Web的前端性能优化研究与应用

随着互联网的发展,Web的加载速度成为Web网站能否推广应用的关键,而Web前端性能是影响其速度的主要因素。国内外在该领域均做了一定的研究,但对其优化的关键技术未做出很好综合。该文依托于商旅管理系统平台,将传统前端优化技术、超文本传输协议（Hyper Text Transfer Protocol,HTTP）协议优化以及Web预取缓存模型相结合作为其系统前端优化的处理方案。经验证,网页加载速度显著加快,用户体验随之提升,对前端优化的研究与应用具有重要的理论和实践意义。     

Web前端开发技术特征与完善策略探讨

当下我国数字经济大力发展,数字信息技术蓬勃发展,与之相关的互联网技术已经逐步渗透到人们的日常生产和生活中。前端开发技术是互联网技术中必不可少的重要技术,无论是基于Web页面还是App等前端界面,都是通过HTML、CSS及JavaScript等前端技术和架构来实现互联网产品的用户界面交互。现有的Web前端技术在加载时间、响应速度、用户体验等方面仍有很大的优化和发展空间。因此,互联网开发技术人员需要充分了解和掌握各种Web前端开发技术,通过技术优化减少HTTP的请求和错误。此外,为了提高用户的体验,在Web前端开发过程中应合理控制文件的大小,减少无效响应和DNS查询次数。     

基于Web的应用系统前端页面性能优化

页面性能优化是基于Web应用系统开发过程中的一个重要环节,本文通过服务器优化、JavaScript脚本和CSS优化、预加载等几个方面进行了探讨,提高了前端页面的性能。     

基于虚拟DOM的Web前端性能优化研究

随着Web前端的不断发展,Web页面变得越来越复杂,强交互性带来页面状态的疯涨,用于更新页面的DOM操作也越来越多。然而频繁的DOM操作使得页面渲染缓慢,造成前端性能瓶颈。在分析造成此问题原因的基础上,介绍一种由虚拟DOM和Diff算法来优化DOM操作的方法。通过分析传统Tree-Diff与DOM-Diff算法,对现有Diff算法做出改进。最后搭建测试平台,对改进后算法(Virtual-DOM)、React JS以及原生JS进行渲染性能测试,并对测试结果进行对比分析。测试结果表明:虚拟DOM确实可以优化浏览器的渲染性能,且在特定的情况下,DOM-Diff改进算法效果比React JS更理想。     

基于分片后备存储的WebKit嵌入式浏览器渲染优化研究

嵌入式浏览器越来越成为嵌入式系统上最重要的系统软件,随着HTML5的发展,对嵌入式浏览器性能的要求越来越高.通过对WebKit嵌入式浏览器渲染流程的研究,提出一种针对WebKit的基于分片后备存储的WebKit嵌入式浏览器渲染优化算法.该方法使用分片的后备存储优化渲染、缓存页面的可见区域,将Web页面的渲染区域精确到小的区域上,减少了不必要的区域的重新绘制,对于计算资源有限的嵌入式设备,大大减少了Web页面渲染的时间.实验表明,该方法有效地提高了嵌入式浏览器的页面渲染速度,提升了用户体验.     

3G 院线业务应用层的性能优化

提出了对Web性能前端进行改进的方法。3G院线网站开发完成以后,无论是通过管理员的使用,还是Yslow工具的分析,都表明该网站性能较低。整个网站加载速度慢,加载文件多。针对这一缺点,对3G院线网站进行了改进。主要是减少HTTP请求数、对文件进行压缩传输和在浏览器中对文件进行缓存来达到提高网站访问性能。     

Web数据库技术进展

一、引言 WWW是目前Internet上发展最快的领域,也是Internet网上最重要的信息检索手段。早期的Web页面(Home Page)主要用来传递静态HTML文档,后来由于CGI接口,特别是Java和JavaScript语言的引入,使得Web页面可以方便地传播动态信息。借助Java和JavaScript语言,可以设计出具有动画、声音、图形/图像和各种特殊效果的Web页面。 WWW的主要内容包括超文本传输协议(HTTP)、超文本标记语言(HTML)、通用网关接口(CGI)、Java和JavaScript语言等。 HTTP(Hyper Text Transfer Protocol),是一个专门为Web服务器和Web浏览器之间交换数据而设计的网络协议。它通过规定通用资源定位符(UBLs)使客户端的浏览器与服务器的Web资源建立链接关系,从而奠定     

Web页面加载方式研究分析与优化

随着Web 2.0的发展,页面内容愈加丰富,页面加载耗时也随之增长。传统页面加载方式浏览器和服务器不能同时工作导致页面加载效率低下,而改进的AJAX化页面加载方式却又增加了页面HTTP请求数量。针对页面加载方式理论研究的不足,尝试在.NET MVC环境下实现Big Pipe页面加载技术,并进行多线程优化。对比分析优化前后的Big Pipe页面加载方式和传统及AJAX页面加载方式,实验证明,优化后的多线程Big Pipe技术在加载多模块复杂页面时有更好的用户体验。     

基于DOM和Script的DHTML及其应用

随着Internet/Intranet热点的不断升温,Web网页开发技术逐步发展完善。动态HTML即DHTML作为HTML的扩展得到了广泛的应用。DHTML是一组建立和显示交互胜Web页面的技术,它包括动态样式、动态内容、二维层、数据捆绑和多媒体效果等。相对于HTML被加载到测览器后形成不会变化的静态页面,DHTML有着以下的特点：1）在页面加载到测览器后,页面的内容、格式等仍可以变化。这些变化是由客户端浏览器来完成的,而不需服务器端响应。2）页面中文档的内容、格式的改变可以通过脚本语言编程实现。这使得DHTML相对于传统的HTML有了一…     

基于前端的Web性能优化

随着互联网的高速发展,用户与页面的交互越来越复杂,会话时间也越来越长,同时人们对互联网的依赖性日益增强,网站性能逐渐成为Web产品市场成败和评测网站用户体验的重要指标之一。文中着重从前端的角度对Web性能优化的方法和策略进行分析与研究。     

Seeker optimisation (human group optimisation) algorithm with chaos

Seeker optimisation algorithm (SOA), also referred to as human group metaheuristic optimisation algorithms form a very hot area of research, is an emerging population-based and gradient-free optimisation tool. It is inspired by searching behaviour of human beings in finding an optimal solution. The principal shortcoming of SOA is that it is easily trapped in local optima and consequently fails to achieve near-global solutions in complex optimisation problems. In an attempt to relieve this problem, in this article, chaos-based strategies are embedded into SOA. Five various chaotic-based SOA strategies with four different chaotic map functions are examined and the best strategy is chosen as the suitable chaotic scheme for SOA. The results of applying the proposed chaotic SOA to miscellaneous benchmark functions confirm that it provides accurate solutions. It surpasses basic SOA, genetic algorithm, gravitational search algorithm variant, cuckoo search optimisation algorithm, firefly swarm optimisation and harmony search the proposed chaos-based SOA is expected successfully solve complex engineering optimisation problems.     

The multi-disciplinary design of a large-scale civil aircraft wing taking account of manufacturing costs

This paper presents results from a major research programme funded by the European Union and involving 14 partners from across the Union. It shows how a complex tool set was assembled which was able to optimise a large civil airliner wing for weight, drag and cost. A multi-level MDO process was constructed and implemented through a hierarchical system in which cost comprised the top level. Conventional structural sizing parameters were employed to optimise structural weight but the upper-level optimisation used 6 overall design variables representing major design parameters. The paper concludes by presenting results from a case study which included all the components of the total design system.     

Dynamic Ant Colony Optimisation

Ant Colony optimisation has proved suitable to solve static optimisation problems, that is problems that do not change with time. However in the real world changing circumstances may mean that a previously optimum solution becomes suboptimal. This paper explores the ability of the ant colony optimisation algorithm to adapt from the optimum solution for one set of circumstances to the optimal solution for another set of circumstances. Results are given for a preliminary investigation based on the classical travelling salesman problem. It is concluded that, for this problem at least, the time taken for the solution adaption process is far shorter than the time taken to find the second optimum solution if the whole process is started over from scratch.     

Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems

Particle swarm optimisation (PSO) is a well-established optimisation algorithm inspired from flocking behaviour of birds. The big problem in PSO is that it suffers from premature convergence, that is, in complex optimisation problems, it may easily get trapped in local optima. In this paper, a new PSO variant, named as enhanced leader PSO (ELPSO), is proposed for mitigating premature convergence problem. ELPSO is mainly based on a five-staged successive mutation strategy which is applied to swarm leader at each iteration. The experimental results confirm that in all terms of accuracy, scalability and convergence rate, ELPSO performs well.     

A genetic algorithm for combined topology and shape optimisations

A method to find optimal topology and shape of structures is presented. With the first the optimal distribution of an assigned mass is found using an approach based on homogenisation theory, that seeks in which elements of a meshed domain it is present mass; with the second the discontinuous boundaries are smoothed. The problem of the optimal topology search has an ON/OFF nature and has suggested the employment of genetic algorithms. Thus in this paper a genetic algorithm has been developed, which uses as design variables, in the topology optimisation, the relative densities (with respect to effective material density) 0 or 1 of each element of the structure and, in the shape one, the coordinates of the keypoints of changeable boundaries constituted by curves. In both the steps the aim is that to find the variable sets producing the maximum stiffness of the structure, respecting an upper limit on the employed mass. The structural evaluations are carried out with a FEM commercial code, linked to the algorithm. Some applications have been performed and results compared with solutions reported in literature.     

A new effective operator for the hybrid algorithm for solving global optimisation problems

Hybrid algorithms have been recently used to solve complex single-objective optimisation problems. The ultimate goal is to find an optimised global solution by using these algorithms. Based on the existing algorithms (HP_CRO, PSO, RCCRO), this study proposes a new hybrid algorithm called MPC (Mean-PSO-CRO), which utilises a new Mean-Search Operator. By employing this new operator, the proposed algorithm improves the search ability on areas of the solution space that the other operators of previous algorithms do not explore. Specifically, the Mean-Search Operator helps find the better solutions in comparison with other algorithms. Moreover, the authors have proposed two parameters for balancing local and global search and between various types of local search, as well. In addition, three versions of this operator, which use different constraints, are introduced. The experimental results on 23 benchmark functions, which are used in previous works, show that our framework can find better optimal or close-to-optimal solutions with faster convergence speed for most of the benchmark functions, especially the high-dimensional functions. Thus, the proposed algorithm is more effective in solving single-objective optimisation problems than the other existing algorithms.     

一种解决复合形局部最优及加速计算的方法

对求解非线性约束优化问题的复合形法陷入局部最优的问题进行探讨,给出了一种改进的方法.改进后的方法不仅可以有效地寻找全局最优解,而且计算速度较传统复合形算法快.     

Finding the maximal membership in a fuzzy set of an element from another fuzzy set

The problem of finding the maximal membership grade in a fuzzy set of an element from another fuzzy set is an important class of optimisation problems manifested in the real world by situations in which we try to find what is the optimal financial satisfaction we can get from a socially responsible investment. Here, we provide a solution to this problem. We then look at the proposed solution for fuzzy sets with various types of membership grades, ordinal, interval value and intuitionistic.     

Chaotic bat swarm optimisation (CBSO)

Bat swarm optimisation (BSO) is a novel heuristic optimisation algorithm that is being used for solving different global optimisation problems. The paramount problem in BSO is that it severely suffers from premature convergence problem, that is, BSO is easily trapped in local optima. In this paper, chaotic-based strategies are incorporated into BSO to mitigate this problem. Ergodicity and non-repetitious nature of chaotic functions can diversify the bats and mitigate premature convergence problem. Eleven different chaotic map functions along with various chaotic BSO strategies are investigated experimentally and the best one is chosen as the suitable chaotic strategy for BSO. The results of applying the proposed chaotic BSO to different benchmark functions vividly show that premature convergence problem has been mitigated efficiently. Actually, chaotic-based BSO significantly outperforms conventional BSO, cuckoo search optimisation (CSO), big bang-big crunch algorithm (BBBC), gravitational search algorithm (GSA) and genetic algorithm (GA).     

Hindrances for robust multi-objective test problems

Despite the significant number of benchmark problems for evolutionary multi-objective optimisation algorithms, there are few in the field of robust multi-objective optimisation. This paper investigates the characteristics of the existing robust multi-objective test problems and identifies the current gaps in the literature. It is observed that the majority of the current test problems suffer from simplicity, so five hindrances are introduced to resolve this issue: bias towards non-robust regions, deceptive global non-robust fronts, multiple non-robust fronts (multi-modal search space), non-improving (flat) search spaces, and different shapes for both robust and non-robust Pareto optimal fronts. A set of 12 test functions are proposed by the combination of hindrances as challenging test beds for robust multi-objective algorithms. The paper also considers the comparison of five robust multi-objective algorithms on the proposed test problems. The results show that the proposed test functions are able to provide very challenging test beds for effectively comparing robust multi-objective optimisation algorithms. Note that the source codes of the proposed test functions are publicly available at www.alimirjalili.com/RO.html.