Topology and sizing optimization of discrete structures using a cooperative coevolutionary genetic algorithm with independent ground structures

This article proposes a method called the cooperative coevolutionary genetic algorithm with independent ground structures (CCGA-IGS) for the simultaneous topology and sizing optimization of discrete structures. An IGS strategy is proposed to enhance the flexibility of the optimization by offering two separate design spaces and to improve the efficiency of the algorithm by reducing the search space. The CCGA is introduced to divide a complex problem into two smaller subspaces: the topological and sizing variables are assigned into two subpopulations which evolve in isolation but collaborate in fitness evaluations. Five different methods were implemented on 2D and 3D numeric examples to test the performance of the algorithms. The results demonstrate that the performance of the algorithms is improved in terms of accuracy and convergence speed with the IGS strategy, and the CCGA converges faster than the traditional GA without loss of accuracy.     

基于多目标粒子群协同算法的状态参数优化

针对铅锌烧结过程综合透气性、烧结终点的优化具有强非线性、计算复杂等特点,提出了一种有效的多目标粒子群协同优化算法。首先,建立了有综合透气性、烧结终点两个目标的优化模型。接着,通过改进的约束比较方法、粒子极值选取方法,以及利用不同的粒子群来分别优化相应的变量,提出了一种改进的多目标粒子群协同优化算法。最后,利用提出的多目标优化算法进行综合透气性、烧结终点的优化。仿真结果表明,所提出的多目标优化算法能较好地解决综合透气性、烧结终点的优化问题。     

Multi-objective optimization of engineering systems using game theory and particle swarm optimization

This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.     

多目标优化软件开发及其应用

针对基于传统数学规划原理的多目标优化方法在实际工程优化问题中的脆弱性及现有一些多目标优化方法在应用时具有的缺陷性,提出了一种将改进粒子群算法与灰色决策相结合的多目标优化方法.联合MATLAB和VB,开发了一款基于该方法的便于工程应用的多目标优化软件.将该软件应用于掘进机铲板参数多目标优化,取得了良好的优化效果,验证了该方法的可行性,为工程实际中处理多目标优化问题提供了便利与借鉴.     

Multi-objective feasibility enhanced particle swarm optimization

This article introduces a new method entitled multi-objective feasibility enhanced partical swarm optimization (MOFEPSO), to handle highly-constrained multi-objective optimization problems. MOFEPSO, which is based on the particle swarm optimization technique, employs repositories of non-dominated and feasible positions (or solutions) to guide feasible particle flight. Unlike its counterparts, MOFEPSO does not require any feasible solutions in the initialized swarm. Additionally, objective functions are not assessed for infeasible particles. Such particles can only fly along sensitive directions, and particles are not allowed to move to a position where any previously satisfied constraints become violated. These unique features help MOFEPSO gradually increase the overall feasibility of the swarm and to finally attain the optimal solution. In this study, multi-objective versions of a classical gear-train optimization problem are also described. For the given problems, the article comparatively evaluates the performance of MOFEPSO against several popular optimization algorithms found in the literature.     

Multi-objective aerodynamic shape optimization of small livestock trailers

This article presents a formal optimization study of the design of small livestock trailers, within which the majority of animals are transported to market in the UK. The benefits of employing a headboard fairing to reduce aerodynamic drag without compromising the ventilation of the animals’ microclimate are investigated using a multi-stage process involving computational fluid dynamics (CFD), optimal Latin hypercube (OLH) design of experiments (DoE) and moving least squares (MLS) metamodels. Fairings are parameterized in terms of three design variables and CFD solutions are obtained at 50 permutations of design variables. Both global and local search methods are employed to locate the global minimum from metamodels of the objective functions and a Pareto front is generated. The importance of carefully selecting an objective function is demonstrated and optimal fairing designs, offering drag reductions in excess of 5% without compromising animal ventilation, are presented.     

飞机总体设计中的一种多目标鲁棒优化方法

 提出一种基于灵敏度的多目标鲁棒优化方法。针对各维设计变量存在扰动的情况,在原约束多目标优化模型上,附加偏差目标函数,并采用最差估计法对约束条件进行鲁棒可行性调整。采用全局敏度方程方法来计算目标函数和约束函数对设计变量的敏度,进而采用Pareto遗传算法搜索约束多目标优化问题的非劣解集,设计者可以根据不同的设计准则从中选择合适的设计点。将上述方法用于飞机总体参数优化设计,并与采用常规优化方法所得的优化结果进行了分析和比较。     

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。     

Multi-objective optimization of space station logistics strategies using physical programming

This study extends a previously proposed single-objective optimization formulation of space station logistics strategies to multi-objective optimization. The four-objective model seeks to maximize the mean utilization capacity index, total utilization capacity index, logistics robustness index and flight independency index, aiming to improve both the utilization benefit and the operational robustness of a space station operational scenario. Physical programming is employed to convert the four-objective optimization problem into a single-objective problem. A genetic algorithm is proposed to solve the resulting physical programming-based optimization problem. Moreover, the non-dominated sorting genetic algorithm-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the physical programming-based solution. The proposed approach is demonstrated with a notional one-year scenario of China's future space station. It is shown that the designer-preferred compromise solution improving both the utilization benefit and the operational robustness is successfully obtained.     

钢铝板材压力连接模具几何参数多目标优化

为解决钢铝混合车身中钢和铝异种材料间的连接问题,对无铆钉自冲铆连接钢铝异种材料的可行性进行了实验研究和仿真分析.在实验验证的基础上,为得到连接接头处的最优机械性能,以接头颈厚值和自锁值为评价指标,综合运用实验设计、统计分析、代理模型构造响应面以及遗传算法对钢铝压力连接模具几何参数进行多目标优化,得到了接头颈厚值和自锁值...     

Multi-objective optimization of a flexible slider-crank mechanism synthesis,based on dynamic responses

This work deals with a multi-body system synthesis. A flexible slider crank mechanism has been investigated as an illustrative application. The main interest is focused on the mechanism design variables’ identification based on its dynamic responses. Three responses have been involved such as the slider velocity, the slider acceleration and the mid-point transversal deflection of the flexible connecting rod. Each of these responses has been embroiled separately in a mono-objective optimization. Subsequently, the multi-objective optimization subsuming these responses has been established. Two different optimization methods have been studied namely the genetic algorithm (GA) and the particle swarm optimization (PSO) technique. It has been proved that the multi-objective optimization presents more accurate results beside the mono-objective optimization. Compared to the GA, the PSO is more powerful and is able to identify the mechanism design variable with better accuracy, in spite of the affordable computational time allowed with the GA optimization.     

Multi-objective optimization based on meta-modeling by using support vector regression

Practical engineering design problems have a black-box objective function whose forms are not explicitly known in terms of design variables. In those problems, it is very important to make the number of function evaluations as few as possible in finding an optimal solution. So, in this paper, we propose a multi-objective optimization method based on meta-modeling predicting a form of each objective function by using support vector regression. In addition, we discuss a way how to select additional experimental data for sequentially revising a form of objective function. Finally, we illustrate the effectiveness of the proposed method through some numerical examples.     

基于代理模型的空投装备气囊缓冲系统多目标优化

基于有限元法和控制体积法建立装备-气囊系统有限元模型,并采用试验数据对模型进行验证。复杂气囊系统着陆缓冲过程仿真计算资源消耗大,难以应用传统迭代方法进行参数优化。为克服这些问题,结合扩展拉丁超立方设计,以最大着陆冲击加速度和最大翻转角度为响应,采用径向基函数构建代理模型。在代理模型基础上,利用多目标遗传算法对主气囊高度、横向宽度及排气孔面积等气囊缓冲系统参数进行了多目标优化。优化结果表明:优化后最大冲击加速度减小了15.5%,最大翻转角度减小了70.3%,缓冲性能与横向稳定性均有所提高。     

汽车前纵梁吸能盒结构耐撞性多目标优化

吸能盒上诱导槽的分布形式对汽车碰撞性能有很大影响.以汽车前纵梁吸能盒结构为对象,建立整车正面碰撞仿真模型,对吸能盒结构的耐撞性进行了多目标优化设计.以吸能盒上诱导槽之间的间距为设计变量,使用Hammersley方法采集样本点后,以车辆吸能盒的最大吸能量E、最大刚性墙反力F以及车身最大加速度a为目标函数,通过krigin...     

基于GA-NLP的剪刀式折叠桥梁展桥机构多目标优化设计

针对新型剪刀式折叠桥梁展桥机构的优化设计问题,首先建立了展桥机构的运动学和静力学模型,然后以展桥机构关键铰点位置和岸桥节与竖直方向所成夹角为优化设计变量,以展桥机构的空间位置为主要约束条件,以展桥油缸、连杆、关键铰点受力峰值最小为优化目标,通过正规化和加权处理构造了展桥机构多目标优化分析模型,并采用遗传算法（genetic algorithm, GA）和非线性规划(nonlinear programming, NLP)混合算法对该优化分析模型进行求解。最后,利用ADAMS（automatic dynamic analysis of mechanical systems,机械系统动力学自动分析）软件验证了展桥机构多目标优化分析模型的正确性。结果表明,优化后展桥油缸承载的拉力与推力峰值分别减小了57.9%和25.3%,连杆承载的拉力与压力峰值分别减小了26.1%和55.2%,展桥机构2个关键铰点受力峰值分别减小了23.5%和26.8%。研究结果可为展桥机构的改进设计提供理论依据。     

基于物理规划的 Stewart 平台多目标优化

应用物理规划方法对500米口径球面射电望远镜(FAST)精调 Stewart 平台进行了多目标优化设计.根据 Stewart 平台的设计准则,将 Stewart 平台的运动精度、重量和工作效率作为目标函数,构造了相应的以目标函数为变量的偏好函数,建立了基于物理规划的多目标优化模型,采用遗传算法对该优化问题进行求解.结果表明,物理规划避免了基于权重的多目标优化方法中权系数难以确定的问题,有效地给出了优化问题的 Pareto 解.     

Multi-objective optimization for the multi-mode finance-based project scheduling problem

The finance-based scheduling problem (FBSP) is about scheduling project activities without exceeding a credit line financing limit. The FBSP is extended to consider different execution modes that result in the multi-mode FBSP (MMFBSP). Unfortunately, researchers have abandoned the development of exact models to solve the FBSP and its extensions. Instead, researchers have heavily relied on the use of heuristics and meta-heuristics, which do not guarantee solution optimality. No exact models are available for contractors who look for optimal solutions to the multi-objective MMFBSP. CPLEX, which is an exact solver, has witnessed a significant decrease in its computation time. Moreover, its current version, CPLEX 12.9, solves multi-objective optimization problems. This study presents a mixed-integer linear programming model for the multi-objective MMFBSP. Using CPLEX 12.9, we discuss several techniques that researchers can use to optimize a multi-objective MMFBSP. We test our model by solving several problems from the literature. We also show how to solve multi-objective optimization problems by using CPLEX 12.9 and how computation time increases as problem size increases. The small increase in computation time compared with possible cost savings make exact models a must for practitioners. Moreover, the linear programming-relaxation of the model, which takes seconds, can provide an excellent lower bound.     

基于弹流润滑理论的非对称齿轮胶合强度多目标优化

为提高双压力角非对称齿廓齿轮的设计质量,缩短设计周期,依据弹性流体动力润滑理论,通过范例,以齿间最小油膜厚度最大和齿轮传动总体积最小为目标函数,按照粒子群优化算法,利用MATLAB编制优化程序,进行约束多目标优化设计.在此基础上,根据齿轮啮合原理和现代摩擦学原理从数学逻辑关系和物理机理上分析了目标函数对各个设计变量的灵敏度.研究结果表明:非对称齿轮的体积随模数和齿宽的增加而增加,对模数的敏感程度大于齿宽;齿间最小油膜厚度随模数、齿宽、压力角及变位系数的增加而增加,其敏感程度依次为压力角、模数、齿宽和变位系数;压力角是影响弹流润滑齿间最小油膜厚度最重要的因素,在工作齿侧适度增大压力角可以显著增大最小膜厚;大、小齿轮的变位系数对最小油膜厚度具有同等的影响程度.     

基于统计提升准则的注塑工艺参数优化

为了同时改善生产平板型注塑制品时的总体收缩度和收缩均匀度,提出基于统计提升准则的注塑成型工艺参数的多目标优化方法,寻找平衡两个质量指标的优化设计.首先利用小规模的实验设计方法获得建模数据集,针对应用中存在的建模数据奇异点问题提出一种数据预处理方法,并依此分别建立两个指标的初始替代模型,用于代替优化过程中代价高昂的计算分析;随后依据Pareto统计提升准则寻找新的采样点加入建模数据集来重新建模,使寻优结果不断趋近真实的Pareto前沿.仿真结果表明,较常规的建模优化方法,本文提出的方法能使用较少的采样数据,显著地改善平板制品的收缩质量.对于HDPE材质的矩形制品,保压曲线先恒定后线性递减可以获得好的收缩均匀度,使用压力上限值恒定保压可以获得好的平均收缩度.     

Multi-objective optimization of spectrum detection in cognitive IoT using artificial physics

In cognitive Internet of Things (C-IoT), spectrum detection aims to find the available spectrum resources for cognitive sensor nodes. However, it always consumes more energy to get higher detection rate in spectrum detection, so energy consumption and detection rate are positively correlated in C-IoT. Different from the available algorithms, we model spectrum detection in C-IoT as a multi-objective optimization problem and aim to find the trade-off points of spectrum detection. An artificial physics optimization algorithm is proposed to solve spectrum detection problems in C-IoT. The simulation results show that the proposed algorithm can effectively reduce the energy consumption and keep a high detection rate.