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1.
对活塞连杆组的选配进行数学建模,并用遗传算法对活塞连杆组的组合优化进行了研究.在用遗传算法进行组合优化时,提出了采用惩罚函数法把约束条件转移到目标函数上,从而使整个问题转化为无约束问题.通过系统模拟,比较了基本遗传算法、进化规划法和精英保留遗传算法三种遗传算法,并最终确定进化规划算法为最适合活塞连杆组组合优化的算法.  相似文献   

2.
林国玺  宣慧玉 《工业工程》2006,9(1):107-111
考虑到遗传算法本身存在易"早熟收敛"的缺陷,提出将模拟退火算法中的Metropolis接受准则引入到遗传算法的群体更新策略中,并将其应用于物流管理中的带容量约束和时间窗的车辆路径问题(CVRPTW).针对Solomon提出的几个标准问题,从数值计算上探索了遗传算法和模拟退火算法融合后的优化能力,获得了满意的效果.  相似文献   

3.
    
通过对某复杂产品制造企业现有数控加工流程进行分析,建立了一个仿真优化集成框架,并提出了一种新的建模求解思路:首先,以通用仿真工具Arena为基础,建立其加工车间的仿真模型;其次,将遗传算法与启发式方法相结合,以遗传算法优化各机器前工件加工的优先顺序,并在仿真过程中,结合启发式规则和一种重调度策略实现动态实时调度;最后,应用面向对象的编程思想,借助Arena类库,设计了一个作业排序问题的仿真优化集成框架.通过实例验证了算法的有效性.  相似文献   

4.
从供应链集成的角度出发,基于多目标规划,根据排队论探讨了随机性需求下多级分销网络设计与库存控制的整合优化问题,提出了多级分销网络设计和库存控制整合优化的多目标规划模型.针对遗传算法收敛速度慢、易陷入局部最优等缺点,采用了基于并列选择法的遗传-模拟退火算法混合优化策略.实验证明,模拟退火算法提高了遗传算法的全局搜索能力,改善了遗传算法的求解性能.  相似文献   

5.
可靠性优化的一种新的算法   总被引:9,自引:0,他引:9       下载免费PDF全文
 建立了可靠性冗余优化模型,提出了一种基于粒子群优化算法的可靠性优化的新方法,该方法结合了遗传算法的思想。实例结果表明,粒子群算法比模拟退火算法和遗传算法效果好。  相似文献   

6.
对于"一刀切"矩形件优化排样问题,采用遗传算法与蚁群算法的混合算法进行研究.针对两种算法的传统混合策略和现有混合策略的不足,对两种算法的混合策略进行改进,并利用种群本身的染色体适值来判断种群进化是否停滞,确定了算法的最佳融合时机.对具体算例的分析验证表明,改进后的混合策略可有效减少算法的冗余迭代次数,提高搜索速度,是一种行之有效的排样算法.  相似文献   

7.
蚁群算法结合微遗传算法优化设计多层雷达吸波涂层   总被引:4,自引:2,他引:2  
晁坤  刘运林  杨儒贵 《功能材料》2008,39(6):1052-1055
针对多层雷达吸波涂层的结构特点,提出蚁群算法结合微遗传算法的全新优化设计方案.在给定最大厚度的约束条件下,在2~18GHz频率范围内,采用对不同频段给定不同吸收率的方案,对多层雷达吸波涂层进行了优化设计.实验数据表明,该融合算法获得的解比微遗传算法更为精确.这种融合算法为求解类似的组合约束优化问题提供了一种新的方法.  相似文献   

8.
服装样片裁剪空行程路径是影响裁床裁剪效率的重要因素,裁剪路径优化有助于提高裁剪效率。针对裁剪空行程优化问题,先构建了裁剪空行程路径的数学模型,结合遗传算法和蚁群算法的优势,提出了遗传蚁群混合算法优化裁剪空行程。算法先利用遗传算法的快速搜索能力建立蚁群算法的初始信息素分布,然后采用蚁群算法求精确解,最后建立仿真实验验证混合算法的可行性,与基本遗传算法相比,混合算法具有较优的全局优化性能。  相似文献   

9.
崔长彩  李兵 《测试技术学报》2004,18(Z2):137-140
本文应用现代优化算法--遗传算法实现了对机械产品形位误差的精确计算.着重介绍了遗传算法实现技术中的关键问题,采用了统一的极值优化评定模型、与目标函数呈倒数关系的适应度函数、最优保留法和赌轮法相结合的选择策略、有性算术交叉策略和阈值自适应的高斯变异机制等.实例验算证明算法设计合理,计算精度较高.  相似文献   

10.
构建了一个智能优化平台,包括粒子群算法、蚁群算法、遗传算法、模拟退火算法、混沌算法和复合形法.该平台包括4个模块,分别是优化问题选择,优化参数设置,优化过程显示以及优化结果输出.将该优化平台应用在RGB LED混合白光的优化设计中,对混合照明模块中发光效率和显色指数进行优化,给出了混合照明模块中发光效率和显色指数的计算...  相似文献   

11.
This paper presents a comparison of results for optimization of captive power plant maintenance scheduling using genetic algorithm (GA) as well as hybrid GA/simulated annealing (SA) techniques. As utilities catered by captive power plants are very sensitive to power failure, therefore both deterministic and stochastic reliability objective functions have been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. The significant contribution of this paper is to incorporate stochastic feature of generating units and that of load using levelized risk method. Another significant contribution of this paper is to evaluate confidence interval for loss of load probability (LOLP) because some variations from optimum schedule are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are incorporated in terms of near-optimum schedules obtained from hybrid GA/SA technique during the final stages of convergence. Case studies corroborate that same optimum schedules are obtained using GA and hybrid GA/SA for respective deterministic and stochastic formulations. The comparison of results in terms of interval of confidence for LOLP indicates that levelized risk method adequately incorporates the stochastic nature of power system as compared with levelized reserve method. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power.  相似文献   

12.
This paper discusses the optimum design method of the weaving structure of three-dimensional (3-D) reinforced composites. We propose the design method which combines the genetic algorithms (GA) and the finite element analysis. GA is one of the optimization techniques for the combinatorial optimization problem. In the finite element analysis, we used the original structure model which can express the fiber arrangement state in the 3-D composites faithfully. In this study, the original weaving structure model is constructed by combining the basic structure which has the fiber bundle and the cubic grid of resin. From analysis results, in the small design region, we can obtain the optimum weaving structure. Moreover, we proposed a new genetic operation, to design the weaving structure at the larger design region. These operations aim to prevent the failure of the partial weaving structure in the analytical model as much as possible. From the analysis results, the optimum weaving structure is obtained at the large design region, similar to above results. Consequently, it seems that the proposed method enables the design of the optimum weaving structure in the 3-D composites.  相似文献   

13.
In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error?=?3.362 and root mean square error?=?0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.  相似文献   

14.
This investigation presents an optimization of laminated cylindrical panels based on fundamental natural frequency. Also, trends of change in optimum stacking sequence while the proportions of structures vary, are studied which can be insightful for design purposes. A displacement based finite element model is used, in order to extract fundamental natural frequencies of T300/5208 Carbon/Epoxy cylindrical panels. To obtain optimum designs, the Globalized Bounded Nelder–Mead (GBNM) algorithm is employed. Predictions are compared with the results of Genetic Algorithm (GA) method and show faster and more accurate convergence to the global optimum, while variables are continuous in GBNM and discrete in GA. Moreover, verification of novel convergence criteria to ameliorate local searcher in GBNM is examined.  相似文献   

15.
Bo Liao 《工程优选》2013,45(4):381-396
The success of both genetic algorithms (GA) and the Luus–Jaakola (LJ) optimization procedure in engineering optimization and the desire for efficient optimization methods arising from practical experience make the comparison of these two methods necessary. The GA and the LJ optimization procedure are compared in terms of convergence speed and reliability in obtaining the global optimum. Instead of using the number of function evaluations, this study uses computation time for comparison of convergence speed, which is more precise. Although for some problems, such as parameter estimation for the catalytic cracking process of gas oil, both GA and LJ converge to the optimum rapidly and show high reliability; in most cases, the LJ optimization procedure was found to be faster than GA and exhibited higher reliability in obtaining the global optimum. Furthermore, the LJ optimization procedure is easier to program.  相似文献   

16.
A new tire design procedure capable of determining the optimum tire construction was developed by combining a finite element method approach with mathematical programming and a genetic algorithm (GA). Both procedures successfully generated optimized belt structures. The design variables in the mathematical programming were belt angle and belt width. Using the merits of a GA which enabled the use of discrete variables, the design variables in the GA were not only the topology of the belt and belt angle but also the belt material. Furthermore, a discrete objective function such as the number of parts could be optimized in the GA. The optimized structure obtained by the GA was verified to increase the cornering stiffness more than 15 percent as compared with the control structure in an indoor drum test.  相似文献   

17.
The transformer manufacturing cost minimisation (TMCM), also known as transformer design optimisation, is a complex constrained mixed-integer non-linear programming problem with discontinuous objective function. This paper proposes an innovative method combining genetic algorithm (GA) and finite element method (FEM) for the solution of TMCM problem. The main contributions of the proposed method are: (a) introduction of an innovative recursive GA with a novel external elitism strategy associated with variable crossover and mutation rates resulting in an improved GA, (b) adoption of two particular finite element models of increased accuracy and high computational speed for the validation of the optimal design by computing the no-load loss and impedance and (c) combination of the innovative recursive GA with the two particular finite element models resulting in a proposed GA-FEM model that finds the global optimum, as concluded after several tests on actual transformer designs, while other existing methods provided suboptimal solutions that are 3.1?5.8% more expensive than the optimal solution.  相似文献   

18.
遗传算法的改进策略及其在桥梁抗震优化设计中的应用效果   总被引:14,自引:3,他引:11  
谢楠  陈英俊 《工程力学》2000,17(3):31-36
本文论述了采用遗传算法进行结构优化设计时遇到的诸如计算量大、早熟收敛和边界探索不足等棘手问题,提出了三个解决对策,编制了计算程序,其在桥梁抗震代化设计中的应用效果表明,改进后的遗传算法不但提高了计算速度,而且在尽可能短的时间内找到最好的优化解。  相似文献   

19.
This paper describes the application of an integrated Genetic Algorithm (GA)/Discrete Event Simulation model for selecting optimum values for Critical Point Policy (CPP) hedging time and buffer size parameters. The CPP is shown to perform well, when compared with the Critical Ratio priority rule, in terms of improving service levels, particularly when subject to conditions where buffer sizes and Takt times are required to be small. The technique developed involves buffer sizes being chosen by a GA according to a constraint on the total storage space available within the system. A method is described for reducing the number of variables that the GA needs to deal with, hence, improving the efficiency of the GA optimization process. The development and application work reported also provides further understanding into how and when the CPP should be applied.  相似文献   

20.
《Composites Part A》2007,38(8):1932-1946
The optimization of injection gate locations in liquid composite molding processes by trial and error based methods is time consuming and requires an elevated level of intuition, even when high fidelity physics-based numerical models are available. Optimization based on continuous sensitivity equations (CSE) and gradient search algorithms focused towards minimizing the mold infusion time gives a robust approach that will converge to local optima based on the initial solution. Optimization via genetic algorithms (GA) utilizes natural selection as a means of finding the optimal solution in the global domain; the computed solution is at best, close to the global optimum with further refinement still possible. In this paper, we present a hybrid global–local search approach that combines evolutionary GAs with gradient-based searches via the CSE. The hybrid approach provides a global search with the GA for a predetermined amount of time and is subsequently further refined with a gradient-based search via the CSE. In our hybrid method, we utilize the efficiency of gradient searches combined with the robustness of the GA. The resulting combination has been demonstrated to provide better and more physically correct results than either method alone. The hybrid method provides optimal solutions more quickly than GA alone and more robustly than CSE based searches alone. A resin infusion quality parameter that measures the deviation from a near uniform mold volume infusion rate is defined. The effectiveness of the hybrid method with a modified objective function that includes both the infusion time and the defined mold infusion quality parameter is demonstrated.  相似文献   

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