基于组合优化方法的平面叶栅优化设计

采用奇点分布法与贝塞尔(Bezier)曲线参数化方法相结合的平面叶栅设计方法进行叶栅的初步设计与参数化表达。该方法在完成设计的同时为基于现代优化算法的叶栅粘性流动最优化设计提供设计变量,以实现叶栅翼型的变形控制。然后结合N-S方程流场数值模拟,采用多目标遗传算法(NCGA)和序列二次规划法(NLPQL)组合的优化算法,通过调节叶栅翼型的形状控制参数对叶栅的总压损失和空化性能进行了优化。结果表明,优化效果良好。     

考虑运输代价的制造工作流调度算法

在云制造环境下,因制造服务资源所在地域的差异性,多目标制造工作流调度不仅考虑制造服务所需时间、费用,还需考虑产品运输所需时间、费用,原有工作流调度算法无法有效优化运输代价.针对此问题,结合遗传算法全局搜索能力强与粒子群算法收敛速度快的特点,提出多目标混合遗传粒子群(MOGA - PSO)算法.仿真结果表明混合算法能够有效降低运输代价,使得工作流调度得到进一步优化,可适用于云制造环境.     

Scenario-Based Multi-Objective Genetic Algorithm Optimization of Closed Ball-Milling Circuit of Esfordi Phosphate Plant

The main goal of this article is to demonstrate an approach based on integration of process simulation and Multi-Objective Genetic Algorithm (MOGA) concepts to solve a real grinding circuit optimization problem by finding the best operating condition under which process objectives can be achieved. Esfordi phosphate plant is located near city of Bafgh at Yazd province of Iran and produces 5 Mt of phosphate annually. The fine particles (nearly ?20 µm) in hydrocyclone underflow which contain a high grade of iron are subjected to over grinding. In addition to electrical energy loss, this causes problems in the downstream process, i.e., flotation stage. The main goals of this study were to solve this problem by adjusting operating condition so that (a) hydrocyclone overflow particle size can be increased from 94.2 µm to 100 µm and (b) increase hydrocyclone underflow particle size from 205 to 500 µm. The second process objective will decrease fine particles in hydrocyclone underflow stream. First, plant sampling campaigns were carried out to calibrate ball mill and hydrocyclone models to be used for performing simulation trials. Then, full circuit simulations were done and optimized by MOGA search process to find the best operating condition that produces hydrocyclone overflow and underflow streams with predefined particle sizes simultaneously. The results indicate that there are various solutions that can be recommended for plant testing and performance improvements. The results of plant implementation of one solution for scenario No. 4 showed improved circuit performance and also validated simulator predictions.     

基于MOGA/SOS的互连缓冲及线型优化算法

将互连延时、信号响应波形、布线面积作为互连优化的3个目标函数,把推导出的串扰下边界作为优化参量的限制条件,采用分布式RLC模型作为互连系统的近似解析模型,提出了一种基于单目标排序非支配集构造算法的多目标遗传算法,用于解决互连优化中的缓冲及线型优化问题.算法所得解为满足串扰限制条件且对信号延时、信号波形以及布线面积进行优化的折中解.测试结果表明所提算法对互连优化问题规模的适应性强,所得解的优化结果明显优于基于Elmore模型的优化结果,布线面积减少了30%,信号延时与串扰性能分别提高了25%和25.73%.     

短应力线轧机端盖分析与优化设计

对短应力线轧机轴承座外侧静迷宫端盖的结构进行优化分析,以减轻端盖的质量、提高端盖的强度与刚度。以减少静迷宫端盖质量和最大应力应变作为优化目标,使用Creo建立端盖的参数化模型,通过ANSYS Workbench进行静力学分析,构建响应曲面模型并进行敏感度分析,最后通过MOGA算法对端盖设计参数进行最优求解。优化后与初始值相比,端盖质量减少了16.58%,等效应力降低了22.10%,等效弹性应变下降了20.39%。优化后实现了端盖的轻量化,提高了设计强度和刚度,为以后短应力线轧机的设计优化提供了参考。     

Use of multiple objective evolutionary algorithms in optimizing surveillance requirements

This paper presents the development and application of a double-loop Multiple Objective Evolutionary Algorithm that uses a Multiple Objective Genetic Algorithm to perform the simultaneous optimization of periodic Test Intervals (TI) and Test Planning (TP). It takes into account the time-dependent effect of TP performed on stand-by safety-related equipment. TI and TP are part of the Surveillance Requirements within Technical Specifications at Nuclear Power Plants. It addresses the problem of multi-objective optimization in the space of dependable variables, i.e. TI and TP, using a novel flexible structure of the optimization algorithm. Lessons learnt from the cases of application of the methodology to optimize TI and TP for the High-Pressure Injection System are given. The results show that the double-loop Multiple Objective Evolutionary Algorithm is able to find the Pareto set of solutions that represents a surface of non-dominated solutions that satisfy all the constraints imposed on the objective functions and decision variables. Decision makers can adopt then the best solution found depending on their particular preference, e.g. minimum cost, minimum unavailability.     

基于MOGA和SVM的发酵过程建模

针对微生物发酵过程,使用多目标遗传算法（multi-objective genetic algorithm, MOGA）确定最优参数．MOGA和SVM回归相结合形成一种新的建模方法,该方法利用现场生产数据建立了青霉素效价预估模型．仿真结果表明此方法具有很强的拟合和泛化能力．MOGA方法的有效性也得到了验证,它也能够自动选择最优参数．     

Multi-objective optimization of linear multi-state multiple sliding window system

This paper considers the optimal element sequencing in a linear multi-state multiple sliding window system that consists of n linearly ordered multi-state elements. Each multi-state element can have different states: from complete failure up to perfect functioning. A performance rate is associated with each state. The failure of type i in the system occurs if for any i (1≤i≤I) the cumulative performance of any r_i consecutive elements is lower than w_i. The element sequence strongly affects the probability of any type of system failure. The sequence that minimizes the probability of certain type of failure can provide high probability of other types of failures. Therefore the optimization problem for the multiple sliding window system is essentially multi-objective. The paper formulates and solves the multi-objective optimization problem for the multiple sliding window systems. A multi-objective Genetic Algorithm is used as the optimization engine. Illustrative examples are presented.     

基于稳健性的肘杆传动机构的优化设计

采用复数矢量法建立了肘杆传动机构的运动方程,运用稳健设计原理,以肘杆传动机构的几何参数为设计变量,以满足曲柄摇杆机构及滑块位移条件为约束,以滑块冲压阶段的最大速度以及速度波动为目标函数,同时考虑杆件制造误差的影响,采用具有正态分布参数的蒙特卡洛法采样和多目标遗传算法对机构进行稳健性优化设计。优化结果表明,该方法更符合实际情况,且当设计变量发生变异时,能有效保证机构性能。