Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking

This paper deals with the problem of supervised wrapper-based feature subset selection in datasets with a very large number of attributes. Recently the literature has contained numerous references to the use of hybrid selection algorithms: based on a filter ranking, they perform an incremental wrapper selection over that ranking. Though working fine, these methods still have their problems: (1) depending on the complexity of the wrapper search method, the number of wrapper evaluations can still be too large; and (2) they rely on a univariate ranking that does not take into account interaction between the variables already included in the selected subset and the remaining ones.Here we propose a new approach whose main goal is to drastically reduce the number of wrapper evaluations while maintaining good performance (e.g. accuracy and size of the obtained subset). To do this we propose an algorithm that iteratively alternates between filter ranking construction and wrapper feature subset selection (FSS). Thus, the FSS only uses the first block of ranked attributes and the ranking method uses the current selected subset in order to build a new ranking where this knowledge is considered. The algorithm terminates when no new attribute is selected in the last call to the FSS algorithm. The main advantage of this approach is that only a few blocks of variables are analyzed, and so the number of wrapper evaluations decreases drastically.The proposed method is tested over eleven high-dimensional datasets (2400-46,000 variables) using different classifiers. The results show an impressive reduction in the number of wrapper evaluations without degrading the quality of the obtained subset.     

Sink placement on a 3D terrain for border surveillance in wireless sensor networks

With their distributed nature and redundant operation capability, wireless sensor networks are very suitable for border surveillance scenarios that track intruders trying to breach to a safe side. In such scenarios, keeping the operation going on for as long as possible is the most important aspect of the network. We propose that by placing sink at a carefully selected coordinate will results in a longer living network. We also place restrictions on the candidate locations so that the sensing quality of the network is above a useful predetermined value and the sink is placed in a relatively safe location to avoid destruction. In order to find the suitable coordinates we propose a modified lifetime metric which takes quality and safety measures into account. We also propose a genetic algorithm which uses a discrete event simulator-in-the-loop over a three dimensional terrain to find locations for the sink that fits the given quality and safety restrictions. Using a three dimensional underlying terrain makes the proposed approach more realistic. The results obtained for various sensor network scenarios indicate that the proposed algorithm can find locations that increase the lifetime by also considering the sensing quality and safety.     

复合量化多目标方法在维修力量派遣决策中的应用

针对部队装备维修决策特点,从多目标决策需求出发,结合三角模糊数结构特点,考虑非平衡指派和任务紧急程度,建立装备维修力量模糊多目标指派模型,用水平效应及不确定度对决策意识的模糊性进行描述并加以量化,应用改进遗传算法求多目标模糊派遣模型的最优决策解。实例仿真表明,其计算结果符合实际情况,其最优结果可为决策者提供决策依据,帮助提高维修保障调配的适时性和任务执行的有效性,充分发挥装备维修力量的作用。     

基于脉冲机动的引力辅助深空探测轨道设计

以借助金星引力的火星探测器轨道设计为背景,针对借力前后可能存在的速度不匹配问题,提出了一种施加脉冲机动的解决方式。首先以借力飞行的基本理论为基础,推导了施加脉冲的实现过程。其次,探讨了基于等高线图的引力辅助轨道设计方法,当速度不匹配时,不存在无动力的借力飞行,针对可能发生的速度不匹配的情况,文章采用在引力辅助过程完成之后,施加脉冲,提高探测器的速度,实现了速度不匹配的借力飞行情况。最后研究了遗传算法在引力辅助轨道设计中的应用,以金星借力的火星探测轨道为例,同样在无动力的引力辅助不满足要求的情况下,研究了基于遗传算法的引力辅助轨道优化设计流程,得出与等高线图法基本一致的结果。对两种方法的结果进行比较分析,验证了施加脉冲机动的方法在引力辅助轨道设计中的适用性。     

基于遗传算法的SVM冰凌预报模型研究

针对传统支持向量回归参数大多采用经验率定的情况,提出了一种基于遗传算法优化支持向量回归参数的冰凌预报模型.运用MATLAB软件进行建模,并将其应用于黄河宁蒙段三湖河口封、开河日期的预报.结果表明,经过优化的支持向量回归模型泛化能力强,预报效果优于传统支持向量回归模型.     

求解平衡运输问题的改进遗传算法

采用改进遗传算法求解平衡运输问题,针对平衡运输问题及其数学模型,应用改进的选择算子、交叉算子、变异算子和自适应交叉概率与变异概率等遗传算法机制,通过实例表明,该算法在求解平衡运输问题上的优越性.     

基于新障碍参数更新的二阶Mehrotra型预估—校正算法

针对二阶Mehrotra型预估-校正算法的一种变型算法,本文介绍一种新的自适应障碍参数更新法。利用该更新方法提出了相应的算法。新算法与之前的二阶Mehrotra型预估-校正算法相比,不用根据预估步和校正步的步长来确定参数的更新,而是在每步迭代中都采用自适应更新。最后证明了该算法在没有引进任何＂保障措施＂的情况下也具有相同的多项式时间复杂度。     

基于改进遗传算法的结构可靠度计算

针对传统的遗传算法容易产生早熟收敛现象以及局部搜索能力较差等缺陷,结合鱼群算法中具有加快寻优速度的追尾行为和克服局部极值能力较好的聚群行为对其进行改进。将改进后的遗传算法应用到框架结构的可靠度分析中,并以门式框架结构为例,建立了以单元截面积、外荷载为设计变量,可靠度指标为目标函数的优化模型。分别采用JC法与改进遗传算法对门式框架结构的可靠度指标进行对比计算,两种算法在同一验算点不同荷载值下的可靠度指标的计算结果相近,但改进后的遗传算法在分析过程中受到的约束条件较少,简单高效。     

Optimization of axial symmetrical FGM under thetransient-state temperate field

A numerical procedure for analyzing the temperature distribution in a hollow axisymmetric cylinder,made of functionally gradient material (FGM),was investigated.Based on the thermal elasticity theory and the arbitrary difference precise integration (ADPI) method,temperature distribution through the FGM cylinder in the ring section under a transient-state temperature field was developed and presented.A genetic algorithm (GA) was applied to the thermal stress optimal design of an FGM hollow cylinder,and as a result,the minimum thermal stress distribution in the FGM cylinder was obtained.A corresponding numerical procedure regarding to a ceramic-metal FGM cylinder was performed,and the computational results were discussed.