基于螺旋搜索机制的行星搜索算法

螺旋搜索机制的全局搜索能力强,广泛用于萤火虫算法及鲸鱼搜索算法,但其收敛速度慢,收敛精度低,局部搜索能力较差。通过改变收敛范围较小时的搜索模式,提出了局部螺旋搜索来提高其局部搜索能力,并引入变异操作提高其局部搜索能力,提出了行星搜索算法。通过对单峰及多峰值测试函数对该算法进行验证。结果表明行星搜索算法在收敛速度、搜索精度及局部搜索能力等方面较粒子群算法、萤火虫算法及鲸鱼搜索算法等有明显提升。     

实数编码量子共生演算法及其在云任务调度中的应用

针对共生演算法收敛慢和易陷入局部最优的问题,结合量子遗传算法理论,提出一种实数编码的量子共生演算法(real-coded quantum symbiotic organisms search,RQSOS)。首先依据三角模糊数提出差异度概念,并依此构造一个以自变量向量的分量和一对概率幅为等位基因的三倍染色体,使一条染色体携带更多信息并增强解的多样性;然后提出一种基于阿基米德螺旋线的探索学习模式,加强对解空间的探索精度;最后使用共生演算法更新差异度值并依据差异度值对种群进行学习和变异操作,促使整个种群快速向最优方向进化且减小了陷入局部最优的概率。利用数值优化问题和云任务调度问题对算法进行验证,仿真结果表明,RQSOS算法在收敛速度和寻优能力上均有明显提升,是一种可行有效的算法。     

基于引力搜索算法的分数阶变异时序回归GSA-TSGM(1,1)模型

为了利用分数阶累加算子在灰色短期预测中的高效性能,首次将分数阶累加算子引入变异时序回归模型以期取得更高的预测精度。主要方法如下：首先取湖北省链子崖某监测点1978—1987年的十年数据作为训练集并使用引力搜索算法确定最佳分数阶累加阶数,而1988—1993年的六年数据作为验证集验证提出的模型;其次对比了经典灰色模型GM(1,1)、分数阶累加灰色模型、变异时序回归模型TSGM(1,1)三种灰色模型。结果如下：首先修正了陈西江等人变异时序回归模型仿真时出现的错误,其次表明了相比于其他的模型,基于引力搜索算法的分数阶累加时序回归模型在进行灰色长期预测中具有较高的预测精度。因此,通过分数阶累加算子提高了灰色理论中长期预测模型的精度,为灰色长期预测提供了指导。     

Designing amino acid sequences to fold with good hydrophobic cores

We present two methods for designing amino acid sequences ofproteins that will fold to have good hydrophobic cores. Giventhe coordinates of the desired target protein or polymer structure,the methods generate sequences of hydrophobic (H) and polar(P) monomers that are intended to fold to these structures.One method designs hydrophobic inside, polar outside; the otherminimizes an energy function in a sequence evolution process.The sequences generated by these methods agree at the levelof 60–80% of the sequence positions in 20 proteins inthe Protein Data Bank. A major challenge in protein design isto create sequences that can fold uniquely, i.e. to a singleconformation rather than to many. While an earlier lattice-basedsequence evolution method was shown not to design unique folders,our method generates unique folders in lattice model tests.These methods may also be useful in designing other types offoldable polymer not based on amino acids     

SIMOP: Efficient reactive distillation optimization using stochastic optimizers

A methodology to improve the efficiency of stochastic methods applied to the optimization of chemical processes with a large number of equality constraints is presented. The methodology is based on two steps: (a) the optimization of the simulation step, which involves the optimum choice of design variables and subsystems to be simultaneously solved; (b) the optimization of the nonlinear programming (NLP) problem using stochastic methods. For the first step a flexible tool (SIMOP) is used, whereby different numerical procedures can be easily obtained, taking into account the problem formulation and specific characteristics, the need for specific initialization schemes and the efficient solution of systems of nonlinear equations. This methodology was applied to the optimization of a reactive distillation process for the production of ethylene glycol. Due to the complexity of the mathematical model, several different numerical procedures were generated, and their influence on the computational burden and on the reliability and accuracy of the optimization to reach the global optimum were studied. The results obtained suggest that in addition to the choice of design variables, the structure of subsystems associated to numerical procedures has a considerable impact on the performance of the optimizers.     

Enhanced user performance in an image gallery application with a mobile autostereoscopic touch display

In this study, we explored how stereoscopic depth affects performance and user experience in a mobile device with an autostereoscopic touch display. Participants conducted a visual search task with an image gallery application on three layouts with different depth ranges. The task completion times were recorded, and the participants were asked to rate their experiences. The results revealed that the image search times were facilitated by a mild depth effect and that too great a depth slowed search times and decreased user-experience ratings.     

Revisiting the two-stage choice model: an empirical study of consumer choice on brand website visits

In consumer choice behaviour literature, a two-stage choice model serves as a base theory where choice behaviour is decomposed into a consideration stage plus a choice stage. Yet, this default model has been increasingly challenged by the ‘limited consumer search’ school of thought because consumers do not necessarily review all products in a choice set before making decisions, but frequently draw on external information as mental short cuts. Methodologically, the choice behaviour in stage 1 has traditionally been latent because data that directly observe how consumers form their consideration sets by eliminating alternatives are lacking. This study continues the ‘limited consumer search’ line of reasoning and proposes an aided non-compensatory process in choice stage 1, where consumers reduce brand website alternatives by using online ratings to arrive at a consideration set. We use observed Web analytics data to unveil the stage 1 choice process and also the transition from stage 1 to stage 2, which is in need of further research based on extant literature. Lastly, we cross-validate our model with two types of websites (i.e. search vs. experience/credence) and find our model is contingent on the type of website content, where consumers’ inclination to use online ratings for decision-making varies.     

Prediction of DNA-binding regulatory proteins in bacteriophage T7

The high-resolution structure of several specific DNA-bindingproteins have been determined, and they display a common structuralmotif which mediates their binding to DNA. This motif consistsof two -helices connected by a sharp turn, and its amino acidsequence has several distinguishing features. A computer searchof the proteins coded by the genome of bacteriophage T7 hasbeen performed in an attempt to identify those proteins thatpotentially contain this motif. Eight proteins were found tohave regions similar to that of the motif. Of these, three arerelatively small, have no known function and are good candidatesfor being DNA-binding regulatory proteins. The methods describeduse commonly available computer programs and databases, andare therefore easy to implement.     

基于Nutch与元搜索引擎技术的高校网络舆情监测系统研究

针对高校网络舆情监测工作的不足,结合高校舆情信息的传播特点,提出了在校园网范围内基于Nutch搜索引擎技术进行信息检索,在互联网上采用元搜索引擎技术获取相关信息的舆情监测系统构建方案。采用关键词特征库匹配方式自动进行网络舆情监测,及时准确地发现网络舆情信息;探讨了高校对于网络舆情的监测、分析、引导和反馈等环节的处理方法,从而形成完善的应对机制。     

动态适应布谷鸟搜索算法

介绍一种新的生物启发算法—–布谷鸟搜索(CS)及其相关的L′evy飞行搜索机制.为了进一步提高算法的适应性,将反馈引入算法框架,建立了CS算法参数的闭环控制系统.将Rechenberg的1/5法则作为进化的评价指标,引入学习因子平衡种群的多样性和集中性,提出动态适应布谷鸟算法(DACS).最后,通过数值实验验证了所提出算法的有效性.