基于DTC-MOPSO算法的焊接机器人路径规划

点焊机器人在汽车白车身焊接中的应用大大提高了企业的生产效率,本文从焊接路径长度和能量两方面进行焊接机器人多目标路径规划．为了很好地解决这个问题,本文对一种新型多目标粒子群算法（三态协调搜索多目标粒子群优化算法）进行改进,得到适合于求解离散多目标优化问题的离散化三态协调搜索多目标粒子群算法（DTC-MOPSO）．通过和两个经典的优化算法比较,DTC-MOPSO算法在分散性和收敛性方面都有很好的优化性能．最后运用Matlab机器人工具箱对机器人的运动学、逆运动学以及逆动力学进行分析以求解机器人的路径长度和能耗,并将改进的算法应用于焊接机器人路径规划中,结果显示规划后的路径明显优于另外两种算法.     

Constrained multi-objective optimization algorithms: Review and comparison with application in reinforced concrete structures

Engineering design problems are often multi-objective in nature, which means trade-offs are required between conflicting objectives. In this study, we examine the multi-objective algorithms for the optimal design of reinforced concrete structures. We begin with a review of multi-objective optimization approaches in general and then present a more focused review on multi-objective optimization of reinforced concrete structures. We note that the existing literature uses metaheuristic algorithms as the most common approaches to solve the multi-objective optimization problems. Other efficient approaches, such as derivative-free optimization and gradient-based methods, are often ignored in structural engineering discipline. This paper presents a multi-objective model for the optimal design of reinforced concrete beams where the optimal solution is interested in trade-off between cost and deflection. We then examine the efficiency of six established multi-objective optimization algorithms, including one method based on purely random point selection, on the design problem. Ranking and consistency of the result reveals a derivative-free optimization algorithm as the most efficient one.     

Forward kinematics,performance analysis,and multi-objective optimization of a bio-inspired parallel manipulator

In this paper, a bio-inspired parallel manipulator with one translation along z-axis and two rotations along x- and y- axes is developed as the hybrid head mechanism of a groundhog robotic system. Several important issues including forward kinematic modeling, performance mapping, and multi-objective improvement are investigated with specific methods or technologies. Accordingly, the forward kinematics is addressed based on the integration of radial basis function network and inverse kinematics. A novel performance index called dexterous stiffness is defined, derived and mapped. The multi-objective optimization with particle swarm algorithm is conducted to search for the optimal dexterous stiffness and reachable workspace.     

基于物理规划的航空保障多目标优化模型

根据作战需求确定优化权衡目标,以使用保障需求和技术可行性为约束条件,以保障能力指标集合,以备件储备量的组合为决策变量,建立多个单目标优化模型和多目标优化模型,提出了基于物理规划的航空保障能力多目标优化方法,结合粒子群算法得到了满足设计者偏好的最佳航材备件方案.设计了航空保障能力系数的满意等级,构造了各优化目标的偏好函数和综合偏好函数,使整个设计过程更加灵活地反映决策者偏好,减轻大规模多目标设计问题的计算负担,使军用机群的保障能力更适合实际作战要求,同时通过对比单目标优化的结果,验证了算法的有效性.     

Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures

We present a new, generic method/model for multi-objective design optimization of laminated composite components using a novel multi-objective optimization algorithm developed on the basis of the Quantum behaved Particle Swarm Optimization (QPSO) paradigm. QPSO is a co-variant of the popular Particle Swarm Optimization (PSO) and has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are – the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; Failure Mechanism based Failure criteria, Maximum stress failure criteria and the Tsai–Wu Failure criteria. The optimization method is validated for a number of different loading configurations – uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences as well as fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Also, the performance of QPSO is compared with the conventional PSO.     

Optimal design of a parallel assembling robot with large payload-to-mass ratio

Assembly in a confined space, such as the cabin of an aircraft or train, demands the assembling device to be with compact structure, satisfactory kinematics and excellent load carrying capability. A six degree-of-freedom (DoF) parallel robot is proposed and designed for such assembling tasks in this paper. Specially, internal structure changes introduced by topology optimization are considered and the multi-objective optimization reaching large load-to-mass ratio is implemented. First, external dimensions of the base and the ratio of the remaining volume to the complete volume are the inputs. Once a set of input variables are given, the topology optimization will be performed by FEA software to form the structure of the base. The stiffness and mass of the base, being the outputs, are obtained numerically by software. Then, the meta models are established by the response surface model (RSM) method. On this basis, stiffness and mass models of the robot are built by the semi-analytical method. The optimal design is implemented by Pareto-based multi-objective optimization. Different arrangements of the objectives are compared. The results show that kinematic indices on the Pareto fronts are all at a satisfactory level. The optimization having payload-to-mass ratio as objective leads to the optimum with higher stiffness along z-axis and smaller mass. The design 6-DoF parallel assembly robot can carry up to 42.06 kg loads while the mass is only 12.002 kg.     

燃料电池有轨电车能量管理Pareto多目标优化

节能环保的出行方式得到政府的大力推广, 其中燃料电池混合动力有轨电车由于可无网运行且节能环保而备受关注.为了改善燃料电池/超级电容/动力电池大功率有轨电车的燃料经济性与系统耐久性, 提出一种有轨电车能量管理策略(Energy management strategy, EMS)的多目标优化方法. 首先以氢燃料消耗量和能量源性能衰减率作为评价指标, 建立多目标成本函数. 由于两个指标很难在同一个等式中评价, 设计了基于状态机与非支配排序的能量管理Pareto多目标优化方法, 获得了有轨电车能量管理策略Pareto非劣解集, 并分析了能量管理策略的目标功率参数对性能指标的影响规律, 进而遴选出兼顾燃料经济性与系统耐久性的综合最优解. 结果表明, 与功率跟随策略和基于遗传算法优化策略相比, 该能量管理优化方法的燃料经济性分别提高了29.4 %和2.4 %.     

考虑元素烧损和仓储成本的特种铝合金配料优化

配料计算是特种铝合金熔炼的重要准备工序,直接影响产品的最终性能.为提高产品质量和配料效率,降低原料和仓储物流成本,建立考虑元素烧损和旧料循环利用等因素的特种铝合金配料优化模型.针对该模型的目标多样性和非线性等特点,设计以投料量和投料时间为决策变量的实数编码规则,提出一种基于第3代非支配遗传算法并融入分布式估计策略的多目标优化算法用于求解该模型.通过基于真实生产数据的仿真实验进行模型和算法验证.实验结果表明,所提出模型能够有效地解决特种铝合金配料优化问题,与传统的多目标优化算法相比,所提出求解算法能够获得更优的结果.     

基于NSGA-II 算法的平面并联机构动态性能多目标优化

针对高速高加速度的平面并联机构,采用面向控制系统的机构设计方法,对机械结构进行优化设计． 分别在运动学、动力学层面上提出多个性能指标,以其作为目标函数和约束条件建立了标准的多目标优化模型．基 于NSGA-II 算法求解多目标优化问题,进行尺度综合．最终结果表明,优化后的机构能较大程度上消除系统耦合, 提升动态性能,为高速控制系统设计提供良好的机械硬件平台．     

Multi-directional local search

This paper introduces multi-directional local search, a metaheuristic for multi-objective optimization. We first motivate the method and present an algorithmic framework for it. We then apply it to several known multi-objective problems such as the multi-objective multi-dimensional knapsack problem, the bi-objective set packing problem and the bi-objective orienteering problem. Experimental results show that our method systematically provides solution sets of comparable quality with state-of-the-art methods applied to benchmark instances of these problems, within reasonable CPU effort. We conclude that the proposed algorithmic framework is a viable option when solving multi-objective optimization problems.     

A new multi-objective programming scheme for topology optimization of compliant mechanisms

This paper presents an alternative method in implementing multi-objective optimization of compliant mechanisms in the field of continuum-type topology optimization. The method is designated as “SIMP-PP” and it achieves multi-objective topology optimization by merging what is already a mature topology optimization method—solid isotropic material with penalization (SIMP) with a variation of the robust multi-objective optimization method—physical programming (PP). By taking advantages of both sides, the combination causes minimal variation in computation algorithm and numerical scheme, yet yields improvements in the multi-objective handling capability of topology optimization. The SIMP-PP multi-objective scheme is introduced into the systematic design of compliant mechanisms. The final optimization problem is formulated mathematically using the aggregate objective function which is derived from the original individual design objectives with PP, subjected to the specified constraints. A sequential convex programming method, the method of moving asymptotes (MMA) is then utilized to process the optimization evolvement based on the design sensitivity analysis. The main findings in this study include distinct advantages of the SIMP-PP method in various aspects such as computation efficiency, adaptability in convex and non-convex multi-criteria environment, and flexibility in problem formulation. Observations are made regarding its performance and the effect of multi-objective optimization on the final topologies. In general, the proposed SIMP-PP method is an appealing multi-objective topology optimization scheme suitable for “real world” problems, and it bridges the gap between standard topological design and multi-criteria optimization. The feasibility of the proposed topology optimization method is exhibited by benchmark examples.     

羽毛球机器人机械臂运动轨迹多目标规划

为了实现羽毛球机器人机械臂高速连续平滑地击打羽毛球动作,提出了一种新的多目标机械臂运动轨迹优化模型。首先,该轨迹优化模型根据D-H运动学模型,通过坐标变换建立机械臂的位姿表达式。然后,采用牛顿下山法求出给定路径关键点的运动学逆解集,并基于最短路径算法从逆解集中求出最优解。最后,根据所求出最优解,采用三次样条插值建立电机转角函数,以实现机械臂的连续平滑运动。实验结果表明：新的轨迹优化模型能够有效地降低电机能耗和提高转动效率,从而保证了机械臂响应速度。     

汽车零部件立体仓库货位优化问题研究

以立体仓库库存为研究对象,从物流仓储管理角度,研究了货位分配优化问题。分 析了汽车零部件货位布局优化原则,建立多目标货位分配优化数学模型,对遗传算法进行了算子 设计,运用Matlab 软件实现模型的求解,得出可行的货位优化方案。最后结合实例进行多目标 货位优化数学模型求解及应用,并以三维仿真图形展示了优化效果,验证了所设计的遗传算法的 有效性,对同类问题的解决具有参考意义。     

Bio-inspired optimization for the molecular docking problem: State of the art,recent results and perspectives

Molecular docking is a Bioinformatics method based on predicting the position and orientation of a small molecule or ligand when it is bound to a target macromolecule. This method can be modeled as an optimization problem where one or more objectives can be defined, typically around an energy scoring function. This paper reviews developments in the field of single- and multi-objective meta-heuristics for efficiently addressing molecular docking optimization problems. We comprehensively analyze both problem formulations and applied techniques from Evolutionary Computation and Swarm Intelligence, jointly referred to as Bio-inspired Optimization. Our prospective analysis is supported by an experimental study dealing with a molecular docking problem driven by three conflicting objectives, which is tackled by using different multi-objective heuristics. We conclude that genetic algorithms are the most widely used techniques by far, with a noted increasing prevalence of particle swarm optimization in the last years, being these last techniques particularly adequate when dealing with multi-objective formulations of molecular docking problems. We end this experimental survey by outlining future research paths that should be under target in this vibrant area.     

铝电解生产智能优化制造研究综述

铝电解行业具有战略基础地位,面临着诸多挑战性难题,包括原料来源复杂使得工况难以稳定优化运行、多目标协同优化难度大、控制决策智能化水平和数据利用率低以及铝电解企业在内外环境的不确定性影响下难以实时做出正确决策等.为了解决上述问题,本文提出构建一种集铝电解智能分布式感知系统、系列槽智能协同优化控制系统、大型槽智能优化控制系统、智能安全运行监控系统和虚拟制造系统于一体的铝电解智能优化制造系统的方法.同时提出了铝电解制造系统的未来发展目标和愿景功能,并给出了相关研究方向.最后给出了技术发展规划,提出中短期规划和中长期规划"两步走"战略,并对铝电解生产智能优化制造系统发展前景作出展望.     

基于灰色综合关联分析的多目标优化方法

针对现有多目标优化方法存在的搜索性能弱、效率低等问题,提出一种基于灰色综合关联分析的多目标优化方法.该多目标优化方法采用单目标优化算法构建高质量的参考序列,计算参考序列与优化解的目标函数值序列之间的灰色综合关联度,定义基于灰色综合关联度的解支配关系准则,将灰色综合关联度作为多目标优化算法的适应度值.以带顺序相关调整时间的多目标流水车间调度问题作为应用对象,建立总生产成本、最大完工时间、平均流程时间及机器平均闲置时间的多目标函数优化模型.提出基于灰色关联分析的多目标烟花算法,对所建立的多目标优化模型进行优化求解.仿真实验表明,所提出多目标烟花算法的性能优于3种基于不同多目标优化方法的烟花算法及两种经典多目标算法,验证了所提出的多目标优化方法及多目标算法的可行性和有效性.     

基于混合预测策略与改进社会学习优化算法的动态多目标优化方法

近年来,国内外学者针对基于预测的动态多目标优化算法开展了深入研究,并提出了一系列有效的算法,然而已有的研究工作通常采用单一的预测策略,使得算法不能有效地应对剧烈的环境变化。针对上述问题,提出了一种基于混合预测策略与改进社会学习优化算法的动态多目标优化方法。具体地,当环境发生变化时,该方法首先基于代表性个体预测策略生成一部分群体;其次,基于拐点预测策略生成一部分新群体,特别地,为了提高种群的多样性,根据算法迭代的历史信息和环境变化情况随机地生成一定数量的新个体;为了提高问题的求解效率,对社会学习优化算法进行了改进,为每个进化空间设计了适用于动态多目标优化问题的算子;最后,将混合预测策略与改进的社会学习优化算法结合,构成了一种新的动态多目标优化方法。以FDA、DMOP和F函数集作为实验测试函数集,与四种代表性算法进行了性能对比;并以反向世代距离(inverted generational distance, IGD)对该方法的性能进行了深入的分析。实验结果表明所提方法具有较好的收敛性和鲁棒性。     

基于改进混沌粒子群算法的薄壁件铣削参数优化

为提高薄壁框体结构件铣削加工精度及加工效率,提出一种薄壁框体结构件铣削加工工艺参数优化方法.针对标准粒子群算法存在易陷入局部最优解,且不能自适应调整权重系数等问题,将混沌算法与多目标粒子群算法结合,建立了以铣削力和单位时间材料去除率为优化目标,以铣削4因素为优化变量,以机床主轴转速、进给量、铣削深度和表面粗糙度为约束条...     

An efficient metamodel-based multi-objective multidisciplinary design optimization framework

This paper presents an efficient metamodel-based multi-objective multidisciplinary design optimization (MDO) architecture for solving multi-objective high fidelity MDO problems. One of the important features of the proposed method is the development of an efficient surrogate model-based multi-objective particle swarm optimization (EMOPSO) algorithm, which is integrated with a computationally efficient metamodel-based MDO architecture. The proposed EMOPSO algorithm is based on sorted Pareto front crowding distance, utilizing star topology. In addition, a constraint-handling mechanism in non-domination appointment and fuzzy logic is also introduced to overcome feasibility complexity and rapid identification of optimum design point on the Pareto front. The proposed algorithm is implemented on a metamodel-based collaborative optimization architecture. The proposed method is evaluated and compared with existing multi-objective optimization algorithms such as multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II), using a number of well-known benchmark problems. One of the important results observed is that the proposed EMOPSO algorithm provides high diversity with fast convergence speed as compared to other algorithms. The proposed method is also applied to a multi-objective collaborative optimization of unmanned aerial vehicle wing based on high fidelity models involving structures and aerodynamics disciplines. The results obtained show that the proposed method provides an effective way of solving multi-objective multidisciplinary design optimization problem using high fidelity models.     

基于多点非均匀变异的多目标极值优化算法研究

多目标进化算法因其在解决含有多个矛盾目标函数的多目标优化问题中的强大处理能力,正受到越来越多的关注与研究。极值优化作为一种新型的进化算法,已在各种离散优化、连续优化测试函数以及工程优化问题中得到了较为成功的应用,但有关多目标EO算法的研究却十分有限。本文将采用Pareto优化的基本原理引入到极值优化算法中,提出一种求解连续多目标优化问题的基于多点非均匀变异的多目标极值优化算法。通过对六个国际公认的连续多目标优化测试函数的仿真实验结果表明：本文提出算法相比NSGA-II、 PAES、SPEA和SPEA2等经典多目标优化算法在收敛性和分布性方面均具有优势。