催化剂对固体电解质SO2气体传感器响应特性的影响

为了降低固体电解质ＳＯ２气体传感器的工作温度，提高传感器响应精度，制备了Ｐｔ－网，Ｐｔ－Ａｌ２Ｏ３小球，Ｐｔ－石棉和Ｖ２Ｏ５等催化剂，研究了这些催化剂对ＳＯ２＋１／２Ｏ２－ＳＯ３反应的催化活性，以及这些催化剂对固化电解质ＳＯ２气体传感器工作温度和响应性的影响。     

铂系金属氧化物对增强Al2O3热 稳定性作用的研究‘

为探索铂金属氧化物（以下均用ＭＯｘ代表）对增强载体Ａｌ２Ｏ３热稳定性作用的机理。本文采用了氮物理等温吸附，Ｘ－射线物相分析，电子探针及附载催化剂的活性试验等多种方法，研究了Ａｌ２Ｏ３和ＭＯｘ－Ａｌ２Ｏ３两种物系的物理和化学性质，认为ＭＯｘ对增强协体Ａｌ２Ｏ３热稳定性的作用，主要是通过ＭＯｘ与Ａｌ２Ｏ３间存在较强的相互作用，从而提高了Ａｌ＾３＋和Ｏ＾－２的扩散活化能，在一定程度上抑制了它们间的互扩散     

Fe2O3气敏材料的制备及掺杂研究

作者以不同铁盐与ＮａＯＨ反应，用共沉淀法磷选出较好的γ－Ｆｅ２Ｏ３气敏材料；再通过多种掺杂，研究杂质对材料灵敏度的影响，寻找γ－Ｆｅ２Ｏ３对ＣＯ，Ｈ２，ＬＰＧ灵敏的适宜掺杂剂，以促进γ－Ｆｅ２Ｏ３气敏元件的实用化。同时，作者又通过对掺杂物热力学数据的研究，初步发现了一条选择合宜掺杂剂的规律。     

Mn（NO3）2溶液热扩散的TiO2压敏电阻—电容性能

对于Ｎｂ２Ｏ５施主掺杂的ＴｉＯ２半导瓷，掺入ＢａＣＯ３，Ｂｉ２Ｏ３可形成晶界绝缘层并可降低烧结温度，采用Ｍｎ（ＮＯ３）２溶液热扩散的方法使Ｍｎ＾２＋进入晶界可提高晶界热垒，从而提高压敏电压及非线性系数，大幅度降低介电损耗。     

氧化铁薄膜的制备及其对乙醇气体敏感特性的研究

本文报道了以ＦｅＣｌ３·６Ｈ２Ｏ为原料,用热水解法和渗析法制得纯净Ｆｅ（ＯＨ）３胶体后,用加热离心法将胶体涂覆于载玻上,在马弗炉中４００Ｃ下加热３．５小时来制取Ｆｅ２Ｏ３薄膜的方法。ＸＲＤ研究表明：膜层主国是由α－Ｆｅ２Ｏ３和γ－Ｆｅ２Ｏ３两种晶体混合而成。膜片电阻测试表明：常温下膜片即对乙醇气体敏感。     

应用分子力学研究丙烷在晶体上的氨氧化反应

应用分子力学评价在α－Ｓｂ２Ｏ４、β－Ｓｂ２Ｏ４及Ｖ２Ｏ５ ３种晶体上丙烷氨氧化反应物种的吸附及脱附能力,并与丙烷的反应活性和生成丙烯腈选择性的实验数据进行较好的关联。计算结果表明,分子力学可以来研究化学反应的物理阶段,所得分子模拟结果可以对催化剂的设计和开发提供参考和指导作用。     

遗传算法的运行机理分析

遗传算法是一种自适应启发工群体型迭代式全局搜索算法，正受到许多学科的重视。本文首先以函数优化为例分析了遗传算法的运行过程，然后着重探讨了遗传算法的全局收敛性和效率问题，提出了有效基因的新概念及有效基因突变操作，推导出每次遗传搜索产生Ｏ（２＾ｌ－１）数量级的新模式，最后给出了结论。     

电导型CuO—BaTiO3系CO2气体传感器

本文研究了以ＣｕＯ－ＢａＴｉＯ_３作为对ＣＯ_２敏感的基体材料,用金属或金属氧化物进行掺杂以提高其对ＣＯ_２气体的灵敏度．经过比较发现以Ａｇ掺杂的ＣｕＯ－ＢａｉＴＯ_３材料具有较低的工作温度,较高的灵敏度．ＣｕＯ－ＢａＴｉＯ_３的ＣＯ_２气体传感器检测的浓度范围为１００ ×１０~(－６)～１０％．ＣｕＯ－ＢａＴｉＯ_３的 ＣＯ_２气体传感器具有较好的稳定性和选择性．     

PYROSOL薄膜淀积技术

介绍了一种新的膜淀积技术－ＰＹＲＯＳＯＬ技术的原理和方法，总结了一些化合物薄膜的制备条件。给出了Ｆｅ２Ｏ３、ＺｒＯ２薄膜生长的影响因素。还介绍了ＳｉＯ２膜的ＰＹ ＲＯＳＯＬ溶胶———凝胶制备方法。     

四价金属元素锆（Zr）对氧化铁薄膜气敏特性的影响

对用常压化学气相淀积（ＡＰＣＶＤ）工艺制备的纯α－Ｆｅ２Ｏ３薄膜和掺锆（Ｚｒ）α－Ｆｅ２Ｏ３薄膜的气敏特性进行了研究。实验表明掺Ｚｒ是改善α－Ｆｅ２Ｏ３薄膜材料气敏特性的一种有效途径。     

动态系统的常微分方程组建模—基于不同搜索技术的实验研究

以人口模型和化学反应模型为例,通过大量实验研究比较了分别采用基于两种传统的搜索方法即局部搜索算法和模拟退火算法、遗传算法（简称GA）四者相结合的14种不同算法建立动态系统的常微分方程组模型的实验结果,得到了有关各算法性能比较的一些新的结论。两个实例的实验结果表明：在14种算法中,GP＋GA＋LS－MU算法（即在采用GP的模型结构的优化过程中嵌入采用GA的模型参数的优化过程,并且在每一演化代对种群中的部分个体进行基于GP的标准变异算子产生邻域解的局域搜索过程）是目前解决常微分方程组建模问题的最好算法。     

Fuzzy genetic sharing for dynamic optimization

Recently,genetic algorithms(GAs) have been applied to multi-modal dynamic optimization(MDO).In this kind of optimization,an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing optimum.Our fuzzy genetic sharing(FGS) approach is based on a novel genetic algorithm with dynamic niche sharing(GADNS).FGS finds the optimal solutions,while maintaining the diversity of the population.For this,FGS uses several strategies.First,an unsupervised fuzzy clustering method is used to track multiple optima and perform GADNS.Second,a modified tournament selection is used to control selection pressure.Third,a novel mutation with an adaptive mutation rate is used to locate unexplored search areas.The effectiveness of FGS in dynamic environments is demonstrated using the generalized dynamic benchmark generator(GDBG).     

AN ANALYSIS OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS FOR OPTIMIZATION PROBLEMS WITH TIME CONSTRAINTS

Many multiobjective optimization problems in the engineering field are required to be solved within more or less severe time restrictions. Because the optimization criteria, the parameters, and/or constraints might change with time, the optimization solutions must be recalculated when a change takes place. The time required by the optimization procedure to arrive at the new solutions should be bounded accordingly with the rate of change observed in these dynamic problems. This way, the faster the optimization algorithm is to obtain solutions, the wider is the set of dynamic problems to which that algorithm can be applied. Here, we analyze the performance of the nondominated sorting algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA2), and single front genetic algorithms (SFGA, and SFGA2) on two different multiobjective optimization problems, with two and three objectives, respectively. For these two studied problems, the single front genetic algorithms have obtained adequate quality in the solutions in very little time. Moreover, for the second and more complex problem approached, SFGA2 and NSGA-II obtain the best hypervolume in the found set of nondominated solutions, but SFGA2 employs much less time than NSGA-II. These results may suggest that single front genetic algorithms, especially SFGA2, could be appropiate to deal with optimization problems with high rates of change, and thus stronger time constraints.     

Genetic algorithms and neural networks: optimizing connections and connectivity

Genetic algorithms are a robust adaptive optimization method based on biological principles. A population of strings representing possible problem solutions is maintained. Search proceeds by recombining strings in the population. The theoretical foundations of genetic algorithms are based on the notion that selective reproduction and recombination of binary strings changes the sampling rate of hyperplanes in the search space so as to reflect the average fitness of strings that reside in any particular hyperplane. Thus, genetic algorithms need not search along the contours of the function being optimized and tend not to become trapped in local minima. This paper is an overview of several different experiments applying genetic algorithms to neural network problems. These problems include

1. (1) optimizing the weighted connections in feed-forward neural networks using both binary and real-valued representations, and

2. (2) using a genetic algorithm to discover novel architectures in the form of connectivity patterns for neural networks that learn using error propagation.

Future applications in neural network optimization in which genetic algorithm can perhaps play a significant role are also presented.     

Coevolutionary augmented Lagrangian methods for constrainedoptimization

This paper introduces a coevolutionary method developed for solving constrained optimization problems. This algorithm is based on the evolution of two populations with opposite objectives to solve saddle-point problems. The augmented Lagrangian approach is taken to transform a constrained optimization problem to a zero-sum game with the saddle point solution. The populations of the parameter vector and the multiplier vector approximate the zero-sum game by a static matrix game, in which the fitness of individuals is determined according to the security strategy of each population group. Selection, recombination, and mutation are done by using the evolutionary mechanism of conventional evolutionary algorithms such as evolution strategies, evolutionary programming, and genetic algorithms. Four benchmark problems are solved to demonstrate that the proposed coevolutionary method provides consistent solutions with better numerical accuracy than other evolutionary methods     

Parallel genetic algorithms: a survey and problem state of the art

In relation with development of computer capabilities and the appearance of multicore processors, parallel computing made it possible to reduce the time for solution of optimization problems. At present of interest are methods of parallel computing for genetic algorithms using the evolutionary model of development in which the main component is the population of species (set of alternative solutions to the problem). In this case, the algorithm efficiency increases due to parallel development of several populations. The survey of basic parallelization strategies and the most interesting models of their implementation are presented. Theoretical ideas on improvement of existing parallelization mechanisms for genetic algorithms are described. A modified model of parallel genetic algorithm is developed. Since genetic algorithms are used for solution of optimization problems, the proposed model was studied for the problem of optimization of a multicriteria function. The algorithm capabilities of getting out of local optima and the influence of algorithm parameters on the deep extremum search dynamics were studied. The conclusion on efficiency of application of dynamic connections of processes, rather than static connections, is made. New mechanisms for implementation and analysis of efficiency of dynamic connections for distributed computing in genetic algorithms are necessary.     

GENETIC ALGORITHM APPROACH TO A LUMBER CUTTING OPTIMIZATION PROBLEM

Genetic algorithms are a technique for search and optimization based on the Darwinian principle of natural selection. They are iterative search procedures that maintain a population of candidate solutions. The best or most fit solutions in that population are then used as the basis for the next generation of solutions. The next generation is formed using the genetic operators reproduction, crossover, and mutation. Genetic algorithms have been successfully applied to engineering search and optimization problems. This paper presents a discussion of the basic theory of genetic algorithms and presents a genetic algorithm solution of a lumber cutting optimization problem. Dimensional lumber is assigned a grade that represents its physical properties. A grade is assigned to every board segment of a specific length. The board is then cut in various locations in order to maximize its value, A genetic algorithm was used to determine the cutting patterns that would maximize the board value.     

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments

Abstract In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.     

Combining genetic algorithms with BESO for topology optimization

This paper proposes a new algorithm for topology optimization by combining the features of genetic algorithms (GAs) and bi-directional evolutionary structural optimization (BESO). An efficient treatment of individuals and population for finite element models is presented which is different from traditional GAs application in structural design. GAs operators of crossover and mutation suitable for topology optimization problems are developed. The effects of various parameters used in the proposed GA on the optimization speed and performance are examined. Several 2D and 3D examples of compliance minimization problems are provided to demonstrate the efficiency of the proposed new approach and its capability of obtaining convergent solutions. Wherever possible, the numerical results of the proposed algorithm are compared with the solutions of other GA methods and the SIMP method.     

A Hybrid Immigrants Scheme for Genetic Algorithms in Dynamic Environments

Dynamic optimization problems are a kind of optimization problems that involve changes over time.They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time.Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years.Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments.One approach is to maintain the diversity of the population via random immigrants.This paper proposes a hybrid immigrants scheme that combines the concepts of elitism,dualism and random immigrants for genetic algorithms to address dynamic optimization problems.In this hybrid scheme,the best individual,i.e.,the elite,from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme.These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population,replacing the worst individuals in the population.These three kinds of immigrants aim to address environmental changes of slight,medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes.Based on a series of systematically constructed dynamic test problems,experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme.Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.