非稳态罚函数遗传算法及其用于机械/结构系统的健壮性设计

在机械 /结构的优化设计中 ,普遍存在约束的作用 ,且最优解往往位于可行域的边界上。由于外界环境的变化或人为因素造成设计变量扰动 ,可能使设计成为不可行。本文提出了一种基于设计变量敏感性的健壮性设计方法 ,并用非稳态罚函数遗传算法来实现多目标优化设计。     

基于Pareto遗传算法的高速插秧机变速器优化设计

以某高速插秧机变速器的优化设计为例,将Pareto最优解概念和遗传算法相结合,在遗传算法的基础上引入群体排序技术、小生境技术和Pareto解集过滤器等技术,并针对设计变量都是离散变量的特点,采用先将生成的随机数变换到约束范围后再圆整到最近离散值的方法,构造了适用于求解多目标优化问题的Pa-reto遗传算法,运用该算法获得了变速器在体积最小、中心距最小和总重合度最大目标下的Pareto最优解集。结果表明,采用Pareto遗传算法优化设计的变速器达到了综合优化设计的效果。     

多目标柔性作业车间调度决策精选机制研究

针对多目标柔性作业车间调度优化无法找到唯一最优解的问题，提出多目标遗传算法和层次分析法模糊综合评判的分阶段优化策略。提出优化阶段和精选阶段的优化任务，优化阶段选出一组Pareto解集，精选阶段从Pareto解集中选出最优解；在精选阶段运用层次分析法和模糊评判集成的策略精选调度决策。决策算例证明提出的方法是可行的，可很好地帮助决策者选择出一个最满意的解。     

聚类差分进化算法求解多目标工艺规划与 调度集成问题

针对多目标工艺规划与调度集成问题,以完工时间、交货总拖期和设备工作负荷为优化目标,建立了多目标非线性工艺规划集成模型,提出一种聚类差分进化算法。该算法设计了包含工艺、设备和加工顺序信息的3层编码结构,结合聚类算法、差分进化算法和遗传算法的相关操作,有效地优化工艺信息和调度方案,保持可行解的多样性,实现Pareto非支配解集快速更新。通过对Pareto非支配解集进行领域搜索,使其更加接近或到达Pareto最优解集。最后通过实例验证了算法的性能。     

基于区间不确定性的车架非线性可靠性优化

采用非线性有限元分析方法,用ABAQUS软件对车架的刚度和强度进行了分析。基于分析结果选取对结构强度和质量影响比较大的梁的厚度作为区间的设计变量,把车架材料的密度和泊松比作为不确定量,利用高维模型（TPS-HDMR）构建了设计变量与应力之间的近似模型,运用Kriging模型构建了设计变量与质量的近似模型。采用遗传算法中的NSGA-Ⅱ方法和隔代遗传算法,对车架应力和质量两目标进行了优化,并加入可靠度作为约束,得到了Pareto最优解集。     

用Pareto遗传算法实现机械／结构系统的多目…

提出了一种用Ｐａｒｅｔｏ遗传算法（ＰＧＡ）来实施的带约束的多目标优化方法。ＰＧＡ可得到Ｐａｒｅｔｏ最优解集，从中可选出满足设计需要的解。本文提出的算法包括５个基本算子：选、变异、交叉、小生境技术、Ｐａｒｅｔｏ集合过滤器。本文设计了小生境技术和Ｐａｒｅｔｏ集合过滤器，并建立了用于目标优化的适应度函数，使用模糊罚函数法带约束的多目标优化问题转换为无约束优化问题，其于以上方法，文中也提出了一种通用的多目     

矢量禁忌算法及其在电磁场逆问题中的应用

为分析、计算多目标优化设计的电磁场逆问题,提出了矢量禁忌优化算法.在矢量禁忌算法中,应用接触理论判断Pareto最优解、应用排序法确定可行解的适值.为保证搜索到的Pareto最优解均匀分布于目标函数和决策变量空间,提出了一种简单、有效的适值共享函数.通过典型算例的验证,可以看出,对于多目标优化设计问题,所提出的矢量禁忌能够搜索到均匀、平滑的Pareto曲线,由此可见,矢量禁忌算法可实际运用于复杂工程电磁场逆问题的分析和计算.     

基于灵敏度分析的稳健可靠性优化设计模型及MATLAB实现

在机械零件的可靠性优化设计中,普遍存在约束的作用,且最优解往往位子可行域的边界上。由于外界环境的变化或人为因素造成设计变量扰动,可能使设计成为不可行。本文提出了一种基于设计变量敏感度的稳键性设计方法,建立了基于灵敏度分析产生附加目标函数的两种稳健可靠性优化设计模型,并用 ＭＡＴＬＡＢ语言的优化工具箱和符号工具箱来实现机械零件的稳健可靠性优化设计,计算实例说明所述模型的可行性与实用性。     

带模糊预约时间的车辆路径问题的多目标禁忌搜索算法

为优化具有模糊预约时间的车辆路径问题,应用模糊事件给出了车队服务满意度的一个新的度量方法和求最大满意度的计算方法.建立了多目标数学规划模型,并提出多目标禁忌搜索算法求解Pareto最优解.采用随机车辆配载方法生成初始解放入候选解池中,提出插人可行邻域和2-Opt可行邻域进行邻域搜索.对池中的Pareto解进行并行的禁忌搜索得到局部Pareto解再注人池中,最后求得一组Pareto解.通过Solomon的benchmark算例,与非支配排序遗传算法Ⅱ进行对比实验,说明了所提算法的优越性.     

基于遗传算法的锥齿轮减速器优化设计

通过对一个具体的锥齿轮减速器进行分析与设计,建立了优化数学模型,并应用遗传算法求出了问题的最优解,探讨了用遗传算法解决此类多约束、多变量类型优化问题的可行性。     

A new multi-objective ant colony algorithm for solving the disassembly line balancing problem

The disassembly line is the best choice for automated disassembly of disposal products. Therefore, disassembly line should be designed and balanced so that it can work as efficiently as possible. In this paper, a mathematical model for the multi-objective disassembly line balancing problem is formalized firstly. Then, a novel multi-objective ant colony optimization (MOACO) algorithm is proposed for solving this multi-objective optimization problem. Taking into account the problem constraints, a solution construction mechanism based on the method of tasks assignment is utilized in the algorithm. Additionally, niche technology is used to embed in the updating operation to search the Pareto optimal solutions. Moreover, in order to find the Pareto optimal set, the MOACO algorithm uses the concept of Pareto dominance to dynamically filter the obtained non-dominated solution set. To validate the performance of algorithm, the proposed algorithm is measured over published results obtained from single-objective optimization approaches and compared with multi-objective ACO algorithm based on uniform design. The experimental results show that the proposed MOACO is well suited to multi-objective optimization in disassembly line balancing.     

Multi-objectives optimal model of heavy equipment using improved Strength Pareto Evolutionary Algorithm

The problem of injection molding machine’s multi-objective optimization is very important. A triple-objective optimization model with the largest mould moving speed and injecting capacities and the smallest injecting power has been created. The optimized design constraints of the optimal model are summarized. The computational efficiency of Strength Pareto Evolutionary Algorithm (SPEA) is improved by using rough set-based support vector clustering method. The number of external stocks is reduced. The optimal Pareto solution is determined by eliminating the uncertainty in the artificial priority election. The multi-objective optimization of the HT1600X1N injection molding machine is taken as an example. The SPEA-RSVC-II which is the mixed algorithm of Strength Pareto Evolutionary Algorithm and Ro′ugh-based Support Vector Clustering is applied. It shows that the new method could accelerate the population clustering operation effectively and improves the efficiency of optimized calculation.     

基于NSGAⅡ的齿轮减速器多目标优化研究

为了解决工程设计中有离散变量、多约束的多目标优化问题,对改进的非占优排序遗传算法(NSGAⅡ)进行了研究,通过基于拥挤距离的非占优排序,提出了离散变量和多约束的处理方法,利用Matlab软件编写了NSGAⅡ的多目标优化程序,并以二级减速器多目标优化设计为例,建立了多目标优化数学模型,运用NSGAⅡ算法求解得到了帕累托最优解集,根据模糊集合理论的有关方法选取了最优解,与传统方法得到的结果相比,体积、失效概率和传动误差都有不同程度的降低。研究结果表明,修改后的NSGAⅡ能用于有效地求解有离散变量、多约束的多目标优化设计问题。     

火炮身管-反后坐装置集成优化设计方法研究

为减小火炮发射时的后坐阻力,同时减小火炮身管质量,以节制杆结构参数、身管结构参数和内弹道装药参数为设计变量,以身管刚度、强度、寿命、弹丸初速等为约束,建立火炮身管-反后坐装置集成优化设计模型,并采用两种优化方法进行优化,再比较其结果。首先,采用加权组合法将两个目标函数归一为单个目标,应用模拟退火算法进行优化设计,优化后,后坐阻力减小53%,身管质量减小28%。其次,基于Pareto最优理论,采用遗传算法进行优化设计,获得了Pareto最优解集,给设计者提供了更多优化方案;根据工程经验,选取一组优化解,结果发现,对比原设计,后坐阻力减小504%,身管质量减小98%。研究表明,基于Pareto最优理论和遗传算法可以获得更好的优化方案。该研究提供的集成优化模型和算法为火炮身管-反后坐装置一体化设计提供了新方法。
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资源约束下产品开发任务调度的多目标优化

鉴于产品开发任务调度过程中存在资源约束问题和学习与遗忘效应,需要对多个目标进行优化决策,通过定义资源平均利用率并提出学习遗忘效应矩阵,结合耦合设计的多阶段迭代模型,以各阶段资源利用率为约束条件,建立资源约束下考虑学习与遗忘效应的任务调度时间与成本的多目标优化数学模型.采用带精英策略的非支配排序遗传算法求解得出Paret...     

基于非劣最优理论的PSO多目标设计的应用

介绍基于非劣最优理论的粒子群优化算法所进行的多目标优化设计,以斜齿轮体积和传动平稳可靠性为目标函数,建立斜齿圆柱齿轮传动的多目标设计数学模型。为高速运转状态下,斜齿轮设计提供一种快速、优化的好方案。     

Multi-disciplinary design optimization and performance evaluation of a single stage transonic axial compressor

The multidisciplinary design optimization method, which integrates aerodynamic performance and structural stability, was utilized in the development of a single-stage transonic axial compressor. An approximation model was created using artificial neural network for global optimization within given ranges of variables and several design constraints. The genetic algorithm was used for the exploration of the Pareto front to find the maximum objective function value. The final design was chosen after a second stage gradient-based optimization process to improve the accuracy of the optimization. To validate the design procedure, numerical simulations and compressor tests were carried out to evaluate the aerodynamic performance and safety factor of the optimized compressor. Comparison between numerical optimal results and experimental data are well matched. The optimum shape of the compressor blade is obtained and compared to the baseline design. The proposed optimization framework improves the aerodynamic efficiency and the safety factor.     

多目标优化方法在汽车盘式制动器设计中的应用

工程问题优化一般是多目标的优化设计,本文论述的多目标模拟退火方法是基于Pareto理论,用于解决多目标问题的一种实用方法。优化得到均匀分布的Pareto最优解集后,依据不同的设计要求,从其中选择最满意的设计结果。将此方法应用于盘式制动器优化设计,目的是在保证足够制动力矩的前提下,尽量减小摩擦副升温和制动器的尺寸,以提高制动器工作的可靠性并给整车的设计留下更多的空间。     

Multi-objective optimization of collaborative manufacturing chain with time-sequence constraints

To realize the sharing and optimization deployment of manufacturing resources, a concept of collaborative manufacturing chain (CMC) is proposed for the manufacturing of complex products in a networked manufacturing environment. To acquire the optimal CMC, a multi-objective optimization model is developed to minimize the comprehensive cost and the whole production load with time-sequence constraints. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve optimization functions. The optimal solution set of Pareto is obtained. The technique for order preference by similarity to ideal solution (TOPSIS) approach is then used to identify the optimal compromise solution from the optimal solution set of Pareto. Simulation results obtained in this study indicate that the proposed model and algorithm are able to obtain satisfactory solutions.     

A method of genetic algorithm based multiobjective optimization via cooperative coevolution

The paper deals with the identification of Pareto optimal solutions using GA based coevolution in the context of multiobjective optimization. Coevolution is a genetic process by which several species work with different types of individuals in parallel. The concept of cooperative coevolution is adopted to compensate for each of single objective optimal solutions during genetic evolution. The present study explores the GA based coevolution, and develops prescribed and adaptive scheduling schemes to reflect design characteristics among single objective optimization. In the paper, non-dominated Pareto optimal solutions are obtained by controlling scheduling schemes and comparing each of single objective optimal solutions. The proposed strategies are subsequently applied to a three-bar planar truss design and an energy preserving flywheel design to support proposed strategies.