基于遗传算法的车间布局多目标优化

在分析单目标优化缺陷的基础上,构建了车间多行布局物流成本和空间利用率多目标组合优化模型.设计了车间多行布局遗传算法,针对自动换行、两行布置和三行布置三种不同布局策略的编码方式、遗传操作和适应度函数进行了研究.以某车间布局为实例,分别在单目标和多目标情况下,通过Matlab编程对三种不同布局策略进行了遗传运算,验证了算法的收敛性、实用性和有效性.该模型及算法对车间进行多目标多行布局具有借鉴意义.     

基于多目标遗传算法的传感器优化布点研究

针对目前桥梁模态试验中传感器布点优化都是基于单一准则下的研究的局限性,提出采用双准则或多准则下的传感器优化布点研究,该方法改进了以往布点优化中单一目标最优的限制,转而寻求多准则下的优化布点的满意解,而非真正严格数学意义上的最优解。采用Pareto遗传算法,设计了相应的遗传算子和编码方案,成功求解了双准则下传感器最优布点问题,优化结束时,成功给出了一组pareto最优解的前沿面,进而证明了提出的方法是可行的。     

基于遗传算法的辊筒棒磨机多目标优化设计

为了优化辊筒棒磨机的磨矿过程,选取影响磨矿性能的主要因素,利用量纲分析法对磨矿特征进行描述,并结合实验数据,建立磨矿过程的多目标优化模型;利用遗传算法对模型进行求解,得到Pareto最优解集,采用优劣解距离(TOPSIS)法寻得多目标优化问题的一组最优解,并对结果进行分析。结果表明:进行多目标优化后,棒磨机产率提高了5. 46%,能源利用率提升5. 04%,磨矿产物的均一性指数增大1. 088,有效地提升了辊筒棒磨机的综合性能。     

磁流变液减振器的多目标优化与设计

磁流变液减振器由于其结构简单、可靠性高、稳定性好、减振效果出色等优点,成为了当今国内外学者和生产商研究和开发的热点。磁流变液是磁流变液减振器的核心元件,通过改变外加磁场,可以可逆、连续、快速的调节其粘度。磁流变液减振器在车辆的半主动悬架系统中起着关键性的作用,为了能让车辆快速、平稳的行驶,磁流变减振器需要具备良好的动力学籽陛、低功耗特性以及良好的系统响应,但目前绝大多数的研究与设计仅仅是以其中某一项为目标进行优化,优化出来的磁流变液减振器很难满足综合性能要求。此项目从多目标的角度出发,通过遗传算法计算其结构参数,设计出适用于实际车型应用、满足综合性能的磁流变减振器。     

基于多目标遗传算法的山药真空干燥工艺优化

为了得到山药切片真空干燥的最优工艺参数,以平均干燥速率Y₁、单位能耗Y₂、复水比Y₃和白度指数Y₄为试验指标,以干燥温度T、压强V和切片厚度L为影响因素进行进行Box-Behnken响应面优化试验,通过多元非线性回归分析建立各指标的数学模型,最后分别使用加权评分法和遗传算法进行多目标优化。结果表明:多目标遗传算法优化结果更合理,得到的山药切片真空干燥最优工艺参数为:干燥温度63.76℃、压强0.0532 MPa和切片厚度2.46 mm,所对应的指标值为:平均干燥速率0.00897 g/(g·min)、单位能耗14.205 kW·h/kg、复水比2.078和白度指数81.220。     

基于免疫遗传计算的零件多目标优化

由生物引发的信息处理系统可分为:人工神经网络、进化计算和人工免疫系统(AIS)。其中神经网络和进化计算已被广泛用于各领域,而AIS则由于其复杂性较少应用。笔者将免疫算法与遗传计算结合,研制了一个基于免疫遗传计算的优化设计系统(Immune ＆ Genetic Algorithm based Design SupportSystem-IGBODS)。IGBODS用于零件的优化设计,避免了遗传算法搜索效率低,过早收敛和不能很好保持个体的多样性等问题,具有很大的优越性。     

一种基于遗传算法的多目标优化算法研究

对一般的无约束多目标优化问题的求解进行讨论,提出一种基于遗传算法的求解方法,该方法区别于传统遗传算法的求解模式,它采用带性别标志的编码、子群体的选择、保留Pareto最优解,并对解集进行共享函数的处理,最后得到较高质量的Pareto最优解集,给出的两个算例也充分说明此方法在处理多目标问题的可行性和实用性。     

基于改进遗传算法的多式联运网络优化

在对多种运输方式比较分析的基础上,从运输成本、换装成本及时间惩罚成本3个角度,建立多式联运网络模型,采用改进遗传算法进行模型求解,在标准遗传算法基础上引入移民算子,保证了种群多样性,避免了局部最优,增强了算法搜索能力.采用MATLAB计算平台对模型进行案例求解,结果表明,采用多式联运网络运作模型的成本降低了45.8%,从而验证了多式联运网络运作模型的有效性和实用性.     

基于遗传算法的热轧生产过程多目标优化

针对热轧生产过程中的板坯出库策略问题,建立了旨在减少板坯倒垛量和板坯温降程度的多目标模型;针对问题的特点,提出了基于遗传算法的求解算法.算法能够根据目标的重要性调节加权参数的大小,以便根据应用中的实际情况调整优化的重点;通过在迭代过程中引入启发式变异算子,来提高算法的收敛速度.最后通过数值实验验证了算法的可行性和有效性.     

新型多目标遗传算法及其在导弹射击效能优化中的应用

多目标规划是一类重要的优化模型，有着广泛的实际应用，但其求解至今仍是运筹学的一个难点．针对一般约束多目标优化问题，在设计了新的适应度函数和选择算子的基础上，提出一种新型多目标遗传算法．将其应用于导弹对集群目标射击效能优化问题，验证了算法的有效性．     

A new optimization algorithm based on a combination of particle swarm optimization,convergence and divergence operators for single-objective and multi-objective problems

Particle swarm optimization (PSO) is a randomized and population-based optimization method that was inspired by the flocking behaviour of birds and human social interactions. In this work, multi-objective PSO is modified in two stages. In the first stage, PSO is combined with convergence and divergence operators. Here, this method is named CDPSO. In the second stage, to produce a set of Pareto optimal solutions which has good convergence, diversity and distribution, two mechanisms are used. In the first mechanism, a new leader selection method is defined, which uses the periodic iteration and the concept of the particle's neighbour number. This method is named periodic multi-objective algorithm. In the second mechanism, an adaptive elimination method is employed to limit the number of non-dominated solutions in the archive, which has influences on computational time, convergence and diversity of solution. Single-objective results show that CDPSO performs very well on the complex test functions in terms of solution accuracy and convergence speed. Furthermore, some benchmark functions are used to evaluate the performance of periodic multi-objective CDPSO. This analysis demonstrates that the proposed algorithm operates better in three metrics through comparison with three well-known elitist multi-objective evolutionary algorithms. Finally, the algorithm is used for Pareto optimal design of a two-degree of freedom vehicle vibration model. The conflicting objective functions are sprung mass acceleration and relative displacement between sprung mass and tyre. The feasibility and efficiency of periodic multi-objective CDPSO are assessed in comparison with multi-objective modified NSGAII.     

基于复合优化方法立式数控加工中心的多目标优化设计

为实现加工中心动静态性能不低于优化前性能,达到整机重量最轻的要求,本文提出了一种复合优化方法来研究多变量、多约束和多目标的数控加工中心优化设计。采用有限元分析和实验模态测试方法分析各大件动态性能,并验证了有限元模型的精确性。然后以该有限元模型为基础进行静态分析,得出各大件的最大变形及应力等。以柔度为目标,采用变密度法拓扑优化设计立柱结构的外形框架;以固有频率为目标,基于元结构的可适应性动态优化方法设计加工中心的筋板结构;以固有频率和质量为目标,基于响应面法的尺寸优化确定各结构的最优尺寸。最后将优化后的各大件进行整机装配,分析校核整机动静态性能。分析结果表明,优化后的整机在保证加工中心动静态性能的条件下,整机质量从12749kg减少到12127kg,减重达到4.9%,达到了整机的优化设计要求,说明该方法具有较高的精度和较强的工程实用性。     

Performance improvement of an active vibration absorber subsystem for an aircraft model using a bees algorithm based on multi-objective intelligent optimization

This study develops a mathematical model to investigate the behaviour of adaptable shock absorber dynamics for the six-degree-of-freedom aircraft model in the taxiing phase. The purpose of this research is to design a proportional-integral-derivative technique for control of an active vibration absorber system using a hydraulic nonlinear actuator based on the bees algorithm. This optimization algorithm is inspired by the natural intelligent foraging behaviour of honey bees. The neighbourhood search strategy is used to find better solutions around the previous one. The parameters of the controller are adjusted by minimizing the aircraft’s acceleration and impact force as the multi-objective function. The major advantages of this algorithm over other optimization algorithms are its simplicity, flexibility and robustness. The results of the numerical simulation indicate that the active suspension increases the comfort of the ride for passengers and the fatigue life of the structure. This is achieved by decreasing the impact force, displacement and acceleration significantly.     

Radial basis functional model for multi-objective sheet metal forming optimization

Fracture and wrinkling are two major defects in sheet metal forming and can be eliminated via an appropriate drawbead design. This article proposes to adopt a multi-objective particle swarm optimization (MOPSO) approach, which differs from traditional multi-objective optimization with construction of a single cost function. MOPSO shows a certain advantage over other single cost function or population-based algorithms. While radial basis function (RBF) has shown considerable promise in highly non-linear problems, there has been no report in sheet metal forming design. Here RBF is attempted to establish the metamodels for fracture and wrinkling criteria in sheet metal forming design. In this article, a sophisticated automobile inner stamping case is exemplified, which demonstrated that RBF provides a better surrogate accuracy and MOPSO is more effective than the other methods studied. The use of RBF driven MOPSO procedure significantly improved the formability and can be recommended for sheet metal process design.     

基于差异演化算法和多属性决策的机电系统可靠性多目标优化设计

针对机电系统可靠性设计问题,以可靠性和费用(或体积等)最优为目标建立可靠性设计的多目标优化模型．提出了自适应多目标差异演化算法,该算法提出了自适应缩放因子和混沌交叉率,采用改进的快速排序方法构造Pareto最优解,采用NSGA-II的拥挤操作对档案文件进行消减．采用自适应多目标差异演化算法获得多目标问题的Pareto最优解,利用TOPSIS方法对Pareto最优解进行多属性决策．实际工程结果表明：自适应多目标差异演化算法调节参数更少,且求得的Pareto最优解分布均匀;采用基于TOPSIS的多属性决策方法得到的结果合理可行．     

An optimization design of vehicle axle system based on multiobjective cooperative optimization algorithm

ABSTRACT

To address multiobjective, multi constraint and time-consuming structural optimization problems in a vehicle axle system, a multiobjective cooperative optimization model of a vehicle axle structure is established. In light of the difficulty in the nondominated sorting of the NSGA-II algorithm caused by inconsistent effects of the uniformity objective function and physical objective function, this paper combines a multiobjective genetic algorithm with cooperative optimization and presents a strategy for handling the optimization of a vehicle axle structure. The uniformity objective function of the sub discipline is transformed to its self-constraint. Taking the multiobjective optimization of a vehicle axle system as an example, a multiobjective cooperative optimization design for the system is carried out in ISIGHT. The results show that the multiobjective cooperative optimization strategy can simplify the complexity of optimization problems and that the multiobjective cooperative optimization method based on an approximate model is favorable for accuracy and efficiency, thereby providing a theoretical basis for the optimization of similar complex structures in practical engineering.     

基于拓扑优化的重型矿用自卸车翻车保护结构设计

翻车保护结构（roll-over protective structure,ROPS）是安装在工程车辆驾驶室中的一套被动保护装置,能在翻车事故中为驾驶人员提供有效的保护。为解决ROPS承载能力、刚度、轻量化和侧向吸能效果之间的矛盾,将基于变密度法的拓扑优化技术引入重型矿用自卸车ROPS设计中,以解决ROPS在给定设计域内的材料最优分布问题,提高ROPS侧向吸能效果和垂向、纵向的刚度,减轻自重。首先,利用OptiStruct结构优化模块对ROPS进行拓扑优化设计,以多工况组合应变能最小为优化目标,按照国际标准规定的性能要求施加载荷和约束条件。基于拓扑优化结果,对ROPS进行详细设计。然后,利用显示动力分析软件LS-DYNA对ROPS的最终设计模型进行动态加载分析。最后对优化后ROPS的性能与原ROPS的性能进行对比分析。结果表明：拓扑优化设计后的ROPS在3个工况下都没有入侵DLV（deflection-limiting volume,挠曲极限量）,满足国际标准中的承载能力要求;在侧向加载中最大能量吸收达到175 kJ,满足国际标准中的侧向能量吸收要求;相较于原ROPS,拓扑优化设计后的ROPS达到侧向能量吸收要求所需的载荷从1 324.5 kN减小到1 231 kN,加载中心点的垂向位移减小21.3%,纵向位移减小34.4%,质量减小24.1%。研究结果为重型矿用自卸车ROPS的设计提供了新方法,对后续ROPS的设计与改进有一定的指导作用。     

基于支持向量机的车载称重模块设计

称重测量模块是车载称重系统的核心部分．介绍了车载称重模块的工作原理、流程和实施方案．利用Pro/Engineer软件建立了车架的三维模型．在ANSYS中对模型作必要的简化,联系现实情况在车架上施加载荷,找到应变敏感点,分析了应变和载荷之间的关系．基于支持向量机设计了称重算法,用总体样本交叉验证的方法验证了不同工况下该算法的可行性和正确性．     

Geometrical design of thermoelectric generators based on topology optimization

This paper discusses an application of the topology optimization method for the design of thermoelectric generators. The proposed methodology provides the optimized geometry in accordance with various arbitrary conditions such as the types of materials, the volume of materials, and the temperature and shape of the installation position. By considering the coupled equations of state for the thermoelectric problem, we introduce an analytical model subject to these equations, which mimics the closed circuit composed of thermoelectric materials, electrodes, and a resistor. The total electric power applied to the resistor and the conversion efficiency are formulated as objective functions to be optimized. The proposed optimization method for thermoelectric generators is implemented as a geometrical optimization method using the solid isotropic material with penalization method used in topology optimizations. Simple relationships are formulated between the density function of the solid isotropic material with penalization method and the physical properties of the thermoelectric material. A sensitivity analysis for the objective functions is formulated with respect to the density function and the adjoint equations required for calculating it. Depending on the sensitivity, the density function is updated using the method of moving asymptotes. Finally, numerical examples are provided to demonstrate the validity of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.