ATM网上TCP传输的拥塞控制算法分析

本文讨论了ＴＣ庆ＡＴＭ网上存在的问题,对三种典型的ＡＢＲ拥塞控制算法;ＥＦＣＩ、ＥＲＩＣＡ和ＦＭＭＡＲＡ在减少信元丢失和传输时延,提高了ＴＣＰ吞吐量等方面的性能进行了分析和并提出了一些在拥塞控制算法中应考虑的问题。     

ADAPTIVE STRATEGIES FORNC MACHINING COMPOUND FREE-FORM SURFACES

ＡＤＡＰＴＩＶＥＳＴＲＡＴＥＧＩＥＳＦＯＲＮＣＭＡＣＨＩＮＩＮＧＣＯＭＰＯＵＮＤＦＲＥＥ－ＦＯＲＭＳＵＲＦＡＣＥＳＧａｏＳａｎｄｅ；ＺｈｏｕＹｕｎｆｅｉ；ＺｈａｎｇＸｉｎｆａｎｇ；ＺｈｏｕＪｉＡＤＡＰＴＩＶＥＳＴＲＡＴＥＧＩＥＳＦＯＲＮＣＭＡＣＨＩＮ...     

A COUNTER－EXAMPLE TO A FAST ALGORITHM FOR FINDING THE CONVEX HULL OF A SIMPLE POLYGON

ＡＣＯＵＮＴＥＲ－ＥＸＡＭＰＬＥＴＯＡＦＡＳＴＡＬＧＯＲＩＴＨＭＦＯＲＦＩＮＤＩＮＧＴＨＥＣＯＮＶＥＸＨＵＬＬＯＦＡＳＩＭＰＬＥＰＯＬＹＧＯＮＧｏｄｆｒｉｅｄＴｏｕｓｓａｉｎｔＡＣＯＵＮＴＥＲ－ＥＸＡＭＰＬＥＴＯＡＦＡＳＴＡＬＧＯＲＩＴＨＭＦＯＲＦＩ...     

A CONFIGURATION SYSTEM FOR FASTENER DESIGN（AN APPROACH TOWARDS COMPUTER AIDED CONCEPTUAL DESIGN）

ＡＣＯＮＦＩＧＵＲＡＴＩＯＮＳＹＳＴＥＭＦＯＲＦＡＳＴＥＮＥＲＤＥＳＩＧＮ（ＡＮＡＰＰＲＯＡＣＨＴＯＷＡＲＤＳＣＯＭＰＵＴＥＲＡＩＤＥＤＣＯＮＣＥＰＴＵＡＬＤＥＳＩＧＮ）ＧｕａｎｇＺｈｏｎｇ；ＭｉｋｅＤｏｏｎｅｒＡＣＯＮＦＩＧＵＲＡＴＩＯＮＳＹＳＴＥ...     

COMPUTER AIDED DESIGN OF AIRCRAFT CABIN LAYOUT

ＣＯＭＰＵＴＥＲＡＩＤＥＤＤＥＳＩＧＮＯＦＡＩＲＣＲＡＦＴＣＡＢＩＮＬＡＹＯＵＴ￥ＹａｎｇＧｕｏｎｇ；ＨｕａｎｇＹｕｙｕ（ＢｅｉｊｉｎｇＵｎｉｖｅｒｓｉｔｙｏｆＡｅｒｏｎａｕｔｉｃｓａｎｄＡｓｔｒｏｎａｕｔｉｃｓＢｅｉｊｉｎｇ．１０００８３）Ｐ．Ｒ．...     

上海软件行业协会工程设计CAD培训中心

电脑绘图类ＡＵＴＯＣＡＤＲ１４初、中、高级班ＡＵＴＯＣＡＤＲ２０００升级班、初级班ＭｉｃｒｏＳｔａｔｉｏｎ班电脑效果图制作类３ＤＳＶＩＺ +ＰＨＯＴＯ基础、提高班３ＤＳＭＡＸ +ＰＨＯＴＯ基础、提高班平面广告设计类ＰＨＯＴＯＳＨＯＰ +ＩＬＬＵＳＴＲＡＴＥ班ＣＯＲＥＬＤＲＡＷ +ＰＡＧＥＭＡＫＥ班电脑影视动画制作类３ＤＳＭＡＸ基础、提高班电脑实用技术培训类新版办公自动化班电子商务管理班电脑维护维修、组装、系统安装班干部信息化培训班多媒体与网页制作类ＦＲＯＮＴＰＡＧＥ +ＦＲＥＥＨＡＮＤ班Ｆｌａｓｈ +Ｄｒｅ…     

求多总线系统容错度的多项式时间算法

求多总线系统容错度的多项式时间算法陈四清，周六丁（重庆大学计算机科学系重庆６３００４４）ＰＯＬＹＮＯＭＩＡＬ－ＴＩＭＥＡＬＧＯＲＩＴＨＭＳＦＯＲＤＥＴＥＲＭＩＮＡＴＩＮＧＴＨＥＦＡＵＬＴ－ＴＯＬＥＲＡＮＣＥＤＥＧＲＥＥＯＦＭＵＬＴＩＢＵＳＳＹＳＴＥＭ...     

A COMPUTER MULTIMEDIA BOOK OF MECHANICAL DRAWING

ＡＣＯＭＰＵＴＥＲＭＵＬＴＩＭＥＤＩＡＢＯＯＫＯＦＭＥＣＨＡＮＩＣＡＬＤＲＡＷＩＮＧＺｕｏＺｏｎｇｙｉＦｅｎｇＫａｉｐｉｎｇＡｂｓｔｒａｃｔＴｈｉｓｒｅｐｏｒｔｇｉｖｅｓａｖｉｅｗｏｆａｃｏｍｐｕｔｅｒｍｕｌｔｉｍｅｄｉａｂｏｏｋｏｆｍｅｃｈａｎｉｃ...     

INTERFERENCE CHECKING IN COMPUTER-AIDED PLANT DESIGN SYSTEM

ＩＮＴＥＲＦＥＲＥＮＣＥＣＨＥＣＫＩＮＧＩＮＣＯＭＰＵＴＥＲ－ＡＩＤＥＤＰＬＡＮＴＤＥＳＩＧＮＳＹＳＴＥＭＷａｎｇＦｅｎｇＺｈａｏＨｏｎｇＮｉｕＪｕｎｙｕＡｂｓｔｒａｃｔＩｎｔｈｉｓｐａｐｅｒ，ｔｈｅｉｎｔｅｒｆｅｒｅｎｃｅｃｈｅｃｋｉｎｇｏｆｔｈｒ...     

人机界面研究现状评述

ＨＣＩ（ＨＵＭＡＮＣＯＭＰＵＴＥＲＩＮＴＥＲＡＣＴＩＯＮ或ＨＵＭＡＮＣＯＭＰＵＴＥＲＩＮＴＥＲＦＡＣＥ）是计算机科学技术领域的重要研究课题，它关系到计算机系统的可使用性和使用效率，是系统成败的关键因素．本文评述了ＨＣＩ的研究现状，针对ＨＣ研究中面临的困难与问题，并结合作者正在开展的研究课题，讨论ＨＣＩ今后的研究发展方向．     

Analysis of integer programming algorithms with L-partition and unimodular transformations

We study algorithms for solving integer linear programming problems, in particular, set packing and knapsack problems. We pay special attention to algorithms of lexicographic enumeration of L-classes and their combinations with other approaches. We study the problems of using unimodular transformations in order to improve the structure of the problems and speed up the algorithms. We construct estimates on the number of iterations for the algorithms that take into account the specific structure of the problems in question. We also show experimental results.     

密码体制的量子算法分析

很多快速量子算法都可以归结为隐子群问题的讨论,本文回顾了隐子群问题量子算法的基本思想,分析了群上量子算法的优越性。分析了可以归结为隐子群问题的公钥密码体制,描述了求解椭圆曲线上离散对数问题的量子算法,讨论了隐子群问题量子算法的局限性。     

贪心算法求解k-median问题

文章讨论了用贪心算法解k-m edian问题以及其试验结果。首先提出了一个解k-m edian问题的简单贪心算法,然后对求解质量和求解的近似性能比进行了探讨。主要讨论了公制空间和非公制空间初始解的产生,用贪心算法解k-m edian问题以及全局最优解的计算。试验结果表明:贪心算法解公制空间的k-m edian问题效果要好于解非公制空间的k-m edian问题;用贪心算法解公制空间和非公制空间k-m edian问题都能得到较好的结果。     

Generation of polynomial-time algorithms for some optimization problems on tree-decomposable graphs

Recent work in the design of efficient algorithms for optimization problems on treedecomposable graphs concentrates on developing general approaches which lead to families of related algorithms, rather than on developing isolatedad hoc algorithms. This paper develops new general approaches to obtain two new families of related polynomial-time algorithms for (1) packing, partitioning, and covering problems and (2) multiset and multiproperty problems. These problems have not been handled by previous general approaches.     

免疫调度算法综述

调度问题在科学和工程中广泛存在且难以求解,人们一直寻求先进的调度算法来解决这一问题．基于生物免疫系统的计算智能发展迅速,并很快被应用于调度问题,体现了其解决调度问题的优势和特色．对此,综述了国内外免疫调度理论和算法的研究现状,介绍了基于克隆选择、免疫网络、疫苗接种等原理的调度算法,并将其按免疫机理、优化目标以及解决的问题进行分类．最后给出了免疫调度算法在各领域中的应用及其未来研究展望．     

基于遗传算法的混合优化策略研究

最优化问题是工程设计、科学研究、经济管理等众多领域经常遇到的一类问题。随着待解决问题范围的不断扩大以及优化算法研究的不断深入,混合优化策略已成为解决大规模、高复杂度优化问题的一种重要而有效的方法。介绍了遗传算法、贪婪法、模拟退火算法、禁忌搜索的基本原理,阐述了各种算法的优缺点;针对各单一算法存在的缺陷和不足,对三种以遗传算法为主体框架的混合优化算法进行了分析;最后,指出了混合优化算法存在的问题及今后的发展方向。     

Parallel bioinspired algorithms for NP complete graph problems

It is no longer believed that DNA computing will outperform digital computers when it comes to the computation of intractable problems. In this paper, we emphasise the in silico implementation of DNA-inspired algorithms as the only way to compete with other algorithms for solving NP-complete problems. For this, we provide sticker algorithms for some of the most representative NP-complete graph problems. The simple data structures and bit-vertical operations make them suitable for some parallel architectures. The parallel algorithms might solve either moderate-size problems in an exact manner or, when combined with a heuristic, large problems in polynomial time.     

A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.     

Empirical analysis of five nature-inspired algorithms on real parameter optimization problems

In the past few years nature-inspired algorithms are seen as potential tools to solve computationally hard problems. Tremendous success of these algorithms in providing near optimal solutions has inspired the researchers to develop new algorithms. However, very limited efforts have been made to identify the best algorithms for diverse classes of problems. This work attempts to assess the efficacy of five contemporary nature-inspired algorithms i.e. bat algorithm (BA), artificial bee colony algorithm (ABC), cuckoo search algorithm (CS), firefly algorithm (FA) and flower pollination algorithm (FPA). The work evaluates the performance of these algorithms on CEC2014 30 benchmark functions which include unimodal, multimodal, hybrid and composite problems over 10, 30, 50 and 100 dimensions. Control parameters of all algorithms are self-adapted so as to obtain best results over benchmark functions. The algorithms have been evaluated along three perspectives (a) statistical significance using Wilcoxon rank sum test (b) computational time complexity (c) convergence rate of algorithms. Experimental results and analysis revealed that ABC algorithm perform best for majority of the problems on high dimension, while on small dimension, CS is the best choice. FPA attain the next best position follow by BA and FA for all kinds of functions. Self adaptation of above algorithms also revealed the best values of input parameters for various algorithms. This study may aid experts and scientists of computational intelligence to solve intricate optimization problems.     

A multi-objective membrane algorithm guided by the skin membrane

Multi-objective optimization problems exist widely in the field of engineering and science. Many nature-inspired methods, such as genetic algorithms, particle swarm optimization algorithms and membrane computing model based algorithms, were proposed to solve the problems. Among these methods, membrane computing model based algorithms, also termed membrane algorithms, are becoming a current research hotspot because the successful linkage of membrane computing and evolutionary algorithms. In the past years, a lot of effective multi-objective membrane algorithms have been designed, where the skin membrane was often only used as an archive to store good solutions. In this paper, we propose an effective multi-objective membrane algorithm guided by the skin membrane, named SMG-MOMA, where the information of solutions stored in the skin membrane is used to guide the evolution of internal membranes. A skin membrane guiding strategy is suggested by allocating the solutions in skin membrane to internal membranes. Experimental results on ZDT and DTLZ benchmark multi-objective problems show that the proposed algorithm outperforms the-state-of-the-art multi-objective optimization algorithms.