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**排序方式：**共有2306条查询结果，搜索用时 62 毫秒

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基于遗传退火算法的装配线设计多目标优化方法

**总被引：3，自引：0，他引：3**针对混装配线设计这一有约束的多目标优化问题，建立了数学模型。将基于Pareto的解的分级方法与Lp-范数形式的非线性机制相组合，构建了基于遗传退火算法多目标优化方法。重点阐述了个体编码、染色体检修、多目标处理机制等关键技术。设计了算法流程图，并开发了优化程序。该方法克服了加权和方法的不足，用模拟退火改善了遗传算法全局寻优性能。计算实例表明，随着迭代次数的增加，每代的非受控点逐渐收敛于Pareto最优边界，是一种混装线设计多目标优化的新方法。 相似文献

4.

The paper deals with the identification of Pareto optimal solutions using GA based coevolution in the context of multiobjective
optimization. Coevolution is a genetic process by which several species work with different types of individuals in parallel.
The concept of cooperative coevolution is adopted to compensate for each of single objective optimal solutions during genetic
evolution. The present study explores the GA based coevolution, and develops prescribed and adaptive scheduling schemes to
reflect design characteristics among single objective optimization. In the paper, non-dominated Pareto optimal solutions are
obtained by controlling scheduling schemes and comparing each of single objective optimal solutions. The proposed strategies
are subsequently applied to a three-bar planar truss design and an energy preserving flywheel design to support proposed strategies. 相似文献

5.

Generally,the process of designing missile con-trol systeminvolves a great deal manual modificationsand trials in order to meet all the design specifica-tions ,such as robust stability, noise rejection, dy-namic response , steady tracking error , etc . So thedesignis an opti mization problemin nature . Becausegenetic algorithm(GA) is a search algorithmbased onthe mechanismof natural selection and natural genet-ics and is different from conventional opti mization.GAsearches for a population of… 相似文献

6.

Z. G. Wang Y. S. Wong M. Rahman J. Sun 《The International Journal of Advanced Manufacturing Technology》2006,31(3-4):209-218

In this paper, the optimization of multi-pass milling has been investigated in terms of two objectives: machining time and production cost. An advanced search algorithm—parallel genetic simulated annealing (PGSA)—was used to obtain the optimal cutting parameters. In the implementation of PGSA, the fitness assignment is based on the concept of a non-dominated sorting genetic algorithm (NSGA). An application example is given using PGSA, which has been used to find the optimal solutions under four different axial depths of cut on a 37 SUN workstation network simultaneously. In a single run, PGSA can find a Pareto-optimal front which is composed of many Pareto-optimal solutions. A weighted average strategy is then used to find the optimal cutting parameters along the Pareto-optimal front. Finally, based on the concept of dynamic programming, the optimal cutting strategy has been obtained. 相似文献

7.

Bing Li T.J. Nye Don R. Metzger 《The International Journal of Advanced Manufacturing Technology》2006,28(1-2):23-30

Tube hydroforming is an attractive manufacturing technology which is now widely used in many industries, especially the automobile
industry. The purpose of this study is to develop a method to analyze the effects of the forming parameters on the quality
of part formability and determine the optimal combination of the forming parameters for the process. The effects of the forming
parameters on the tube hydroforming process are studied by finite element analysis and the Taguchi method. The Taguchi method
is applied to design an orthogonal experimental array, and the virtual experiments are analyzed by the use of the finite element
method (FEM). The predicted results are then analyzed by the use of the Taguchi method from which the effect of each parameter
on the hydroformed tube is given. In this work, a free bulging tube hydroforming process is employed to find the optimal forming
parameters combination for the highest bulge ratio and the lowest thinning ratio. A multi-objective optimization approach
is proposed by simultaneously maximizing the bulge ratio and minimizing the thinning ratio. The optimization problem is solved
by using a goal attainment method. An example is given to illustrate the practicality of this approach and ease of use by
the designers and process engineers. 相似文献

8.

M. K. Rahman 《Structural and Multidisciplinary Optimization》2006,32(1):40-58

This paper presents an optimization algorithm for engineering design problems having a mix of continuous, discrete and integer variables; a mix of linear, non-linear, differentiable, non-differential, equality, inequality and even discontinuous design constraints; and conflicting multiple design objectives. The intelligent movement of objects (vertices and compounds) is simulated in the algorithm based on a Nelder–Mead simplex with added features to handle variable types, bound and design constraints, local optima, search initiation from an infeasible region and numerical instability, which are the common requirements for large-scale, complex optimization problems in various engineering and business disciplines. The algorithm is called an INTElligent Moving Object algorithm and tested for a wide range of benchmark problems. Validation results for several examples, which are manageable within the scope of this paper, are presented herein. Satisfactory results have been obtained for all the test problems, hence, highlighting the benefits of the proposed method. 相似文献

9.

The Vehicle Routing Problem with Time windows (VRPTW) is an extension of the capacity constrained Vehicle Routing Problem
(VRP). The VRPTW is NP-Complete and instances with 100 customers or more are very hard to solve optimally. We represent the
VRPTW as a multi-objective problem and present a genetic algorithm solution using the Pareto ranking technique. We use a direct
interpretation of the VRPTW as a multi-objective problem, in which the two objective dimensions are number of vehicles and
total cost (distance). An advantage of this approach is that it is unnecessary to derive weights for a weighted sum scoring
formula. This prevents the introduction of solution bias towards either of the problem dimensions. We argue that the VRPTW
is most naturally viewed as a multi-objective problem, in which both vehicles and cost are of equal value, depending on the
needs of the user. A result of our research is that the multi-objective optimization genetic algorithm returns a set of solutions
that fairly consider both of these dimensions. Our approach is quite effective, as it provides solutions competitive with
the best known in the literature, as well as new solutions that are not biased toward the number of vehicles. A set of well-known
benchmark data are used to compare the effectiveness of the proposed method for solving the VRPTW. 相似文献

10.

Jesús González Ignacio Rojas Héctor Pomares Fernando Rojas José Manuel Palomares 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2006,10(9):735-748

Fuzzy systems comprise one of the models best suited to function approximation problems, but due to the non linear dependencies between the parameters that define the system rules, the solution search space for this type of problems contains many local optima. Another important issue is the identification of the optimum structure for the fuzzy system. Depending on the complexity of the model, different solutions can be found with different compromises between their approximation error and their generalization properties. Thus, the problem becomes a multi-objective problem with two clearly competing objectives, the complexity of the model and its approximation error.The algorithms proposed in the literature to construct fuzzy systems from examples usually refine iteratively a unique model until a compromise between its complexity and its approximation error is found. This is not an adequate approach for this problem because there exists a set of Pareto-optimum solutions that can be considered equivalent. Thus, we propose the use of multi-objective evolutionary algorithms because, as they maintain a population of potential solutions for the problem, they are able to optimize both objectives simultaneously. We also incorporate some new expert evolutionary operators that try to avoid the generation of worse solutions in order to accelerate the convergence of the algorithm.The proposed algorithm is tested with some target functions widely used in the literature and the results obtained are compared to other approaches. 相似文献