动态小生境半径两阶段多模态差分进化算法

针对多模态优化问题, 提出一种动态小生境半径两阶段多模态差分进化算法. 基于构象空间退火思想, 设计一种两阶段退火策略来动态调整小生境半径, 并根据退火过程将整个优化过程分为两个阶段. 在第1 阶段, 通过差分限制变异策略生成高质量的新个体来维持种群的多样性, 促进多模收敛; 在第2 阶段, 利用种子邻近变异策略对已探测到的生境高度搜索, 加快算法的收敛速度. 实验结果表明, 所提出算法能够有效实现从全局探测到局部增强的自适应平滑过渡, 是一种有效的多模态优化算法.

     

基于副本交换的局部增强差分进化蛋白质结构从头预测方法

针对蛋白质高维构象空间搜索问题,提出一种基于副本交换的局部增强差分进化蛋白质结构从头预测方法(RLDE)。首先,采用基于知识的Rosetta粗粒度能量模型显著降低构象空间优化变量维数;其次,引入基于片段库知识的片段组装技术进一步减小构象搜索空间,有效避免搜索过程中的熵效应;此外,在每个副本层设置构象种群,采用差分进化算法对种群进行更新,然后利用Monte Carlo算法对种群做局部增强,以此得到全局和部分局部最优构象。综上,RLDE利用差分进化算法较强的全局搜索能力可以对构象空间进行有效的全局搜索;借助Monte Carlo算法局部搜索性能对构象空间局部极小区域进行更为充分的采样;副本交换策略保证了副本层中种群的多样性,同时能够增强算法跳出局部极小的能力,从而使得算法对构象空间的搜索能力进一步增强。15个目标蛋白测试结果表明,所提方法能够有效地对构象空间采样,得到高精度的近天然态蛋白质构象。     

一种求解多模态复杂问题的混合和声差分算法

针对多模态复杂优化问题,提出了一种基于和声搜索和差分进化的混合优化算法:HHSDE算法。在不同的进化阶段,HHSDE算法依据累积加权更新成功率来自适应地选择和声算法或差分算法作为更新下一代种群的方式,并改进了差分算法的变异策略来平衡差分算法的全局与局部搜索能力。通过对10个多模态Benchmark函数进行测试,利用Wilcoxon秩和检验对不同算法的计算结果进行比较,结果表明HHSDE算法具有收敛速度快,求解精度高,稳定性好等优势。     

基于DE 和SA 的Memetic 高维全局优化算法

针对高维复杂多模态优化问题,传统的进化算法存在收敛速度慢,求解精度低等缺点,提出一种面向高维优化问题的Memetic全局优化算法。算法通过全局搜索和局部搜索结合的混合搜索策略,采用多模式并行差分进化算法进行全局搜索,基于高斯分布估计的模拟退火算法进行局部搜索。改进后的Memetic算法不仅继承了差分进化算法能发现全局最优解的优点,而且能大幅度提高搜索效率。最后,通过对4个高维多峰值Benchmark函数进行仿真实验,实验结果表明本文算法有效提高了算法的收敛速度和求解精度。     

求解高维多模优化问题的正交小生境自适应差分演化算法

针对传统优化算法在求解高维多模态优化问题时存在收敛速度慢、求解精度低的问题,提出一种基于正交设计与小生境精英策略的自适应差分进化算法ONDE。首先利用正交表产生初始种群,然后采用小生境精英策略来产生小生境种群(NP),并用小生境种群更新精英个体;接着应用拥挤裁剪避免种群陷入局部搜索,最后利用自适应差分变异算子改进了差分进化(DE)算法。通过对7个benchmark函数仿真验证,实验结果表明,算法在收敛速度、求解精度和稳定性方面都有较大优势。     

基于分解和差分进化的多目标粒子群优化算法

为了提高多目标优化算法解集的分布性和收敛性,提出一种基于分解和差分进化的多目标粒子群优化算法(dMOPSO-DE).该算法通过提出方向角产生一组均匀的方向向量,确保粒子分布的均匀性;引入隐式精英保持策略和差分进化修正机制选择全局最优粒子,避免种群陷入局部最优Pareto前沿;采用粒子重置策略保证群体的多样性.与非支配排序(NSGA-II)算法、多目标粒子群优化(MOPSO)算法、分解多目标粒子群优化(dMOPSO)算法和分解多目标进化-差分进化(MOEA/D-DE)算法进行比较,实验结果表明,所提出算法在求解多目标优化问题时具有良好的收敛性和多样性.     

基于知识引导的自适应动态多模态差分进化算法

为充分利用问题求解过程知识,提升动态多模态优化算法的计算资源利用效率,提出一种基于知识引导的自适应动态多模态差分进化算法.首先,利用自组织映射神经网络实现种群自聚类,形成稳定的小生境;然后,通过对种群全局知识和个体邻域知识的综合学习,设计一种基于知识引导的自适应差分进化算法,在对种群进化状态进行实时监测和分析的基础上,逐层递进地引导不同种群个体自适应地选择最符合当前进化需求的变异方式,提升种群搜索效率,平衡种群多样性与收敛性;最后,针对问题动态特性,设计一种基于历史动态过程知识引导的自适应动态响应机制,通过对历史寻优经验的自适应学习,预测生成新环境下的潜在精英个体,引导种群实现精准快速的多峰定位.实验结果表明,所提出算法能够有效解决动态多模态优化问题,且在不同动态环境设置下其求解性能均优于对比算法.     

蛋白质构象空间局部增强差分进化搜索方法

针对蛋白质构象空间搜索问题,提出一种蛋白质构象空间局部增强差分进化搜索方法。在差分进化算法框架下,采用Rosetta Score3粗粒度知识能量模型有效降低构象空间的搜索维数,加快算法收敛速度;引入基于知识的片段组装技术可以有效提高预测精度;利用Monte Carlo算法良好的局部搜索性能对种群做局部增强,以得到更为优良的局部构象;结合差分进化算法较强的全局搜索能力,可以对构象空间进行更为有效的采样。5个测试蛋白实验结果表明,所提算法具有较好的搜索性能和预测精度。     

基于免疫离散差分进化算法的复杂网络社区发现

针对复杂网络社区发现问题,在标准差分进化算法的框架下,提出一种新型免疫离散差分进化算法(Immune discrete differential evolution, IDDE).该算法通过标签传播策略生成初始种群,采用离散差分进化策略来保证种群在问题空间的全局搜索能力,同时对种群中的优秀个体执行针对性的高频克隆变异操作,以提高算法的局部开发能力,改善算法的收敛性能.在计算机生成网络与真实世界网络中的仿真实验结果表明:IDDE算法具有较强的寻优性能与鲁棒性,能够有效探测复杂网络中存在的社区结构.     

多模态优化问题的邻域低密度个体差分进化算法

针对多模态优化问题(MultiModal Optimization Problems, MMOPs)的求解,提出了一种基于邻域低密度个体的差分进化算法.该算法在每一代,首先使用密度峰值聚类的方法求得每一个个体的密度,然后,将当前个体邻域范围内密度更低的个体作为变异算子的基向量,随着种群的进化,算法将会自动从探索阶段转化为收敛阶段,进而平衡算法的探索与收敛能力.将提出的算法应用于CEC2013多模态基准测试函数并进行仿真实验,结果表明本文算法在评价指标峰值比和稳定性上与其它基于差分进化的多模态优化算法相比具有明显的优势,并随着测试函数的维度与复杂性的增大,优势就更加明显,其性能优于许多现有的基于差分进化的多模态优化算法.     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

基于QTE的蒙古文显示机制研究与实现

蒙古语言是中国蒙古族使用的通用语言,由于蒙古文区别于其他文字的书写方式和其自身变形机制等特点,在很多通用的文字处理引擎中都不被支持。在嵌入式产品开发与应用领域中Linux加QTE已经成为流行方式。该文给出了一种在QTE环境上实现基于标准Unicode的蒙古文点阵显示和变形算法, 并自定义了支持蒙古文的QTE组件,扩展了QTE功能,为在Linux加QTE方式的嵌入式体系结构中处理蒙古文提供了一种解决方法。