基于MATLAB的斜齿轮传动多目标优化设计

本文介绍了齿轮传动的多目标设计方法,以斜齿轮体积和传动平稳可靠性为目标函数,建立了斜齿圆柱齿轮传动的多目标优化设计数学模型.并且结合实例利用科学计算软件MATLAB的优化工具箱求解多目标优化问题.目前,对于斜齿轮优化设计的研究主要集中在减小体积方面,一般不考虑传动平稳性.本文对减小体积和提高传动平稳可靠性两个目标进行了联合优化,这对于齿轮传动设计具有理论指导意义.     

基于MATLAB的斜齿轮传动多目标可靠性优化设计

介绍了齿轮传动的多目标设计方法,以斜齿轮体积和传动平稳可靠性为目标函数,在保证齿轮强度可靠性的前提下,建立了斜齿圆柱齿轮传动的多目标优化设计数学模型.并且结合实例利用科学计算软件MATLAB的优化工具箱求解优化问题.目前,对于斜齿轮优化设计的研究主要集中在减小体积方面,一般不考虑传动平稳性.对减小体积和提高传动平稳可靠性两个目标进行了联合优化,这对于齿轮传动设计具有理论指导意义.     

基于可靠性的斜齿轮传动多目标优化

在可靠性的基础上建立斜齿轮传动的多目标优化教学模型,结合实例,对减小体积和保证传动平稳的两个目标联合进行优化,并用MATLAB优化工具箱进行了求解,优化结果令人满意,这对于齿轮传动的设计具有很大的参考价值.     

基于LINGO的斜齿轮最优化设计

以斜齿轮体积和传动平稳可靠性为目标函数,建立了斜齿圆柱齿轮传动的多目标优化设计数学模型。将LINGO9.0优化算法应用于机械工程的优化设计中,提出了LINGO算法的优化原理及数学模型的建立,给出求解的方法。对减小体积和提高传动平稳可靠性两个目标进行了联合优化,在满足强度、传动平稳性等方面寻求最优化齿轮配置。     

基于全膜弹流理论的交错轴斜齿轮传动多目标优化

针对交错轴斜齿轮传动传统设计中的不足之处，阐述了基于全膜弹流理论的交错轴斜齿轮传动优化设计的目的和意义。选择螺旋角β和齿数比u作为设计变量，油膜厚度和磨损量作为目标函数，啮合效率作为约束条件，合理运用多目标函数优化方法，得出不同工况下以及不同设计要求下的优化结果。     

斜齿圆柱齿轮传动的齿轮箱多目标优化设计

以提高承载能力、满足等强度设计、并减小齿轮传动的中心距为目标函数,建立了二级斜齿圆柱齿轮传动的多目标优化设计数学模型,采用NSGA-Ⅱ算法进行优化求解,对二级斜齿圆柱齿轮进行多目标优化设计,得到了parteto最优解,结果显示其强度和平稳可靠性有所提高,且能够满足等强度设计的要求,同时使中心距有一定的减小。     

高速重载齿轮传动的多目标优化设计

讨论了重载齿轮设计中的各种影响因素;分析了斜齿轮和人字齿轮的优缺点,在此基础上将重载齿轮确定为弧齿线圆柱齿轮,并对其建立了以重合度最大、体积和相对滑动率最小为目标的多目标优化数学模型;给出了重载齿轮传动优化设计约束条件,运用多目标优化的乘除法获得了最优解.采用MATLAB软件实现了实例的优化设计目的,提高了机械设计效率和精度.     

基于Romax的斜齿圆柱齿轮减速器参数优化

以某斜齿圆柱齿轮减速器为研究对象,利用Romax Designer建立分析模型进行参数优化设计,旨在提高减速器的传动平稳性以及使用寿命。结合斜齿轮参数优化理论,以齿顶滚滑比为优化目标进行宏观参数优化、以传递误差和齿面载荷分布为优化目标进行微观参数优化,综合使用宏观参数优化和微观参数优化对减速器第一级传动副进行优化设计,改善了减速器齿轮传动中存在的问题。通过分析对比优化前后的齿轮传动性能,优化后齿轮减速器的传动性能和使用寿命都得到了有效提升。     

基于MATLAB及惩罚函数法的斜齿轮传动优化设计

斜齿轮传动在机械行业中应用广泛,基于机械优化设计理论,以MATLAB为平台,根据斜齿轮的设计要求和特点,运用内点惩罚函数法建立了斜齿轮副优化设计的数学模型,以该对齿轮的体积之和为目标函数进行优化,并利用MATLAB编写了相关优化程序。在同一设计要求下,与传统的斜齿轮设计方法对比,优化设计后的齿轮质量更轻,体积更小,更符合实际工况的需求。     

高速重载齿轮传动的多目标优化设计

讨论了重载齿轮设计中的各种影响因素；分析了斜齿轮和人字齿轮的优缺点，在此基础上将重载齿轮确定为弧齿线圆柱齿轮，并对其建立了以重合度最大、体积和相对滑动率最小为目标的多目标优化数学模型；给出了重载齿轮传动优化设计约束条件，运用多目标优化的乘除法获得了最优解。采用MATLAB软件实现了实例的优化设计目的，提高了机械设计效率和精度。     

基于多目标粒子群算法的可靠性稳健优化设计

采用随机撮动法和粒子群算法对车辆零部件进行可靠性稳健优化设计，利用模糊的多目标粒子群优化算法求出所有满足约束性条件的pareto解集，结合实际情况依据pareto解集确定零部件的规格。实验证明，该方法能迅速有效地获得可靠性稳健设计的信息。     

Pareto archive particle swarm optimization for multi-objective fuzzy job shop scheduling problems

This paper addresses multi-objective job shop scheduling problems with fuzzy processing time and due-date in such a way to provide the decision-maker with a group of Pareto optimal solutions. A new priority rule-based representation method is proposed and the problems are converted into continuous optimization ones to handle the problems by using particle swarm optimization. The conversion is implemented by constructing the corresponding relationship between real vector and the chromosome obtained with the new representation method. Pareto archive particle swarm optimization is proposed, in which the global best position selection is combined with the crowding measure-based archive maintenance, and the inclusion of mutation into the proposed algorithm is considered. The proposed algorithm is applied to eight benchmark problems for the following objectives: the minimum agreement index, the maximum fuzzy completion time and the mean fuzzy completion time. Computational results demonstrate that the proposal algorithm has a promising advantage in fuzzy job shop scheduling.     

车辆前轴的多目标可靠性稳健优化设计

应用车辆零部件可靠性稳健优化设计的理论方法，对车辆前轴进行了可靠性稳健优化设计。通过模糊多目标粒子群算法求出所有满足约束性条件的pareto解集，结合实际情况，依据pareto解集确定零部件的设计规格。实验证明该方法能迅速有效地获得可靠性稳健设计的信息。     

Multiobjective optimization for force and moment balance of a four-bar linkage using evolutionary algorithms

In this study, force and moment balance of a planar four-bar linkage is implemented using evolutionary algorithms. In the current problem, the concepts of inertia counterweights and physical pendulum are utilized to complete balance of all mass effects, independent of input angular velocity. A proposed multiobjective particle swarm optimization, and non-dominated sorting genetic algorithm II are applied to minimize two objective functions subject to some design constraints. The applied algorithms produced a set of feasible solutions called pareto optimal solutions for the design problem. Finally, a fuzzy decision maker is utilized to select the best solution among the obtained pareto solutions. The results show that optimal solutions minimize the weights of applied counterweights and eliminate both shaking forces and moments transmitted to the ground, simultaneously.     

基于粒子群算法的后桥可靠性稳健优化设计

为提高车辆零部件的安全性和稳健性,应用可靠性稳健优化设计理论和多目标决策方法,将车辆后桥的可靠性稳健优化设计转化为多目标优化问题.通过模糊多目标粒子群算法求出所有满足约束性条件的pareto解集,结合实际情况,依据pareto解集确定零部件的设计规格.实验表明,所提方法能迅速有效地获得可靠性稳健设计的信息.     

A particle swarm optimization for multi-objective flowshop scheduling

The academic approach of single-objective flowshop scheduling has been extended to multiple objectives to meet the requirements of realistic manufacturing systems. Many algorithms have been developed to search for optimal or near-optimal solutions due to the computational cost of determining exact solutions. This paper provides a particle swarm optimization-based multi-objective algorithm for flowshop scheduling. The proposed evolutionary algorithm searches the Pareto optimal solution for objectives by considering the makespan, mean flow time, and machine idle time. The algorithm was tested on benchmark problems to evaluate its performance. The results show that the modified particle swarm optimization algorithm performed better in terms of searching quality and efficiency than other traditional heuristics.     

A hybridized approach to optimal tolerance synthesis of clutch assembly

Specifying proper tolerances for manufactured goods results in greater savings and improved performance, which may ultimately determine whether a product succeeds or fails in the marketplace. In the past, tolerance specification has been more an art than a science, and is largely dependent upon experiences. A more scientific and reliable approach is presented in this paper. A hybrid of Nelder-Mead simplex method and particle swarm optimization (NM-PSO) is introduced for the design of tolerance of the machine elements of an overrunning clutch assembly. The objective is to obtain tolerances of the individual components so that the cost of manufacturing and quality loss is minimized. Experimental results demonstrate that hybrid NM-PSO is extremely effective and efficient in locating best-practice optimal solutions compared to geometric programming (GP), genetic algorithm (GA), and particle swarm optimization (PSO) methods.     

基于粒子群算法的电网检修计划编制

在电网检修计划编制的基本原则和工作流程下,根据粒子群基本算法原理对电网检修计划编制进行数学建模。考虑检修时间作为自变量矢量,考虑期望缺供电量和检修成本作为其目标函数,考虑检修时间、检修资源和安全性等多个因素作为约束。结合粒子群算法原理和多目标优化理论,全局搜索非支配解集,形成帕累托前沿。最后依据管理者不同的偏好,通过加权计算的方式量化评估各优化目标,从而遴选出最优解,也即最符合决策人员预期的检修计划。通过与非劣排序多目标遗传算法和多目标粒子群算法进行对比,证明本文算法具有较高的实用性,提升了电网运行维护的自动化水平。     

一种基于微粒群优化算法的冗余机械手轨迹规划法

针对冗余机械手应用遗传算法(或免疫遗传算法)的轨迹规划所存在的不足，提出了基于微粒群优化算法的冗余机械手轨迹规划方法。利用此算法对寻优过程进行仿真研究，所得结果令人满意，与遗传算法相比，微粒群算法简单、容易，且收敛速度快。此方法能够有效地进行机械手轨迹规划，从而能够为充分发挥冗余度的作用为改善机械手运动提供新的思路。     

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions.