遗传算法用于电子枪发射系统的全局最优化设计

常用的优化设计方法 ,如单纯形法、Powell法等 ,易陷入局部最优解。而遗传算法是一种新兴的直接搜索最优化算法 ,它模拟达尔文遗传选择与自然进化的理论 ,根据“适者生存”和“优胜劣汰”的原则 ,借助“复制”、“交换”、“突变”等操作可以得到全局最优解。本文将遗传算法运用于电子枪发射系统的最优化设计 ,得到了使交叠点半径尽可能小的发射系统的最佳结构和相应电参量     

Optimal Design of Alloy Steels Using Multiobjective Genetic Algorithms

Determining the optimal heat treatment regimen and the required weight percentages for the chemical composites to obtain the desired mechanical properties of steel is a challenging problem for the steel industry. To tackle what is in essence an optimization problem, several neural network-based models, which were developed in the early stage of this research work, are used to predict the mechanical properties of steel such as the tensile strength (TS), the reduction of area (ROA), and the elongation. Because these predictive models are generally data driven, such predictions should be treated carefully. In this research work, evolutionary multiobjective (EMO) optimization algorithms are exploited not only to obtain the targeted mechanical properties but also to consider the reliability of the predictions. To facilitate the implementation of a broad range of single-objective and multi-objective algorithms, a versatile Windows 2000®-based application is developed. The obtained results from the single-objective and the multiobjective optimization algorithms are presented and compared, and it is shown that the EMO techniques can be effectively used to deal with such optimization problems.     

一种混合优化策略在工步排序中的应用

结合创成式ＣＡＰＰ系统中工步优化问题，介绍了由遗传算法（ＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍｓ，ＧＡ）和模拟退火算法（ＳｉｍｕｌａｔｅｄＡｎｎｅａｌｉｎｇＡｌｇｏｒｉｔｈｍｓ，ＳＡ）构成的混合寻优策略。最后以一箱体的工艺规划过程为例，将基于混合寻优策略的工步排序融入以加工中心为主要加工设备的ＣＡＰＰ工艺决策过程。     

栅型锻件等温成形计算机模拟

用有限元方法模拟栅型锻件的等温锻造成形过程。发现了在工艺制定中存在的问题并且改进了工艺。相对于物理试制,利用计算模拟技术能够有效地减少该锻件的研发成本和时间。     

基于遗传算法的圆的半径测量

提出一种计算圆的测量半径的新方法－－遗传算法，并对标准遗伟算法提出了一些改进。采用实数值编码，其计算结果的精确度非常高，理论上可以获得全局最优解。该算法简单明了，收敛速度快，在计算机上容易实现。     

Optimal Preform die Design through Controlling Deformation Uniformity in Metal Forging

A finite element based sensitivity analysis method for preform die shape design in metal forging is developed.The optimization goal is to obtain more uniform deformation within the final forging by controlling the deformation uniformity.The objective function expressed by the effective strain is constructed.The sensitivity equations of the objective function,elemental volume,elemental effective strain rate and the elemental strain rate with respect to the design variables are constituted.The preform die shapes of an H-shaped forging process in axisymmetric deformation are designed using this method.     

The Optimal Scheduling of a Reversing Strip Mill: Studies Using Multipopulation Genetic Algorithms and Differential Evolution

This article addresses a problem of minimizing the hot rolling time of an ingot, from a given initial thickness to a prescribed final one, subject to a number of system constraints. The idea is to determine the minimum possible odd number of passes, so that the ingot leaves in the same direction as it entered, which would ensure the necessary degree of reduction without violating the prescribed upper limits of the available torque and roll force. A maximum rolling velocity was also prescribed and additional restrictions were imposed on the rates of acceleration and deceleration inside the mill. The problem was solved by using a number of variants of genetic algorithms, including a multipopulation island model and differential evolution, besides the simple genetic algorithms. The results are compared with some earlier work based on a discrete dynamic programming technique, and a model based on an improved formulation is also presented.     

采用遗传算法直接获取模糊控制器的查询表

在实际应用中,模糊控制器常常被转化为一个查询表,以减小实时运行中的计算量。这样的查询表仍然能够保留原模糊控制器的性能特征,但另一方面这个查询表是如何获得的对控制器来说并不是本质的,控制器的性能只取决于查询表本身。这样,在模糊控制器的结构下采用其他方法直接生成这种查询表,或许能够比采用模糊逻辑的方法更为有效。本文给出了采用遗传算法直接搜索查询表的方法,以获取性能优良的控制器,给出了查询表式下二队控制     

Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

Data-driven models were constructed for the mechanical properties of multi-component Ni-based superalloys, based on systematically planned, limited experimental data using a number of evolutionary approaches. Novel alloy design was carried out by optimizing two conflicting requirements of maximizing tensile stress and time-to-rupture using a genetic algorithm-based multi-objective optimization method. The procedure resulted in a number of optimized alloys having superior properties. The results were corroborated by a rigorous thermodynamic analysis and the alloys found were further classified in terms of their expected levels of hardenabilty, creep, and corrosion resistances along with the two original objectives that were optimized. A number of hitherto unknown alloys with potential superior properties in terms of all the attributes ultimately emerged through these analyses. This work is focused on providing the experimentalists with linear correlations among the design variables and between the design variables and the desired properties, non-linear correlations (qualitative) between the design variables and the desired properties, and a quantitative measure of the effect of design variables on the desired properties. Pareto-optimized predictions obtained from various data-driven approaches were screened for thermodynamic equilibrium. The results were further classified for additional properties.     

Regulating Crown and Flatness During Hot Rolling: A Multiobjective Optimization Study Using Genetic Algorithms

A genetic algorithms-based multioptimization study has been carried out for the hot rolling practice in an integrated steel plant. The aim is to identify the parameter settings and rolling schedules that would result in the optimum values of crown and flatness--two major parameters related to the geometric tolerances in the rolled sheet. Two objective functions and some appropriate constraints have been formulated for this purpose, and two different evolutionary algorithms are tried out on them. The optimized results are presented in the forms of Pareto fronts and discussed in the context of the actual process.     

Identification of Factors Governing Mechanical Properties of TRIP-Aided Steel Using Genetic Algorithms and Neural Networks

Mechanical properties of transformation induced plasticity (TRIP)-aided multiphase steels are modeled by neural networks using two methods of reducing the network connectivity, viz. a pruning algorithm and a predator prey algorithm, to gain understanding on the impact of steel composition and treatment. The pruning algorithm gradually reduces the complexity of the lower layer of connections, removing less significant connections. In the predator prey algorithm, a genetic algorithm based multi-objective optimization technique evolves neural networks on a Pareto front, simultaneously minimizing training error and network size. The results show that the techniques find parsimonious models and, furthermore, extract useful knowledge from the data.     

Optimal Scheduling of Casting Sequence Using Genetic Algorithms

Scheduling a casting sequence involving a number of orders with different casting weights and satisfying due dates of is an important optimization problem often encountered in foundries. In this article, we attempt to solve this complex, multi-variable, and multi-constraint optimization problem by using different implementations of genetic algorithms (GAs). In comparison with a mixed-integer linear programming solver, GAs with problem-specific operators are found to provide faster (with a subquadratic computational time complexity) and more reliable solutions to very large (more than 1 million integer variables) casting sequence optimization problems. In addition to solving the particular problem, the study demonstrates how problem-specific information can be introduced in a GA for solving complex real-world problems.     

遗传算法在项目进度计划中的应用

在项目进度计划中,将工序的前后约束关系变换成一个关联矩阵,从可执行的工序集合中随机产生初始化种群,采用改进型的双点交叉算子,并提出了基于关系矩阵的邻位变异算子,避免了不可行个体的产生。文章给出一个3种资源约束的多项目进度计划实例以说明该算法的有效性。     

Parallel Optimization of Program Instructions Using Genetic Algorithms

This paper describes an efficient solution to parallelize software program instructions, regardless of the programming language in which they are written. We solve the problem of the optimal distribution of a set of instructions on available processors. We propose a genetic algorithm to parallelize computations, using evolution to search the solution space. The stages of our proposed genetic algorithm are: The choice of the initial population and its representation in chromosomes, the crossover, and the mutation operations customized to the problem being dealt with. In this paper, genetic algorithms are applied to the entire search space of the parallelization of the program instructions problem. This problem is NP-complete, so there are no polynomial algorithms that can scan the solution space and solve the problem. The genetic algorithm-based method is general and it is simple and efficient to implement because it can be scaled to a larger or smaller number of instructions that must be parallelized. The parallelization technique proposed in this paper was developed in the C# programming language, and our results confirm the effectiveness of our parallelization method. Experimental results obtained and presented for different working scenarios confirm the theoretical results, and they provide insight on how to improve the exploration of a search space that is too large to be searched exhaustively.     

用遗传算法计算设计多薄层雷达吸波材料的程序实现技术

本研究探索一种集成式多薄层吸波材料的优化设计系统的方法及程序实现技术.该设计系统采用传输线理论和跟踪计算方法来计算多层吸波材料的反射率,以遗传算法作为优化引擎.利用开放源代码数据库服务器软件MYSQL实现了材料电磁参数数据库.在开放源代码软件平台下,创建了多薄层RAMs计算设计和性能预报智能系统.对几种典型的实际材料,进行了多代遗传、进化等优化计算,给出了比较理想的优化设计结果,并对优化结果和材料智能预报系统功能进行了技术评价.     

A Paradigm for the Scheduling of a Continuous Walking Beam Reheat Furnace Using a Modified Genetic Algorithm

The paper discusses the application of a paradigm for creating scheduling systems for steel reheating furnaces. The proposed paradigm utilizes a modified version of a Genetic Algorithm (GA) to optimize such schedules via new ways of realizing the crossover and mutation operations. The work was conducted in collaboration with the Thrybergh Combination Mill owned by CORUS- Sheffield (UK). The outcome of this research work is a novel scheduling system which links together the scheduling and furnace controls and is also able to accept new and already established mill heuristics. The proposed methodology is 'flexible' as well as 'generic' as it can be augmented easily in order to suit other industrial set-ups.     

基于遗传算法的直线度误差的测量

提出了一种基于遗传算法的计算直线度误差的新方法。该方法满足最小条件原理并采用与最小包容区域法等效的理想包容参考直线计算直线度误差，其计算结果的精确度非常高，理论上可以获得全局最优解。这种算法简单明确，具有精度高、收敛速度快、易于计算机程序实现和推广应用等特点。     

Effects of Process Parameters on the Temperature Field in Ti-6Al-4V Alloy Blade Precision Forging Process

Blade precision forging is a high temperature and large plastic deformation process. Process parameters have a great effect on temperature distribution in billet, so in this paper, by taking a Ti-6Al-4V alloy blade with a tenon as an object, the influence of process parameters on the temperature distribution in precision forging process was investigated using 3D coupled thermo-mechanical FEM （finite element method） code developed by the authors. The results obtained illustrate that： （1） the gradient of temperature distribution increases with increasing the deformation degree; （2） with increasing the initial temperature of the billet, the zones of high temperature become larger, and the gradient of temperature distribution hardly has any increase; （3） friction factors have little effect on the distribution of temperature field; （4） with increasing upper die velocity, temperature of the billet increases while the temperature gradient in billet decreases. The results are helpful to the design and optimization of the process parameters in precision forging process of Ti-alloy blade.     

Application of Genetic Algorithms in Texture Analysis and Optimization

The genetic algorithms method (GAM) is a modern computer technique based on some ideas taken from biological evolution theory. The GAM is especially useful in a study of problems being not completely determined. They are, e.g., problems having a few but not very different solutions or problems without a strict (exact) solution. The last situation may occur if it is enough to find a good enough solution but not necessarily the best one. In GAM approach, it is not necessary to know a priori a general scheme of problem solution; however, it is important to have a procedure estimating the quality of a solution. This procedure is necessary to eliminate some solutions and to accept another ones. In the last years, GAM was applied with success in different areas of science (e.g., sociology, construction engineering, artificial intelligence and many others). The present authors have applied GAM in crystallographic texture analysis: the orientation distribution function (ODF) was calculated from a set of measured pole figures. The quality of obtained results was very good; however, the calculation time and memory space were relatively high. The main reason of this situation was the use of spherical harmonic function series for the ODF representation. An important simplification of the calculation scheme (and of calculation time and memory space) can be obtained if ODF is represented by a sum of a few Gauss-type functions. The quality of solutions in this new approach is still very correct. The above-mentioned improved GAM scheme was also used to find an optimal crystallographic texture that optimizes the elastic properties of material. By using this approach the Young modulus can be maximised or minimised along a given sample direction.