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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。 相似文献
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以鼓式制动器效能因素最大和制动鼓体积最小为目标,对鼓式制动器进行多目标优化设计,利用iSIGHT集成的多目标优化遗传算法,得到多目标优化问题的Pareto最优解集,再利用Pareto最优解集专用后处理工具EDM,找到最合适的折衷解. 相似文献
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为解决钛合金铣削加工存在的高成本、低效率问题,以生产效率最大和刀具寿命消耗率最小为目标,建立了铣削参数优化模型。提出了扩展非支配排序遗传算法(ENSGA-Ⅱ),对种群初始化子过程进行规范化处理,保证了种群的多样性和均匀性;根据Pareto最优原理将非支配概念从目标函数空间扩展到约束空间,使得多目标多约束问题的处理更具有适应性和有效性。实例表明,该方法具有良好的寻优能力,能够获得满意的Pareto解集。借助于该方法,工艺人员可根据优化目标灵活地选择铣削参数,更好地协调生产效率、生产成本和表面质量之间的关系。 相似文献
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利用神经网络实现复杂结构的多目标优化设计 总被引:7,自引:1,他引:6
结构优化设计中常常包含大量的有限元计算。现代多目标优化设计的发展趋势是以 Pareto遗传算法为代表的随机搜索方法 ,能够搜索到整个 Pareto最优解集 ,但计算量相当大 ,如果每次迭代都要涉及有限元计算 ,将是非常耗时的工作。本文在利用 Kolm ogorov多层神经网络映射存在定理的基础上导出的用神经网络进行结构近似分析的方法 ,用均匀试验设计方法选取特征样本点供神经网络训练 ,将神经网络与 Pareto遗传算法有机地结合 ,使多目标优化的计算效率进一步提高 相似文献
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以某功率分流式无级变速器为研究对象,在变速器结构已确定的情况下,并保证齿轮强度的基础下,对功率分流式无级变速器建立以速比范围最大、体积最小和轴中心距最短的齿轮系多目标优化数学模型,将Pareto最优解概念与遗传算法相结合,应用Matlab优化工具箱进行联合优化计算,获得了该问题的最优解集,结果表明,文中提出的方法能够有效地解决多参数多目标的优化问题。 相似文献
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水平型制造协作联盟订单分配多目标优化模型研究 总被引:4,自引:0,他引:4
针对水平型制造协作联盟的订单分配问题,引入了生产负荷参数,建立了最小化综合成本与生产负荷均衡的多目标优化模型。应用改进的非支配排序遗传算法对多目标优化模型进行求解,获得了Pareto最优解集。仿真计算结果表明,所提出的模型和算法能够获得满意的解。 相似文献
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基于量子位实数编码的优化算法及轧制规程多目标优化 总被引:1,自引:0,他引:1
针对热连轧轧制规程优化问题,以等功率裕量和轧制能耗为优化目标函数建立热连轧轧制规程多目标优化模型,提出基于量子位实数编码的热连轧轧制规程多目标优化算法。该算法将免疫遗传算法框架与量子计算思想相结合,采用量子位实数编码,利用量子态干涉进行遗传算子的交叉和变异,同时保证非支配解按拥挤距离选择优势免疫抗体种群,得到 Pareto 全局最优解集。以某轧钢厂热连轧精轧机组为例,验证本文所提及算法的有效性。实例分析表明,所提及的算法在寻优能力和收敛速度上均优于传统的NSGA-II算法,能够获得更好的Pareto解集,有效地解决热连轧轧制规程多目标优化问题,改善了轧制能耗。 相似文献
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A new multi-objective ant colony algorithm for solving the disassembly line balancing problem 总被引:1,自引:1,他引:0
Li-Ping Ding Yi-Xiong Feng Jian-Rong Tan Yi-Cong Gao 《The International Journal of Advanced Manufacturing Technology》2010,48(5-8):761-771
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. 相似文献
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为解决将高维目标变为单目标优化时各子目标不能同时较优,而多目标算法直接用于高维目标优化时又存在难以找到一个有代表性的Pareto非劣解集问题,在某轿车驾驶员侧约束系统的优化过程中提出了乘员损伤准则与多目标算法协同优化的方法。在已有相关损伤准则基础上根据最新版的FMVSS 208和ECE R94法规提出了适合研究问题的损伤准则;以提出的损伤准则为媒介,将一个高维目标优化问题降为一个低维目标优化问题,通过灵敏度分析、实验设计、多项式近似模型筛选出优化设计变量并得到近似模型,用多目标算法NSGA-Ⅱ对近似模型进行计算得到Pareto非劣解集,将得到的Pareto非劣解集中的每个解代入损伤准则损伤值计算公式,升序排列得到各子目标同时较优而损伤值最小的优化解。最终的优化结果表明:该方法很好地解决了乘员约束系统的高维目标优化问题,优化效果明显。 相似文献
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José Elias Claudio Arroyo Ana Amélia de Souza Pereira 《The International Journal of Advanced Manufacturing Technology》2011,55(5-8):741-753
This paper presents a multi-objective greedy randomized adaptive search procedure (GRASP)-based heuristic for solving the permutation flowshop scheduling problem in order to minimize two and three objectives simultaneously: (1) makespan and maximum tardiness; (2) makespan, maximum tardiness, and total flowtime. GRASP is a competitive metaheuristic for solving combinatorial optimization problems. We have customized the basic concepts of GRASP algorithm to solve a multi-objective problem and a new algorithm named multi-objective GRASP algorithm is proposed. In order to find a variety of non-dominated solutions, the heuristic blends two typical approaches used in multi-objective optimization: scalarizing functions and Pareto dominance. For instances involving two machines, the heuristic is compared with a bi-objective branch-and-bound algorithm proposed in the literature. For instances involving up to 80 jobs and 20 machines, the non-dominated solutions obtained by the heuristic are compared with solutions obtained by multi-objective genetic algorithms from the literature. Computational results indicate that GRASP is a promising approach for multi-objective optimization. 相似文献
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研究了基于模糊偏好的多目标粒子群算法,算法将种群的最优解集进行Pareto排序,并动态更新Pareto解集,使其更快速的靠近Pareto前沿,对非劣解进行模糊评价,根据目标偏好的模糊信息,来确定折衷解的满意解。经典算例验证,该算法在计算时间及非劣解质量上,要优于多目标遗传算法。 相似文献
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A simulated annealing algorithm to find approximate Pareto optimal solutions for the multi-objective facility layout problem 总被引:2,自引:2,他引:0
Ramazan Şahin Orhan Türkbey 《The International Journal of Advanced Manufacturing Technology》2009,41(9-10):1003-1018
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. 相似文献
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在电网检修计划编制的基本原则和工作流程下,根据粒子群基本算法原理对电网检修计划编制进行数学建模。考虑检修时间作为自变量矢量,考虑期望缺供电量和检修成本作为其目标函数,考虑检修时间、检修资源和安全性等多个因素作为约束。结合粒子群算法原理和多目标优化理论,全局搜索非支配解集,形成帕累托前沿。最后依据管理者不同的偏好,通过加权计算的方式量化评估各优化目标,从而遴选出最优解,也即最符合决策人员预期的检修计划。通过与非劣排序多目标遗传算法和多目标粒子群算法进行对比,证明本文算法具有较高的实用性,提升了电网运行维护的自动化水平。 相似文献
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基于多目标遗传算法的水陆两栖可变形机器人结构参数设计方法 总被引:7,自引:0,他引:7
水陆两栖可变形机器人是一种兼具变形能力与两栖环境适应能力的新型移动机器人。在其机构设计中,结构参数直接影响该机器人在任务环境中的各项机动性能。针对水陆两栖可变形机器人工作环境复杂性和任务多变性,提出一种基于多目标遗传算法的机器人结构参数设计方法,以得到该型机器人在两栖环境中的最优的综合性能。在水陆两栖可变形机器人陆地环境和水环境中运动学和动力学模型基础上,建立两栖环境中机器人的机动性能指标函数与结构参数的映射关系,并在此基础之上构建面向水陆两栖可变形机器人的结构参数设计的多目标优化问题。利用多目标遗传算法得到该多目标机构参数设计问题的Pareto最优解集,并且通过组合赋权方法确定各目标决策属性的权重,从Pareto最优解集中得到符合设计要求的水陆两栖可变形机器人的各项机构参数最优解,进而指导机器人最终结构参数设计。根据最终得到的结构参数研制出水陆两栖可变形机器人样机Amoeba-II,并在两栖环境下进行样机的各项性能试验,最终验证了基于多目标遗传算法的机器人结构参数设计方法的有效性以及在机器人设计中的适用性。 相似文献
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This study investigated the performance of parallel optimization by means of a genetic algorithm (GA) for lubrication analysis. An air-bearing design was used as the illustrated example and the parallel computation was conducted in a single system image (SSI) cluster, a system of loosely network-connected desktop computers. The main advantages of using GAs as optimization tools are for multi-objective optimization, and high probability of achieving global optimum in a complex problem. To prevent a premature convergence in the early stage of evolution for multi-objective optimization, the Pareto optimality was used as an effective criterion in offspring selections. Since the execution of the genetic algorithm (GA) in search of optimum is population-based, the computations can be performed in parallel. In the cases of uneven computational loads a simple dynamic load-balancing scheme is proposed for optimizing the parallel efficiency. It is demonstrated that the huge amount of computing demand of the GA for complex multi-objective optimization problems can be effectively dealt with by parallel computing in an SSI cluster. 相似文献