A cluster-DEE-based strategy to empower protein design

The Medical and Pharmaceutical industries have shown high interest in the precise engineering of protein hormones and enzymes that perform existing functions under a wide range of conditions. Proteins are responsible for the execution of different functions in the cell: catalysis in chemical reactions, transport and storage, regulation and recognition control. Computational Protein Design (CPD) investigates the relationship between 3-D structures of proteins and amino acid sequences and looks for all sequences that will fold into such 3-D structure. Many computational methods and algorithms have been proposed over the last years, but the problem still remains a challenge for Mathematicians, Computer Scientists, Bioinformaticians and Structural Biologists. In this article we present a new method for the protein design problem. Clustering techniques and a Dead-End-Elimination algorithm are combined with a SAT problem representation of the CPD problem in order to design the amino acid sequences. The obtained results illustrate the accuracy of the proposed method, suggesting that integrated Artificial Intelligence techniques are useful tools to solve such an intricate problem.     

Genetic algorithm for S-transform optimisation in the analysis and classification of electrical signal perturbations

At present, many analytical methods are used for the analysis, detection and classification of electrical signal perturbations. One of these methods, the S-transform, has proven effective under specific conditions for acquiring information and parameters of interest associated with a signal. However, depending on the nature of the signal and the input parameters, this method offers different results that sometimes negatively impact the quality of information obtained in the time and frequency domains.This paper describes the design of a genetic algorithm that optimises the S-transform for analysis and classification of the perturbations in electrical signals. This algorithm provides the best parameter values for optimising the Gaussian window, maximising the precision obtained with regard to classification and, later, analysis (via other techniques, such as neural networks or rule-based systems).This paper demonstrates the effectiveness of the S-transform (specified herein) with respect to the original S-transform and reports the best values obtained after optimisation via a comparative study that includes both typical cases and perturbations in modern electrical systems.     

Extending the information delivery manual approach to identify information requirements for performance analysis of HVAC systems

Heating, Ventilation and Air-Conditioning (HVAC) systems account for more than 15% of the total energy consumption in the US. In order to improve the energy efficiency of HVAC systems, researchers have developed hundreds of algorithms to automatically analyze their performance. However, the complex information, such as configurations of HVAC systems, layouts and materials of building elements and dynamic data from the control systems, required by these algorithms inhibits the process of deploying them in real-world facilities. To address this challenge, we envision a framework that automatically integrates the required information items and provides them to the performance analysis algorithms for HVAC systems. This paper presents an approach to identify and document the information requirements from the publications that describe these algorithms. We extend the Information Delivery Manual (IDM) approach so that the identified information requirements can be mapped to multiple information sources that use various formats and schemas. This paper presents the extensions to the IDM approach and the results of using it to identify information requirements for performance analysis algorithms of HVAC systems.     

M eta- heur ist ic 算法研究进展

对模拟退火、遗传算法和禁忌搜索法等代表性ｍｅｔａ－ｈｅｕｒｉｓｉｃ算法在理论与应用方面的研究进行综述,探讨自满结构和研究体系上的统一性,并归纳指出其发展方向。     

Optimal radio resources and transmission formats assignment in OFDMA systems

The problem of assigning radio resources and transmission formats to users in the downlink of an OFDMA network is addressed. In particular, a single cell environment with a realistic interference model and a margin adaptive approach is considered, i.e., the aim is of minimizing the total transmission power while maintaining a certain given rate for each user. The computational complexity issues of the problem are discussed, and an approximation analysis is presented. Heuristic approaches, based on rounding techniques and graph models, are also proposed. Computational experiences show that, in a comparison with a commercial state-of-the-art optimization solver and with alternatives from the literature, the proposed algorithms are effective in terms of solution quality and CPU times.     

高频地波雷达系统优化控制的研究与建模

现代电子工业技术的发展使高频地波雷达的功能不断扩增,导致雷达系统控制十分复杂。因此,需要通过系统的性能评估、优化及智能控制使之能够在各种情况下发挥其最大效能。依据高频地波雷达的特点和探测目标类型进行系统建模仿真。考虑不同模糊控制方法在高频地波雷达系统中的可行性,将遗传算法和粒子群优化算法等方法与模糊控制结合实现对雷达系统的优化控制。通过对雷达信号频率、功率和相参积累时间的模糊控制达到期望的工作性能。利用 Matlab设计 GUI控制界面。     

用改进的遗传算法实现架构恢复

高层架构恢复对软件维护和软件进化至关重要.把实现架构恢复的聚类问题看作优化问题,通过对常规遗传算法中初始群体产生策略、选择操作方法、交叉概率和变异概率的自适应性等重要参数和关键环节的改进,设计并实现了混合遗传聚类算法(hybrid genetic clustering algorithm,简称HGCA).同时也对该算法的有效性和综合性能进行了实验分析,结果表明,该算法对初始群体的产生有较好的约束作用.与传统遗传算法相比,它的群体性能和收敛性能都较优,且收敛精度高.同时,基于MoJo度量模型的相似性度量值充分说明了HGCA算法对架构恢复的正确性和有效性.     

双链量子遗传算法的收敛性分析

基于实数编码和目标函数梯度信息的双链量子遗传算法可增加种群的多样性、扩大解空间的搜索域、加速算法的进化进程、避免早熟收敛现象,但没有从理论上证明该算法的收敛性。为此,给出相应的定理,利用定理从理论上证明该算法的收敛性,通过仿真实例,论述量子编码和量子旋转门对算法收敛性和优化效率的影响。结果表明,该研究丰富和完善了双链量子遗传理论。     

基于混合递阶遗传算法的径向基神经网络学习算法及其应用

在研究径向基神经网络学习算法的基础上, 提出了一种新型的径向基神经网络学习算法———混合递阶遗传算法. 该算法将递阶遗传算法和最小二乘法的优点结合在一起, 能够同时确定径向基神经网络的结构和参数, 并具有较高的学习效率. 采用基于混合递阶遗传算法的径向基神经网络对混沌时间序列学习和预测, 取得了较好的效果.     

基于代理模型和改进遗传算法的注塑翘曲优化

提出一种最小化制品翘曲的注塑工艺参数优化集成方法.以空调柜机顶盖注塑制品开发为例,该方法使用Moldflow软件分析制品的翘曲变形,运用田口方法确定与制品翘曲量密切相关的工艺因素,然后采用响应曲面法(RSM)和改进的精英保留自适应遗传算法(EAGA)相结合的方法,建立主要影响工艺参数与制品翘曲量之间的关系模型,通过对模型寻优以实现对制品翘曲的优化.该方法的适用性在制品的实际生产中得到了验证.