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。     

基于多目标遗传优化的注射成型机性能设计

采用一种基于多目标第二代非支配排序遗传算法的大型注射成型机总体性能优化设计方法,以最大注射压力、最大注射速率和最小注射功率为优化目标,系统分析了两两组合优化和三目标并行优化时,注射油缸内径、螺杆直径、工作油压力和工作油流量等设计参数对注射机构性能的影响,以及螺杆最大塑化能力的变化趋势.对于多目标优化的Pareto集,使用基于模糊集合理论的方法在其中选择一个最优解,排除人为偏好的不确定因素.与强度Pareto进化算法相比,第二代非支配排序遗传算法能以较小的时空复杂性得到分布均匀且更加逼近最优解的Pareto前沿.最后,以HTF180X2N大型注射成型机为例,说明所得结果可为决策者在大型注射成型机方案设计阶段进行目标权衡处理提供有效的依据.     

动态多目标优化的运动物体图像分割

对小区背景下运动物体图像进行分割时多使用单目标或多目标优化方法,这类方法不能有效适应目标的动态变化,因此本文提出一种动态多目标图像分割优化方法。该方法将时间及环境动态因素作为动态因子,利用K均值(KMeans)算法和和模糊C均值(FCM)聚类算法构造多目标函数;结合动态多目标粒子群算法(DMPSO),使用背景差分法定义环境变化规则,实现动态多目标的图像分割。根据DMPSO算法优化后的聚类结果,分别与K-Means和FCM聚类方法得到的结果进行了对比。结果表明,动态多目标优化的Pareto最优解集分布均匀,图像分割准确率可达到95%,对图像识别的准确率可达到90%,具有较高的识别能力,能满足确定背景下运动物体的准确识别。     

基于策略的复杂机械产品可维修性设计

为提高机械产品的使用寿命,对产品的维修性和维修性的度量进行了分析,建立了基于维修效率和维修成本的混合策略复杂机械产品维修性能优化数学模型,提出了基于改进的强度Pareto进化算法的复杂机械产品的维修性能设计流程.根据该模型,求得了多目标维修性能优化问题的Pareto最优解集,并使用基于模糊集合理论的方法选择出最优解,在优化产品维修效率的同时优化产品维修成本.应用所提方法对多段式立体停车设备升降横移部件进行了维修件能优化设计,验证了该方法的正确性与高效性.     

基于拥挤距离排序的多目标粒子群优化算法及其应用

针对多目标粒子群算法在全局寻优能力和Pareto集多样性上的不足,提出基于拥挤距离排序的多目标粒子群算法.该算法采用精英策略,基于个体拥挤距离降序排列,进行外部种群的缩减和全局最优值的更新,并在内部粒子群中引入小概率变异机制,增强算法的全局寻优能力,控制Pareto最优解的数目,同时保证其收敛性和多样性特征.在电梯曳引性能的多目标优化应用中,证明了该算法对于两目标和三目标优化问题求解的有效性.不同规模实例的运算对比表明,该算法在Pareto前沿的收敛性和多样性方面均优于改进强度Pareto进化算法,且缩短了运算时间,具有较高的效率与鲁棒性.     

基于多目标优化算法的发动机进气道设计

在发动机进气道满足性能的基础上采用多目标优化方法对其结构进行多目标优化设计.提出了利用多目标进化算法的优化策略对进气道进行优化设计,选取进气道气流转折角作为设计变量,在进气道尺寸以及流量的约束条件下,采用Halton序列产生初始均匀种群,利用Pareto的非支配排序的方法对发动机进行优化设计以达到尽可能小的阻力系数、尽可能大的总压恢复系数和进气道升压.通过文中设计的多目标进化算法对发动机进气道进行优化设计,得到的优化解均优于初始设计的Pareto最优解,表明多目标进化算法较强的适应能力,达到了进气道优化设计的目的,为发动机进气道的优化设计提供了参考.     

基于SPEA的多目标柔性作业车间调度方法

研究了多目标柔性作业车间调度问题,构建了以制造工期、加工成本及交货期为目标函数的柔性作业车间多目标调度模型,应用改进的强度Pareto进化算法(SPEA)进行求解。在该算法中,引入模糊C-均值聚类(FCM)加快外部种群的聚类过程。采用约束Pareto支配和双层编码策略,一次运行就能够求得Pareto最优解集,并利用模糊集合理论的方法得到Pareto解的优先选择序列和选出一个最优解。最后,将该方法应用于某机械公司车间调度中,验证了该方法的有效性和适应性。     

基于直觉模糊数与多目标优化算法的工艺路线优化

为解决工艺规划中的工艺路线决策问题,提出基于直觉模糊数与元胞自动机—第二代强度Pareto进化算法的工艺路线多目标优化方法。分析了零件的加工特征并将其分解为可用知识化表达的加工元,为有效处理加工元之间的模糊性顺序约束关系,利用直觉模糊数设计结构矩阵来建立加工元之间的约束关系。在此基础上,构建了以加工设备变换成本、装夹变换成本、刀具变换成本为优化目标的工艺路线多目标优化模型。为提高求解多目标工艺路线的求解效率,利用元胞自动机和第二代强度Pareto进化算法对工艺路线优化模型进行求解,得到由多个可行的工艺路线组成的Pareto前沿,进而通过模糊熵对其评价后筛选出最佳工艺路线。以某设备的传动箱箱体为例,验证了所提方法的可行性与有效性。     

扇形采样约束多目标差分进化算法及工程应用

工程实践中存在大量约束多目标优化问题(Constrained multi-objective optimization problems, CMOPs),多目标进化算法是求解这类问题的一类有效方法。引入扇形采样技术,将二次变异双种群差分进化算法和约束处理方法相结合,设计求解CMOPs的进化算法——基于扇形采样的约束多目标差分进化算法(Sector-sampling-based constrained multi-objective differential evolution algorithm, SS-CMODE)。扇形采样可避免耗时的非劣操作,且能保证Pareto最优解集的良好逼近性和多样性。通过3个典型CMOPs的对比测试,表明SS-CMODE的解集均匀性和计算效率明显优于对比算法。以J23-80机械压力机使用的双曲柄串联机构多目标优化为例,研究新算法求解工程问题的有效性。以锻冲工作阶段平均速度波动最小和力传动性能最优为目标,建立机构的约束多目标优化模型,再应用SS-CMODE求解该问题。结果表明,该算法能求出多组满足约束条件的Pareto最优解,且解集均匀性良好。     

-种基于Pareto解集的无约束条件的多目标粒子群算法

鉴于多目标优化问题的广泛存在性以及目前关于它的研究还较少,且没有一种很好的、通用的多目标PSO算法,本文提出了一种基于Pareto解集的多目标粒子群算法.通过采用一个"记忆体"来存储当前得到的Pareto最优解,对每次迭代得到的Pareto解集里的解两两进行比较以选取一个较优的解作为更新方程中当前最优解,这样可以更好的引导粒子群进行下一步的寻优操作,最终得到一个完整的Pareto最优解集.几个测试函数的仿真实验结果也表明了该算法取得了很好的效果.     

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SPEA2+的产品族模块单元多目标规划方法

在分析零件功能和物理特性的基础上,应用模糊数学理论给出产品族模块单元规划准则的量化计算方法,建立产品族模块单元多目标规划优化模型。针对传统的目标加权法在模型求解中的不足,采用改进的强度Pareto进化算法(SPEA2+)对模块单元多目标规划问题进行优化求解,从而得到一系列基于Pareto解集的产品模块规划方案,并利用模糊集合理论的Pareto综合选优方法得到了产品模块多目标规划的最优方案。结合项目实施以缝纫机为应用实例,验证提出方法的有效性和适应性。     

Intelligent control of gas-assisted injection molding control systems

This study described the design and construction of gas-assisted injection molding systems incorporating a traditional injection molding machine. This combined system is called a gas-assisted injection molding control system (GAIMCS). The mathematical model of GAIMCS with nonlinear dynamics is difficult to establish accurately. Therefore, model-free intelligent control strategies were developed to control this system and evaluate its control performances. This work presents two intelligent control strategies: (1) traditional fuzzy controller (TFC), and (2) grey prediction fuzzy controller (GPFC). The GAIMCS was controlled by the GPFC, which was compared to a TFC to evaluate the system control performance. The GPFC achieves better control performances in accelerating rise time, and it reduces the system steady-state error better than the TFC for high-pressure gas control in GAIMCS, based on the verified experimental results.     

基于支持向量回归的熔丝制造3D打印能效优化模型

基于响应面法和支持向量回归模型对熔丝制造3D打印能效进行预测与优化。首先,利用田口方法设计六因素三水平正交试验,通过响应面法分析得出对加工能效影响较为显著的3个因素即层高、打印速度和热床温度;然后,通过支持向量回归方法建立加工能效预测模型,并与BP神经网络方法进行对比,结果表明支持向量回归方法建模预测性能更优;最后,建立以加工时间和能效为目标的优化模型,利用NSGA-Ⅱ、MOEA/D、SPEA2和MOPSO 4种算法分别对模型进行求解,分析比较4种算法的Pareto前沿,结果表明NSGA-Ⅱ算法在求解此问题时综合表现最佳,对比NGSA-Ⅱ算法求得的优化结果与试验结果可知,NSGA-Ⅱ算法具有有效性和合理性。     

考虑外包的平行机调度问题的多目标遗传算法

研究了一类考虑外包的平行机调度问题,目标是使作业外包总成本与最大完工时间同时最小化。通过对该类问题进行形式化描述与分析,设计了一种数字串形式的解的表示方法,其中每位数字表示固定作业对应的机器编号,该方法能够有效缩小解空间,从而提高搜索效率。进而构建了一种带精英策略的非支配遗传算法PD-NSGA-Ⅱ,为该类多目标调度问题提供Pareto最优解集。大量数据实验结果表明,所构造的PD-NSGA-Ⅱ算法能够在合理的时间内有效求解该类调度问题,其解的质量与计算效率均优于SPEA算法。     

小型航空二冲程煤油发动机小负荷试验研究

将1台点燃式二冲程气道喷射汽油机改造为缸内直喷煤油发动机,在3000r/min小负荷工况下进行了点火提前角、喷气结束角、过量空气系数对性能影响的试验研究。结果表明,喷气结束角为50°和80°（上止点前）时,适当增大点火提前角,能使功率增加、油耗降低,但点火提前角不宜过大,否则会造成HC和CO排放量的增加;点火提前角为15°和30°（上止点前）时,适度提前喷气结束时刻,可以使功率增加、油耗和排放降低;另外,偏浓混合气有利于提高功率输出,偏稀混合气有利于降低排放。     

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions.     

A simulated annealing algorithm to find approximate Pareto optimal solutions for the multi-objective facility layout problem

In this article, we consider the facility layout problem which combines the objective of minimization of the total material handling cost and the maximization of total closeness rating scores. Multi-objective optimization is the way to consider the two objectives at the same time. A simulated annealing (SA) algorithm is proposed to find the non-dominated solution (Pareto optimal) set approximately for the multi-objective facility layout problem we tackle. The Pareto optimal sets generated by the proposed algorithm was compared with the solutions of the previous algorithms for multi-objective facility layout problem. The results showed that the approximate Pareto optimal sets we have found include almost all the previously obtained results and many more approximate Pareto optimal solutions.     

考虑布局熵的多态性车间布局稳健优化模型与算法

为满足大规模定制和产业集群下多品种、小批量的市场需求和订单动态波动的客户需求以及车间低成本、高稳健性的布局要求,设计了以单位面积布置成本、单位产品物流成本和布局熵为优化指标的多目标布局优化模型。提出了基于Pareto优化的聚类并行多目标遗传算法,引入模糊C-均值聚类算法以提高Pareto解集分布的多样性与均匀性,设计了多元胞差分进化重插入操作与基于“精英策略”的移民操作,增强了算法全局与局部搜索能力,有效避免了早熟现象。通过典型算例对比,验证了模型和算法的有效性;同时在企业布局实例应用中,获得了既能满足低成本又能将布局熵值控制在理想范围内的车间布局方案,表明模型具有良好的实用性。     

Pareto archived simulated annealing for job shop scheduling with multiple objectives

In this paper, the job shop scheduling problem is studied with the objectives of minimizing the makespan and the mean flow time of jobs. The simultaneous consideration of these objectives is the multi-objective optimization problem under study. A metaheuristic procedure based on the simulated annealing algorithm called Pareto archived simulated annealing (PASA) is proposed to discover non-dominated solution sets for the job shop scheduling problems. The seed solution is generated randomly. A new perturbation mechanism called segment-random insertion (SRI) scheme is used to generate a set of neighbourhood solutions to the current solution. The PASA searches for the non-dominated set of solutions based on the Pareto dominance or through the implementation of a simple probability function. The performance of the proposed algorithm is evaluated by solving benchmark job shop scheduling problem instances provided by the OR-library. The results obtained are evaluated in terms of the number of non-dominated schedules generated by the algorithm and the proximity of the obtained non-dominated front to the Pareto front.