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.     

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。     

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。     

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.     

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.     

Optimal Antibody Purification Strategies Using Data-Driven Models

This work addresses the multiscale optimization of the purification processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational flow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, flow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody purification process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models.     

Optimal Legal Firing Sequence of Petri Nets Using Linear Programming

Petri nets (PNs) are a reliable graphical and mathematical modeling tool for the formal modeling and validation of systems (W. Reisig, A Primer in Petri Net Design, Springer-Verlag: Berlin, Heidelberg, 1992). Applications of PNs include discrete event dynamic systems (DEDS) that are recognized as being concurrent, asynchronous, distributed, parallel, and/or nondeterministic. It is also a powerful formal method for the analysis of concurrent, embedded, and distributed finite state systems (K. Varpaaniemi, Series A: Research Reports, No. 26, Helsinki University of Technology, Digital Systems Laboratory, Oct. 1993). The reachability analysis of PNs is strategically significant as it captures the dynamic behavior of the system as well as providing efficient verification of the correctness of the model. Few linear programming (LP)-based methods can be found that address the reachability problem, and some of these are suitable for optimal control problems. However, due to an inherent state explosion they are difficult to implement; other methods run easily into deadlock as they lack appropriate mechanisms to avoid the firing of critical transitions (T. Matsumoto and A. Tarek, in Proceedings of the 35th IEEE Conference on Decision and Control, Kobe, Japan, 1996-12, pp. 4459–4468). In this paper an improved and easy to implement method is proposed that combines the Optimality Principle and Linear Programming (OP + LP) techniques to find an Optimal Legal Firing Sequence (OLFS) in PNs. This method can be applied to ordinary PNs with self-loops, avoids deadlocks, and can also be used for general PNs having cycles.     

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。     

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     

Eliminating Forging Defects Using Genetic Algorithms

In this article, an optimization method for metal forging process designs using finite element-based simulation is presented. Using as entry parameters the specifications of the final product the so-called inverse techniques developed for optimization problems allows the calculation of the optimal solution, the design parameters that produce the required product. An evolutionary genetic algorithm is proposed to calculate optimal shape geometry and temperature. An example demonstrating the efficiency of the developed method is presented considering a two-stage hot forging process. It considers optimization of the process parameters to reduce the difference between the realized and the prescribed final forged shape under minimal energy consumption, restricting the maximum temperature.     

Optimal Route Network Design for Transit Systems Using Genetic Algorithms

The paper proposes a technique for the development of "optimal" transit route networks (for example, a bus route network) given the information on link travel times and transit demand. The proposed method, unlike previous techniques, primarily uses optimization tools for the development of the transit route network--the reliance on heuristics is minimal. In the proposed method, genetic algorithms, an evolutionary optimization technique, is used to develop the "optimal" set of routes. Results show that the proposed procedure performs better than the existing techniques.     

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.     

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.     

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.     

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.     

一类资源负荷均衡问题的优化调度算法

针对一类n个独立任务在m个不完全同等的处理机上处理，使处理机的最大负荷为最小的非抢先调度问题，提出了一种启发式算法－－最小平衡算法，并分析了它的时间复杂性，在此基础上，又将最小平衡算法和遗传算法结合起来，提出了基于遗传的最小平衡算法，并用实例证实了该算法的有效性。     

基于免疫遗传计算的零件多目标优化

由生物引发的信息处理系统可分为:人工神经网络、进化计算和人工免疫系统(AIS)。其中神经网络和进化计算已被广泛用于各领域,而AIS则由于其复杂性较少应用。笔者将免疫算法与遗传计算结合,研制了一个基于免疫遗传计算的优化设计系统(Immune ＆ Genetic Algorithm based Design SupportSystem-IGBODS)。IGBODS用于零件的优化设计,避免了遗传算法搜索效率低,过早收敛和不能很好保持个体的多样性等问题,具有很大的优越性。     

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。