考虑随机因素的汽车悬架参数多目标稳健优化

为了稳健地提高汽车平顺性,减少轮胎对路面破坏。以某载货汽车的四自由度悬架模型为研究对象,同时考虑制造精度的影响,以车身垂直加速度、前后轮胎动载荷的均方根值的统计均值和方差为目标函数,采用蒙特卡罗抽样法和自适应多目标遗传算法对悬架参数进行稳健优化。结果表明：优化后的悬架弹簧刚度减少而阻尼系数增大,性能有大幅度改善,与传统的优化设计相比更符合实际情况,且具有更强的抗干扰能力。     

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.     

非支配排序均匀遗传算法*

根据第二代非支配排序遗传算法（NSGA Ⅱ）的不足之处,提出了一种新的多目标遗传算法——非支配排序均匀遗传算法（NSUGA）。新算法采用了多父本多点交叉方式,同时将均匀设计的思想用于算法的交叉操作;新算法还对拥挤距离的计算过程和算法的终止条件进行了改进。通过两个多目标优化测试函数的仿真计算对比,显示NSUGA算法在求解精度、计算效率和避免算法陷于局部最优解方面均优于NSGA II算法。     

Bi-objective partial flexible job shop scheduling problem: NSGA-II,NRGA, MOGA and PAES approaches

This paper deals with a problem of partial flexible job shop with the objective of minimising makespan and minimising total operation costs. This problem is a kind of flexible job shop problem that is known to be NP-hard. Hence four multi-objective, Pareto-based, meta-heuristic optimisation methods, namely non-dominated sorting genetic algorithm (NSGA-II), non-dominated ranked genetic algorithm (NRGA), multi-objective genetic algorithm (MOGA) and Pareto archive evolutionary strategy (PAES) are proposed to solve the problem with the aim of finding approximations of optimal Pareto front. A new solution representation is introduced with the aim of solving the addressed problem. For the purpose of performance evaluation of our proposed algorithms, we generate some instances and use some benchmarks which have been applied in the literature. Also a comprehensive computational and statistical analysis is conducted in order to analyse the performance of the applied algorithms in five metrics including non-dominated solution, diversification, mean ideal distance, quality metric and data envelopment analysis are presented. Data envelopment analysis is a well-known method for efficiently evaluating the effectiveness of multi-criteria decision making. In this study we proposed this method of assessment of the non-dominated solutions. The results indicate that in general NRGA and PAES have had a better performance in comparison with the other two algorithms.     

Multi-objective Genetic Optimization for LCLS-II X-ray FEL

The LCLS-II （linac coherent light source II） will build on the success of the world＇s most powerful X-ray laser： the LCLS （linac coherent light source）. It will add two new X-ray laser beams and room for additional new instruments, thus greatly increasing the number of experiments carried out each year. Multiple operation modes are proposed to accommodate a variety of user requirements. There are a large number of variables and objectives in the design. For each operation mode, MOGA （multi-objective genetic algorithm） is applied to optimize the machine parameters in order to minimize the jitters, energy spread, collective effects and emittance.     

Optimization of a heavy-duty compression-ignition engine fueled with diesel and gasoline-like fuels

Optimal injection strategies for a heavy-duty compression-ignition engine fueled with diesel and gasoline-like fuels (#91 gasoline and E10) and operated under mid- and high-load conditions are investigated. A state-of-the-art engine CFD tool with detailed fuel chemistry was used to evaluate the engine performance and pollutant emissions. The CFD tools feature a recently developed efficient chemistry solver that allowed the optimization tasks to be completed in practical computer times. A Non-dominated Sorting Genetic Algorithm II (NSGA II) was coupled with the CFD tool to seek optimal combinations of injection system variables to achieve clean and efficient combustion. The optimization study identified several key parameters that influence engine performance. It was found that the fuel volatility and reactivity both play important roles at the mid-load condition, while the high-load condition is less sensitive to the fuel reactivity. However, high volatility fuels, such as gasoline and E10, were found to be beneficial to fuel economy at high-load. The study indicates that with an optimized injection system gasoline-like fuels are promising for heavy-duty CI engines due to their lower NOx and soot emissions and higher fuel economy compared to conventional diesel fuels. However, the high in-cylinder gas pressure rise rate associated with Partially Premixed Combustion of gasoline-like fuels can become problematic at high-load and the low-load operating limit is also a challenge. Potential solutions are discussed based on the present optimization results.     

Analysing user trust in electronic banking using data mining methods

The potential fraud problems, international economic crisis and the crisis of trust in markets have affected financial institutions, which have tried to maintain customer trust in many different ways. To maintain these levels of trust they have been forced to make significant adjustments to economic structures, in efforts to recoup their investments and maintain the loyalty of their customers. To achieve these objectives, the implementation of electronic banking for customers has been considered a successful strategy. The use of electronic banking in Spain in the last decade has been fostered due to its many advantages, giving rise to real integration of channels in financial institutions. This paper reviews different methods and techniques to determine which variables could be the most important to financial institutions in order to predict the likely levels of trust among electronic banking users including socio-demographic, economic, financial and behavioural strategic variables that entities have in their databases. To do so, the most recent advances in machine learning and soft-computing have been used, including a new selection operator for multiobjective genetic algorithms. The results obtained by the algorithms were validated by an expert committee, ranking the quality of them. The new methodology proposed, obtained the best results in terms of optimisation as well as the highest punctuation given by the experts.     

MEMS综合与设计优化

研究与探讨MEMS综合这一新技术。MEMS综合即是要实现MEMS的自动化设计与设计优化。分析MEMS综合与VLSI中的综合方法之间的区别之处。对比了现有的几种综合算法：MOGA多目标基因算法、SA模拟热退火算法以及特定目标方法。研究了国外现有的MEMS综合的过程、步骤与综合方法,结合实例说明了国外研究现状。     

基于稳健设计理论的平面机构优化设计

运用稳健设计理论,以连杆机构的几何参数为设计变量,以满足曲柄摇杆机构及传动角条件为约束。以再现轨迹精度最佳为目标函数。同时考虑连杆件制造精度对轨迹精度的影响。采用蒙特卡罗法采样和多目标遗传算法对机构进行稳健优化设计。计算结果表明：该方法更符合实际情况,且当设计变量发生变异时,能有效保证机构的运动轨迹精度。     

多目标遗传算法及在过程优化综合中的应用

化工过程的多目标优化综合问题可归结为多目标混合整数非线性规划(MOMINLP)模型的求解,求解方法主要有数学规划法和多目标进化算法。以多目标遗传算法(MOGA)为代表的进化算法被认为是特别适合求解此类问题。遗传算法大多用于单目标问题的优化,近十几年来将遗传算法应用到多目标优化的研究得到了很大的发展。本文对多目标遗传算法的一些重要概念、发展历程进行了回顾。针对化工过程的模型特点,对MOGA在过程综合中的应用研究进行了讨论,并认为混合遗传算法应是求解此类问题的有效算法。