圆柱齿轮传动优化设计的遗传算法及Matlab实现

提出了在圆柱齿轮传动优化设计中,在传统的罚函数法的基础上应用遗传算法进行优化,并介绍了如何利用Matlab优化工具箱来求解.同时指出,遗传算法在齿轮传动优化设计中有较好的应用前景.     

基于遗传算法的工艺路线决策与优化

在已有的工艺路线决策优化研究的基础上,分析了工艺知识的特点,确定了基于特征的工艺知识表达方法,根据工艺知识间的约束关系,构建基于工艺约束的工艺路线决策空间,提高遗传算法的搜索能力。针对工艺路线决策的不确定性,建立了多目标优化函数,将遗传算法应用于工艺路线决策过程中。通过设计合理的基因编码规则、适应度函数、交叉、变异算法优化工艺路线。通过实例,介绍了利用遗传算法进行工艺路线决策和优化的过程。     

Sliding mode controller with sliding perturbation observer based on gain optimization using genetic algorithm

The Stewart platform manipulator is a closed-kinematics chain robot manipulator that is capable of providing high structural rigidity and positional accuracy. However, this is a complex and nonlinear system, so the control performance of the system is not so good. In this paper, a new robust motion control algorithm is proposed. The algorithm uses partial state feedback for a class of nonlinear systems with modeling uncertainties and external disturbances. The major contribution is the design of a robust observer for the state and the perturbation of the Stewart platform, which is combined with a variable structure controller (VSC). The combination of controller and observer provides the robust routine called sliding mode control with sliding perturbation observer (SMCSPO). The optimal gains of SMCSPO, which is determined by nominal eigenvalues, are easily obtained by genetic algorithm. The proposed fitness function that evaluates the gain optimization is to put sliding function. The control performance of the proposed algorithm is evaluated by the simulation and experiment to apply to the Stewart platform. The results showed high accuracy and good performance.     

A multiobjective genetic algorithm for scheduling a flexible manufacturing system

Though the designers of Flexible Manufacturing Systems (FMS) strive to ensure the maximum flexibility in the system, in practice, after the implementation of such systems the operational executives often find it hard to accommodate frequent variations in the part designs of incoming jobs. This difficulty can very well be overcome by scheduling the variety of incoming parts into the system efficiently. In this work an appropriate scheduling mechanism is designed to generate a nearer-to-optimum schedule using Genetic Algorithm (GA) with two different GA Coding Schemes. Two contradictory objectives of the system were achieved simultaneously by the scheduling mechanism. The results are compared with those obtained by different scheduling rules and conclusions are presented.     

基于遗传算法的LQ控制器的权值优化

线性二次型最优调节器及线性二次型高斯问题,通称LQ问题,是最优控制在民用工业中应用最广的算法。LQ问题的解,最终归结为求解Riccati方程,而方程解的有效性,很大程度上又依赖于两个权矩阵Q阵和R阵的选择,如何选择Q阵和R阵以保证实现控制目标最优一直为控制界所注。本文提出以遗传算法来达到实现全局寻优的目的,得到较满意的结果。     

基于遗传算法的汽车行驶平顺性优化仿真

为了探讨遗传算法应用于汽车行驶平顺性仿真的可行性,以1/4汽车两自由度振动模型为对象,建立了汽车行驶平顺性优化仿真模型,运用遗传算法对该优化仿真模型进行了求解.优化前后汽车行驶平顺性指标的对比结果表明,遗传算法用于汽车行驶平顺性优化仿真取得了较好的效果,可以进一步应用于基于高自由度汽车振动模型的汽车行驶平顺性仿真优化问题中.     

Product configuration optimization using a multiobjective genetic algorithm

Product configuration is one of the key technologies in the environment of mass customization. Traditional product configuration technology focuses on constraints-based or knowledge-based application, which makes it very difficult to optimize design of product configuration. In this paper, an approach based on multiobjective genetic algorithm is proposed to solve the problem. Firstly, a configuration-oriented product model is discussed. A multiobjective optimization problem of product configuration according to the model is described and its mathematical formulation is designed. Secondly, a multiobjective genetic algorithm is designed for finding near Pareto or Pareto optimal set for the problem. A matrix method used to check constraint is proposed, and the coding and decoding representation of the solution are designed, then a new genetic evaluation and select mechanism is proposed. Finally, performance comparison of the proposed genetic algorithm with three other genetic algorithms is made. The result shows that the proposed genetic algorithm outperforms the other genetic algorithms in this problem.     

基于改进自适应遗传算法求解机床制造企业立体仓库堆垛机路径优化问题

以机床制造业为背景,以存放机床零部件的自动化立体仓库为基础,重点分析自动化立体仓库堆垛机的路径分析.通过对遗传算法进行自适应改进,算出能够随时适应的遗传算子,克服了传统遗传算法的早熟收敛问题.通过运用序号法设定各货位在立体仓库中的位置,建立堆垛机拣选作业的数学模型,运用改进自适应遗传算法对初始路径进行改进,得出最优解,并运用Matlab遗传算法工具箱对此进行仿真,实验结果表明,此方法收敛速度快,可以获得全局最优解,其堆垛机路径规划更加快速和有效.     

Journal bearing design using multiobjective genetic algorithm and axiomatic design approaches

This paper describes the optimum design methodology for improving operating characteristics of fluid-film steadily loaded journal bearings. This methodology consists of (1) a simplified closed form solution to accelerate the computation, (2) finite difference mass conserving algorithm for accurate prediction of lubricant flow and power loss, (3) Pareto optimal concept to avoid subjective decision on priority of objective functions, (4) a genetic algorithm to deal with multimodal nature of hydrodynamic-bearing and develop a Pareto optimal front, (5) fitness sharing to maintain genetic diversity of the population used in genetic algorithm, and (6) axiomatic design to provide inside of objective functions and design variables. In the optimum design of journal bearings, the design variables such as radial clearance, length to diameter ratio, groove geometry, oil viscosity and supply pressure are used to simultaneously minimize oil flow and power loss. A step-by-step procedure, graphs and tables are presented to demonstrate the concept and effectiveness of suggested design methodology.     

PGA算法优化的航空装备预防性维修综合决策研究

考虑预防性维修次数和周期对航空装备故障率的影响,将役龄回退因子和故障率递增因子引入到故障率函数中,以有限时间内装备的维修费用最小和平均可靠度最大为优化目标,建立了一种变周期预防性维修综合决策模型。采用改进的Pareto遗传算法(Pareto Genetic Algorithm,PGA)对模型进行优化求解。以某型航空发动机为例对模型进行了验证,得到了10组Pareto最优集,确定了最佳的预防性维修次数及周期。仿真结果表明该模型在保证有效可靠度的基础上大大减少了维修费用。     

遗传算法在函数优化问题中的应用研究

系统地论述了遗传算法在函数优化问题中的应用，提出了基于遗传算法的函数优化问题的通用框架，通过实例的分析及计算，得出较为理想的结果。     

基于遗传算法的非线性控制系统参数优化方法

本文针对非线性控制系统参数优化问题,结合非线性控制系统理论、最优化理论及遗传算法,提出了一种新的仿真优化方法,该方法利用遗传算法来求解非线性控制系统参数优化问题,由于遗传算法是在解空间的多个区域内进行搜索,能以较大的概率跳出局部最优,因此可技巧以整体最优解。仿真结果表明,该方法是一种有效的非线性控制系统参数优化方法。     

遗传算法在机构优化设计中的应用

机构优化设计问题是机械优化设计的一个重要方面。本文尝试用遗传算法解决该类问题,获得了令人满意的结果,并且可以克服常用算法求解析造成的忆中部最优,为机构优化问题开创一条新的路径。     

基于免疫遗传算法的优化设计

遗传算法作为一种生物进化计算模型,在优化计算中得到了广泛应用,但其存在的缺陷,影响了其在一些问题求解中的正确性和有效性.将生物免疫算法与遗传算法相结合,提出了一种基于免疫遗传机理的优化计算模型,避免了遗传算法易出现早熟、搜索效率低及不能很好保持个体多样性等问题.并以免疫遗传算法为基础,研制了优化设计支持IGBODS(Immune & Genetic based Optical Design Support) 系统, IGBODS系统已在多个优化问题中得到应用,优化计算结果表明,免疫遗传算法在优化设计中,具有很大的优越性与良好的应用前景.     

基于遗传算法的电磁防振锤参数优化

为了优化电磁防振锤结构参数,对其结构建立了参数化模型;以此为基础,以电桥电阻平均功率最大为优化目标,以电桥平衡方程为主要约束条件,用遗传算法对电磁防振锤主要结构参数进行了优化设计。结果表明,与机械防振锤相比,电磁防振锤在风振频率为(6～40)Hz范围内防振效果好。     

Optimal fractional order PID design via Tabu Search based algorithm

This paper presents an optimization method based on the Tabu Search Algorithm (TSA) to design a Fractional-Order Proportional-Integral-Derivative (FOPID) controller. All parameter computations of the FOPID employ random initial conditions, using the proposed optimization method. Illustrative examples demonstrate the performance of the proposed FOPID controller design method.     

Initial design domain reset method for genetic algorithm with parallel processing

The Genetic Algorithm (GA), an optimization technique based on the theory of natural selection, has proven to be a relatively robust means of searching for global optimum. It converges to the global optimum point without auxiliary information such as differentiation of function. In the case of a complex problem, the GA involves a large population number and requires a lot of computing time. To improve the process, this research used parallel processing with several personal computers. Parallel process technique is classified into two methods according to subpopulation’s size and number. One is the fine-grained method (FGM), and the other is the coarse-grained method (CGM). This study selected the CGM as a parallel process technique because the load is equally divided among several computers. The given design domain should be reduced according to the degree of feasibility, because mechanical system problems have constraints. The reduced domain is used as an initial design domain. It is consistent with the feasible domain and the infeasible domain around feasible domain boundary. This parallel process used the Message Passing Interface library.     

基于SVM和遗传算法的新型直线电机结构参数优化

建立了一种新型圆筒直线电机的有限元模型,并验证了有限元模型的正确性。基于正交和随机方法安排了有限元仿真试验,获得了用于非线性电磁建模的样本空间。进一步采用支持向量机回归建模方法,建立了直线电机的非线性数学模型,而后采用遗传算法进行了直线电机结构参数多目标优化,得出最优结构参数组合。有限元仿真验证表明,基于支持向量机和遗传算法的优化方法用于研究该种新型直线电机电磁结构参数优化是可行的。     

Optimization of a composite laminated structure by network-based genetic algorithm

Genetic algorithm (GA), compared to the gradient-based optimization, has advantages of convergence to a global optimized solution. The genetic algorithm requires so many number of analyses that may cause high computational cost for genetic search. This paper proposes a personal computer network programming based on TCP/IP protocol and client-server model using socket, to improve processing speed of the genetic algorithm for optimization of composite laminated structures. By distributed processing for the generated population, improvement in processing speed has been obtained. Consequently, usage of network-based genetic algorithm with the faster network communication speed will be a very valuable tool for the discrete optimization of large scale and complex structures requiring high computational cost.     

Range expansion of cooperative mobile wireless network based on swarm intelligence optimization

We research maximum range expansion using a fixed number of mobile wireless nodes along a line which do distributed MIMO (Multiple Input Multiple Output) in diversity configuration, also known as cooperative transmission (CT). The emphasis is on optimal clusters and locations of these clusters to maximize the multi-hop reach along the line based on the swarm intelligence algorithms which are the Glowworm Swarm Optimization (GSO) and Improved Ant Colony Algorithm (IACA). We build the CT model which cannot be solved by conventional method, so we solve it by using GSO and IACA algorithms to get the optimal result. In GSO algorithm, every mobile wireless node is considered as a glowworm, and the intensity of signs is the intensity of luciferin. The direction of movement is determined as well as the direction of movement function under the constraint of outage probability in CT model. The contribution to the IACA is to modify the heuristic function and the pheromone update rule based on the optimization function to avoid local optimal result. Simulation experiments compared GSO, IACA with ACA and Exhaustive Attack Method (EAM), which proves GSO and IACA are effective, and the advantage of IACA is high accuracy and GSO is time saving.