Parallel simple cell mapping for multi-objective optimization

In this article the Parallel Simple Cell Mapping (pSCM) is presented, a novel method for the numerical treatment of multi-objective optimization problems. The method is a parallel version of the simple cell mapping (SCM) method which also integrates elements from subdivision techniques. The classical SCM method exhibits nice properties for parallelization, which is used to speed up computations significantly. These statements are underlined on some classical benchmark problems with up to 10 decision variables and up to 5 objectives and provide comparisons to sequential SCM. Further, the method is applied on illustrative examples for which the method is also able to find the set of local optimal solutions efficiently, which is interesting in multi-objective multi-modal optimization, as well as the set of approximate solutions. The latter is of potential interest for the decision maker since it comprises an extended set of possible realizations of the given problem.     

Polynomial Response Surface Approximations for the Multidisciplinary Design Optimization of a High Speed Civil Transport

Surrogate functions have become an important tool in multidisciplinary design optimization to deal with noisy functions, high computational cost, and the practical difficulty of integrating legacy disciplinary computer codes. A combination of mathematical, statistical, and engineering techniques, well known in other contexts, have made polynomial surrogate functions viable for MDO. Despite the obvious limitations imposed by sparse high fidelity data in high dimensions and the locality of low order polynomial approximations, the success of the panoply of techniques based on polynomial response surface approximations for MDO shows that the implementation details are more important than the underlying approximation method (polynomial, spline, DACE, kernel regression, etc.). This paper selectively surveys some of the ancillary techniques—statistics, global search, parallel computing, variable complexity modeling—that augment the construction and use of polynomial surrogates.     

3-CRS-S并联机构的运动学分析及仿真

目的 对3-CRS-S并联机构进行运动学分析及仿真,验证该机构是否具有优良的运动学性能.方法 运用修正的Kutzbach-Grübler公式对机构进行自由度计算,并分别采用D-H法和数值算法中的粒子群优化算法(PSO)对该3-CRS-S并联机构的位置正逆解进行分析,运用Adams软件对3-CRS-S并联机构进行角度和角速度分析.结果 得出该机构的位置逆解和正解,以及运动学仿真后的角度、角速度图像,该图像曲线均呈现为有规律、周期性的变化,且曲线没有出现有任何断点和突变点,运动范围相对稳定,说明该机构在运行过程中运行平稳.结论 该机构在运动过程中运行平稳,具有良好的运动学性能,在自动化包装机械领域具有广阔的应用前景.     

粒子群优化算法综述

粒子群优化（PSO）算法是一种新兴的优化技术,其思想来源于人工生命和演化计算理论。PSO通过粒子追随自己找到的最好解和整个群的最好解来完成优化。该算法简单易实现,可调参数少,已得到广泛研究和应用。详细介绍了PSO的基本原理、各种改进技术及其应用等,并对其未来的研究提出了一些建议。     

Double global optimum genetic algorithm–particle swarm optimization-based welding robot path planning

Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm–particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.     

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.     

Applicability of surrogates to improve efficiency of particle swarm optimization for simulation-based problems

Particle swarm optimization (PSO) is a population-based, heuristic technique based on social behaviour that performs well on a variety of problems including those with non-convex, non-smooth objective functions with multiple minima. However, the method can be computationally expensive in that a large number of function calls is required. This is a drawback when evaluations depend on an off-the-shelf simulation program, which is often the case in engineering applications. An algorithm is proposed which incorporates surrogates as a stand-in for the expensive objective function, within the PSO framework. Numerical results are presented on standard benchmarking problems and a simulation-based hydrology application to show that this hybrid can improve efficiency. A comparison is made between the application of a global PSO and a standard PSO to the same formulations with surrogates. Finally, data profiles, probability of success, and a measure of the signal-to-noise ratio of the the objective function are used to assess the use of a surrogate.     

舰艇武器布置问题的一种协同优化算法

针对舰艇武器布置问题的特点，提出了一种基于粒子群优化和分类器系统的协同优化算法，以粒子群优化进行优化计算，用分类器系统消除约束．计算实例表明，该算法能较好地实现优化计算，并能节省大量的计算时间．     

Parallel Line Search in Method of Feasible Directions

In this paper the line search procedure within the method of feasible directions is parallelized and used in the solution of constrained structural optimization problems. As the objective function is linear in the variables, the step size problem reduces to a zero finding problem. That is, the step size is the distance along the direction vector to the nearest constraint boundary. Zero finding is accomplished in two steps—a march along the direction vector to bracket the zero followed by an interval reduction scheme. Both these steps are parallelized using MPI for message passing. When implemented on a cluster of workstations, for a convergence parameter of 10^–6, the time for optimization of composite pressure vessel reduces from 3 hours to hour when 64 processors are utilized, with a speedup of 7.0.     

多臂机提综臂辅助旋铆并联机器人优化设计

提综臂是多臂机上实现凸轮开口运动的关键核心部件.为满足多臂机提综臂的自动化加工需求,设计了一种新型辅助旋铆并联机器人,以实现在提综臂旋铆加工过程中对零部件的高速、高精度抓取和摆放,显著提升生产效率和保证加工质量.首先,介绍了新型辅助旋铆并联机器人机构的拓扑结构特征,并结合旋量理论及修正的G-K(Grübler-Kutz...     

一种面向卷烟混合生产线的优化调度模型研究

目的 基于多品种、小批量的生产制造模式,在规定的8 h工作之内,快速有效地生产出多品种、多批量的卷烟,并对所需物料进行准确调度,达到经济效益最大化的目的。方法 针对某烟草生产企业订单需要生产6种型号的品牌香烟,通过分析卷烟生产线的工艺流程,提出一种解决卷烟厂车间资源优化调度的CSS模型,该模型可以根据产品之间的销售需求情况来匹配生产线资源配置,按需求比采用粒子群优化算法计算出单次投料后混合生产香烟所需的最小时间与最大收益。结果 将所得方案进行综合对比后,计算得出在规定工期内,生产香烟获取利益的最优分配方案,在迭代在10次以内时已完成了优化过程,最大获利为3.65万元。结论 该优化模型通过改变相关工艺参数能够实现对不同混合生产线的资源调度优化,并对其他制造行业提供借鉴价值。