：除了50 nm铁粉以外,随着粒径增大,铁粉的着火点温度、最高燃烧速率温度、燃尽温度、活化能、指前因子均增大。50 nm铁粉会在高温下发生熔化并凝结,使得燃尽温度上升,燃尽时间延长,不利于反应正常进行。对于粒径在50 nm～2μm范围内的铁粉,可以通过本文建立的铁粉燃烧速率微分方程近似计算不同粒径微纳米铁粉的燃烧特性参数和燃烧动力学参数,误差在允许的范围内。     

Fuzzy Economic Order Quantity Inventory Models Without Backordering

In economic order quantity models without backordering, both the stock cost of each unit quantity and the order cost of each cycle are characterized as independent fuzzy variables rather than fuzzy numbers as in previous studies. Based on an expected value criterion or a credibility criterion, a fuzzy expected value model and a fuzzy dependent chance programming (DCP) model are constructed. The purpose of the fuzzy expected value model is to find the optimal order quantity such that the fuzzy expected value of the total cost is minimal. The fuzzy DCP model is used to find the optimal order quantity for maximizing the credibility of an event such that the total cost in the planning periods does not exceed a certain budget level. Fuzzy simulations are designed to calculate the expected value of the fuzzy objective function and the credibility of each fuzzy event. A particle swarm optimization (PSO) algorithm based on a fuzzy simulation is designed, by integrating the fuzzy simulation and the PSO algorithm. Finally, a numerical example is given to illustrate the feasibility and validity of the proposed algorithm.     

基于修正扩展卡尔曼滤波和粒子滤波的混沌信号检测与跟踪

针对恶劣环境下混沌信号的检测与跟踪这一难题,以及基于一阶线性化的扩展卡尔曼滤波(EKF)技术存在的严重退化现象,提出了具有较强稳健性的修正EKF技术,获得了与Unscented Kalman filter相匹配的性能.针对上述两种滤波方法在低信噪比情况下存在跟踪误差大的问题,为此引入了新颖的粒子滤波技术并且分析了该技术的可行性,最后仿真实验验证了该技术在低信噪比环境下的优越性.     

敏化光催化降解曙红Y染料的研究

分别研究了TiO2溶胶体系和Fe2O3-TiO2复合溶胶体系在太阳光照射下对曙红Y染料的光催化降解作用.实验表明,通过窄能带隙的Fe2O3纳米粒子和TiO2耦合,不仅能将TiO2的光吸收延伸到可见光区域,而且提高了电荷分离的效率,因而这种复合粒子体系比单一的TiO2体系具有更高的光催化活性,能有效地提高光催化降解作用.     

相对论q-玻色气体的热力学性质

基于Thomas—Fermi半经曲近似即局域密度近似和平衡态化学势为常数原理,计算了相对论q-玻色气体的热力学量,得到了玻色爱因斯坦凝聚的判据以及热容量跃变的判据,这不同于以往文献的理论结果．     

太阳耀斑放能机制的讨论——评《太阳高能物理》

本文对《太阳高能物理》所表述的太阳耀斑流行理论作了讨论,给出一种新的太阳耀斑放能机制——"电子—离子束缚态及其引发核过程"模型。并指出太阳耀斑中存在两个独立过程,有～12.5keV(p-e-p～12.5keV)、～25keV(p-e-A ～25keV)、～25keV(d -e-d ～25keV)线发射和(d,d)聚变。对太阳耀斑的一些主要现象作了分析,给出了太阳耀斑中X射线和高能粒子与流行理论不同的产生机制。