基于自适应遗传算法的双环信号配时优化模型

为了有效利用交叉口时空资源,缓解城市交通拥堵,在双环相位方案的基础上,建立以交叉口平均延误为优化目标,以各相位绿灯时长为优化参数的双环信号配时优化模型,并采用自适应遗传算法对模型进行求解。选取4个十字交叉口,实地调查获得交叉口晚高峰流量数据,分别使用经典的Webster配时法和双环信号配时优化模型对交叉口信号配时方案进行优化。结果表明：与Webster配时法相比,双环信号配时优化模型优化结果更好,分别使4个交叉口平均延误减少11.36%、13.74%、3.72%和9.00%,能够有效改善交叉口运行状况;并且交叉口同相位内的两个流向的流量越不均衡,双环信号配时优化模型优化结果越好。     

Application of a Solidification Mathematical Model and a Genetic Algorithm in the Optimization of Strand Thermal Profile Along the Continuous Casting of Steel

This work presents an optimization method based on a genetic algorithm applied to continuous casting process. A simple genetic algorithm was developed, which works linked to a mathematical model permitting the determination of optimum values for the water flow rates in the secondary cooling zones. First, experimental data (industrial) were compared with simulated results obtained by the solidification mathematical model, to determine the metal/cooling heat transfer coefficients along the machine by the inverse heat conduction problem method. The industrial data concerning surface strand temperature were obtained by using infrared pyrometers along a continuous caster machine during casting of both SAE 1007 and 1025 steels. In a second step, these results were used by a numerical code based on a genetic algorithm for determining optimum settings of water flow rates in the different sprays zones, which are conducive to the best quality of the solidified strand. The simulations were carried out by analyzing the solidification process during continuous casting to attain metallurgical restrictions concerning the reheating of strand surface temperature and metallurgical length.     

Modeling of Total Decarburization of Spring Steel with Genetic Programming

Store Steel Ltd. is one of the biggest spring steel producers in Europe. Spring steel should have proper chemical composition and microstructure and should be without surface defects. Decarburization—that is, reduction of carbon content—also influences spring steel surface quality. During regular production, the data regarding rolled spring steel bars (width, reduction rate, chemical composition, and total decarburization) and the heating furnace for heating billets before rolling (heating temperature, time, and oxygen content) were monitored. On the basis of the monitored data, a mathematical model for the total decarburization depth was developed by genetic programming and linear regression. The average relative deviations from experimental data for the genetic programming developed and linear regression models are 18.186% and 22.999%, respectively. According to the developed models, the heating temperature was lowered, and, consequently, 24.29% lower total decarburization (t-test, p < 0.05) and also 20.65% lower heating furnace natural gas consumption was achieved (t-test, p < 0.05).     

Applications of Genetic Algorithm in Polymer Science and Engineering

In the last several years, genetic algorithm (GA) has gained wide acceptance as a robust optimization algorithm in almost all areas of science and engineering. Polymer science and engineering is no exception. Researchers in this field have devoted considerable attention to the optimization of polymer productionusing objective functions and constraints that lead to products having desired material properties. Multiple-objective functions have been optimized simultaneously. An example is the minimization of the reaction time in a reactor (lower costs) while simultaneously minimizing the concentration of side products (that affect the properties of the product adversely). Several end-point constraints (equality or inequality) may also be present, as, e.g., obtaining polymer of a desired molecular weight. Again, this requirement stems from producing polymer having desired strength. Solving such problems usually result in Pareto sets. A variety of adaptations of GA have been developed to obtain optimal solutions for such complex problems. These adaptations can be used to advantage in other fields too. The applications of GA in areas of polymer science and engineering other than polymerization systems are few and far between, but this field is now maturing, and it is hoped that the future will see several newer applications.     

Ecologically Inspired Water Network Optimization of Steel Manufacture Using Constructed Wetlands as a Wastewater Treatment Process

Traditional optimization models often lack a systems-level perspective at conception, which limits their effectiveness. Expanding system boundaries allow scientists and engineers to model complex interactions more accurately, leading to higher efficiency and profitability in industrial systems. Ecological systems have evolved for billions of years under conditions of material and energy shortage, and ecologists have defined analysis tools and metrics for identifying important principles. These principles may provide the framework to circumvent the limitations of traditional optimization techniques. More specifically, by recruiting functional roles that are often found in ecological systems, but are absent in industrial systems, industries can better mimic how natural systems organize themselves. The objective of this analysis is to traditionally optimize a manufacturing process by comparing the model with ecological and resource-based performance metrics in order to redesign the model with the addition of important functional roles that are found throughout nature. Industry partners provided data for this analysis, which involved building a water network for an existing steel manufacturing facility in China. The results of the traditional optimization model indicate a 23%, 29%, and 20% decline in freshwater consumption, wastewater discharge, and total annual cost, respectively. However, our ecologically inspired optimization model provides an additional 21% and 25% decline in freshwater consumption and total annual cost, respectively. Furthermore, no water is discharged. These results suggest that this unconventional approach to optimization could provide an effective technique not used by existing algorithms to solve the challenging problem of pursuing more sustainable industrial systems.     

Determination of Anisotropic Yield Coefficients by a Data-Driven Multiobjective Evolutionary and Genetic Algorithm

The texture induced anisotropy of yield strength in cold rolled sheet metals is modeled using anisotropic yield criteria. The classical and other optimization methods used so far to determine the yield coefficients are limited by fixed set of experimental data, initial guess values, and pre-determined weight factors. A robust multiobjective optimization based on evolutionary algorithm proposed in this paper minimizes the error in yield stress and plastic strain ratio simultaneously and thereby overcomes the limitations in the approaches used before. The new approach is tested using Hill48 and Barlat89 yield criteria for five different materials from literature. The new approach is observed to improve the prediction capability of yield coefficients when compared to earlier approaches. The Pareto frontier obtained in the new approach can serve as a comparative tool to evaluate the accuracy of different yield criteria.     

Genetic Algorithm Optimization of a Filament Winding Process Modeled in WITNESS

With the advent of smaller, less expensive, and generally more effective computers, simulation models have become increasingly popular tools for solving engineering problems. More and more, engineers are turning to simulation environments to achieve increased system performance at a reduced cost. One such environment found to be very effective is WITNESS, a modeling program developed by AT&T and Istel. This article describes an effort to link a genetic algorithm with WITNESS to optimize a model of a manufacturing process called filament winding. Results show the genetic algorithm to be an effective, optimization tool for use with WITNESS models.     

遗传算法在水力管网优化中的应用

论述了遗传算法解决管网水力问题的主要步骤和优点,分析了遗传算法的结构和控制参数的选取。     

Comparison Between Genetic Algorithm and Genetic Programming Approach for Modeling the Stress Distribution

This article compares genetic algorithm (GA) and genetic programming (GP) for system modeling in metal forming. As an example, the radial stress distribution in a cold-formed specimen (steel X6Cr13) was predicted by GA and GP. First, cylindrical workpieces were forward extruded and analyzed by the visioplasticity method. After each extrusion, the values of independent variables (radial position of measured stress node, axial position of measured stress node, and coefficient of friction) were collected. These variables influence the value of the dependent variable, radial stress. On the basis of training data, different prediction models for radial stress distribution were developed independently by GA and GP. The obtained models were tested with the testing data. The research has shown that both approaches are suitable for system modeling. However, if the relations between input and output variables are complex, the models developed by the GP approach are much more accurate.     

Simulation and Optimization of Wiped-Film Poly-Ethylene Terephthalate (PET) Reactor Using Multiobjective Differential Evolution (MODE)

The multiobjective differential evolution (MODE), which is an extension of the Differential Evolution (DE), is applied to solve the multiobjective optimization problem (MOOP) of wet film Poly-Ethylene Terephthalate (PET) reactor considering minimization of acid end group and vinyl end group as the main objectives. The objective function is modified to solve five different possible cases. The results show that a Pareto set (a set of equally good solutions) is obtained for the cases when two of the parameters (residence time of the polymeric reaction mass, θ, and the speed of the wiped-film agitator, N) are considered as decision variables, unlike a unique solution obtained using Nondominated Sorting Genetic Algorithm (NSGA). The Pareto optimal front provides wide-ranging optimal sets of operating conditions. And an appropriate set of operating conditions can be selected based on the requirements of the user.     

Modeling of Hot Yield Stress Curves for Carbon Silicon Steel by Genetic Programming

The shape of hot yield stress curves (surfaces) is complicated because of the microstructural changes and phase transformations that occur during the hot working process. Such dependencies are difficult to describe mathematically. In this article, modeling of hot yield stress curves for carbon silicon steel (electric steel, 1.87%Si, 0.05-0.08%C) by genetic programming was carried out. Strain rate, strain, and temperature were the parameters monitored experimentally. On the basis of the experimental data, a model for prediction of hot yield stress curves was obtained by genetic programming. The study showed that genetic programming is a powerful prediction tool.     

Optimization of Land-use Based on the Theory of Cellular Automata and Value of Ecosystem Services

The main objective of the study was to confirm the location and configuration of “Habitat Conservation Area” in Dongguan City. The land utilization condition in the target city was simulated using Arc GIS and Geo SOS software basing on multi-criteria decision model of Cellular Automata (CA). Both the simulation result and accuracy satisfied well the basic requirements. In addition to multi-criteria decision model, space optimization technique was used as well in simulation experiments.     

Mathematical Modeling and Optimization of Two-Layer Sintering Process for Sinter Quality and Fuel Efficiency Using Genetic Algorithm

Two-layer sintering by charging a green sinter mix with normal coke rate in the upper layer and reduced coke rate in the lower layer can substantially reduce the coke rate and improve the sinter quality by producing more uniform thermal profile throughout the bed height. The two-layer sintering process has been analyzed by numerical simulation using a detailed CFD-based model, considering all the important phenomena (i.e., gas-solid reaction, melting and solidification, flow through porous bed, heat, and mass transfer etc.). A genetic algorithm optimization technique is then applied to evaluate the optimum coke rate in the two layers of the bed to produce the ideal thermal profile and melting fraction in the sinter bed for optimum sinter quality. By this optimization method a high-quality sinter with minimum return fines can be achieved along with reduced coke rate. Application of genetic algorithm for this type of process optimization has several advantages over traditional optimization techniques, because it can identify the global optimum condition and perform multiobjective optimization very easily for a complex industrial process such as iron ore sintering.     

基于遗传算法的连续型设备布置优化

针对企业在生产过程中遇到的连续型设备布置问题,分析设备面积不等、形状固定及总空间限制等因素,提出分行布置的方案和数学模型来最小化物料搬运成本,用遗传算法对方案进行设计,并进行实证研究.     

Optimization of Johnson-Cook Constitutive Model for Lead-free Solder Using Genetic Algorithm and Finite Element Simulations

To ensure the reliability of microelectronics packages, the high strain rate deformation behavior of the solder joints must be properly understood. Accordingly, the present study proposes a hybrid experimental / numerical method for determining the optimal constants of the Johnson-Cook (J-C) constitutive model for 96.5Sn-3Ag-0.5Cu (SAC305) solder alloy. In the proposed approach, FEM simulations based on the J-C model are performed to describe the load-time response of an SAC305 ball solder joint under an impact velocity of 0.5 m/s. The optimal values of the constitutive model are then determined using an iterative Genetic Algorithm approach based on a comparison of the simulated load-time response and the experimental load-time response. The optimality of the optimized constants is demonstrated by comparing the experimental and simulation results for the load-time curves under impact speeds of 0.3 ∼ 1.0 m/s. It is shown that a good agreement exists between the two sets of results for all values of the impact speed. In other words, the results confirm the validity of the proposed hybrid approach as a means of evaluating the high strain-rate response of lead-free solder.     

基于非线性遗传算法的DTS传感模型的参数辨识与优化

基于光纤拉曼散射效应和Monte-Carlo法,建立了一种DTS传感模型。采用非线性遗传算法对该模型的参数进行了辨识,分析了种群规模、进化代数、交叉率、变异率等参数选值对算法运行结果的影响,并选取了最佳参数组合。搭建了分布式光纤温度传感系统实验平台,并运用该DTS传感模型对分布式光纤温度传感系统进行了仿真预测。实验及仿真结果表明:该传感模型在空间分辨力为1 m时,预测误差在0.24%以内;该分布式光纤温度传感系统的测温误差不大于0.40 ℃。     

Optimization of Construction of Tire Reinforcement by Genetic Algorithm

A new tire design procedure capable of determining the optimum tire construction was developed by combining a finite element method approach with mathematical programming and a genetic algorithm (GA). Both procedures successfully generated optimized belt structures. The design variables in the mathematical programming were belt angle and belt width. Using the merits of a GA which enabled the use of discrete variables, the design variables in the GA were not only the topology of the belt and belt angle but also the belt material. Furthermore, a discrete objective function such as the number of parts could be optimized in the GA. The optimized structure obtained by the GA was verified to increase the cornering stiffness more than 15 percent as compared with the control structure in an indoor drum test.     

Linear Programming Based Genetic Algorithm for the Unequal Area Facility Layout Problem

The facility layout problem (FLP) is generally defined as locating a set of departments in a facility with a given dimension. In this paper, a hybrid genetic algorithm (GA)/linear programming (LP) approach is proposed to solve the FLP on the continuous plane with unequal area departments. This version of the FLP is very difficult to solve optimally due to the large number of binary decision variables in mixed integer programming (MIP) models as well as the lack of tight lower bounds. In this paper, a new encoding scheme, called the location/shape representation, is developed to represent layouts in a GA. This encoding scheme represents relative department positions in the facility based on the centroids and orientations of departments. Once relative department positions are set by the GA, actual department locations and shapes are determined by solving an LP problem. Finally, the output of the LP solution is incorporated into the encoding scheme of the GA. Numerical results are provided for test problems with varying sizes and department shape constraints. The proposed approach is able to either improve on or find the previously best known solutions of several test problems.     

基于遗传算法的混合动力汽车动力总成悬置系统的优化设计研究

为了提高并联式混合动力汽车动力总成五点悬置系统的隔振性能,建立了动力总成五点悬置的动力学模型,以动力总成六自由度能量解耦与固有频率的合理分配为优化目标,五个悬置点的各向刚度为设计变量,采用遗传算法对悬置系统进行优化。应用上述方法对某并联式柴电混合动力汽车悬置系统进行了优化,动力学仿真与实车试验结果表明,悬置优化后消除了整车怠速工况时方向盘抖动,验证了所提方法的合理性。同时,遗传算法克服了序列二次型规划算法（SQP）易收敛于局部最优解的缺点,得到的悬置系统解耦性能优良,优化结果稳定可靠。     

内蒙古西部超低能耗草原民居供暖模式与围护结构优化

为了解决内蒙古西部草原民居围护结构构造单一,供暖方式落后,采暖效率低、室内热环境质量差并造成石化能源的大量浪费污染环境这一问题,研究以超低能耗为目标,充分利用当地丰富的太阳能与风能资源,构建风力发电系统、被动式阳光间及保温窗帘系统、太阳能热水循环系统组成的新型供暖系统,在此基础上利用正交试验对影响围护结构的阳光间进深、窗框材质、南向窗墙比、玻璃材质、保温层厚度、保温层材质、墙体材质7个主要因素以能耗为指标进行分析,选出最优组合方案。结果表明:最优组合方案与原始方案相比,能耗每年减少12 674.79 Kw·h,能耗降低率为77.99%,季室内平均温度提高了8.77℃,PMV值提高了1.86,满足冬季室内人员采暖与舒适度的要求。