دانلود مقاله ISI انگلیسی شماره 8463
عنوان فارسی مقاله

انتخاب روش های بهینه سازی موازی برای مدیریت مالی تحت شرایط عدم قطعیت

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
8463 2000 23 صفحه PDF سفارش دهید 8110 کلمه
خرید مقاله
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عنوان انگلیسی
Selected parallel optimization methods for financial management under uncertainty
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Parallel Computing, Volume 26, Issue 1, January 2000, Pages 3–25

کلمات کلیدی
- بهینه سازی تصادفی - محاسبات موازی - برنامه ریزی مالی
پیش نمایش مقاله
پیش نمایش مقاله انتخاب روش های بهینه سازی موازی برای مدیریت مالی تحت شرایط عدم قطعیت

چکیده انگلیسی

A review of some of the most important existing parallel solution algorithms for stochastic dynamic problems arising in financial planning is the main focus of this work. Optimization remains the most difficult, time and resource consuming part of the process of decision support for financial planning under uncertainty. However, other parts of a specialized decision support system (DSS) are also briefly outlined to provide appropriate background. Finally, financial modeling is but one of the possible application fields of stochastic dynamic optimization. Therefore the same fairly general methods described here are also useful in many other contexts. Authors hope that the overview of this application field may be of interest to readers concerned with development of parallel programming paradigms, methodology and tools. Therefore special care was taken to ensure that the presentation is easily understandable without much previous knowledge of theory and methods of operations research.

مقدمه انگلیسی

Guyed towers are used frequently to support telecommunication antennas. They consist of a slender tall mast that is supported laterally by several sets of inclined pre-tensioned guys attached at a few levels along its height. The guys around the mast are usually spaced at equal angles. The optimization of these structures may be a complex task since the structural behavior of guyed towers is complicated due to the inherent nonlinearity of the guys, and since turbulent wind is the dominant loading that essentially determines the design of the towers. Despite the fact that few typical designs prevail in the engineering practice (e.g., see Fig. 1 and Fig. 2: triangular plan of horizontal cross-section of mast, triangular bracing with lateral stiffeners at the guys attachment levels, etc.), there are many factors that can be chosen to optimize a tower design that seeks to preserve all the safety and serviceability requirements while maintaining a minimal mass of the whole structure. For example, the set of design variables may consist of the number of guys’ clusters, the heights of their attachment positions along the height of the mast, pre-tension forces in the guys, distances of the guys’ foundations from the axis of the mast, width of mast in the cross-section, height of the typical section of the mast, and, finally, of all dimensions of mast elements. Such an optimization problem can be categorized as the mixed topology-sizing optimization.Evidently, the objective function cannot be expressed in a closed form in terms of design variables. Moreover, it may present a large number of local minima points; therefore global optimization methods capable of finding the global minimum or at least a rational solution are needed. If more than several design variables are considered, only the stochastic global optimization algorithms may render satisfactory results. Engineers have worked to optimize slender structures subjected to the wind loading for many years. Bell and Brown [1] optimized guyed towers using the deterministic global optimization technique on the basis of the Branch-and-Bound algorithm. However, it led to only local optimum points since each design variable was considered separately. Thornton et al. [2] developed a computer program for the mass optimization of towers under deflection constraints-static wind loads were treated. Uys et al. [3] proposed a procedure for optimizing steel towers under dynamic wind loading after the Eurocode 1 [4]. Venanzi and Materazzi [5] proposed a multi-objective optimization method for wind-excited structures based on a stochastic simulated annealing algorithm. The objective function involved two competing factors: the sum of the squares of the nodal displacements and the in-plan width of the structure. However, only three factors were included in the set of design variables. The optimization of masts and towers is a complicated global optimization problem. Therefore, considerable attention has been given to the development of effective stochastic problem-oriented optimization algorithms. Zhang and Li [6] combine the shape and sizing optimization of a transmission tower structure in two levels using the ant colony algorithm (ACA). Luh and Lin [7] employed modified binary particle swarm optimization (PSO) for the topology optimization of truss structures, and subsequently the size and shape of members were optimized using the attractive and repulsive particle swarm optimization. Kaveh and Talatahari [8] find that the PSO is more effective than ACA and the harmony search scheme for optimizing truss structures. Deng et al. [9] and Guo and Li [10] proposed several successful modifications of genetic algorithms (GA) to optimize tapered masts and transmission towers. This paper poses the topology-sizing optimization problem of guyed mast as a single-level single-objective global optimization problem using the genetic algorithm to determine the minimum weight design. The set of the design variables may contain up to 10 parameters of different natures. The genetic algorithms [11] have been chosen due to their effectiveness in different engineering application areas [12] and ease of implementation. Another advantage of GA is its stochastic character: the optimization problem has to be solved a sufficient number of times, each time starting from a randomly formed population of individuals in order to exclude the influence of deviation of the results. This usually leads to several optimum points with close objective function values but corresponding to different topologies of the mast; the designer now can choose the relevant topology. The additional problems, e.g., low deformability of the mast, are considered “difficult constraints” and are handled by a penalization technique-a penalty term is added to the objective function if the restraint is not satisfied. Therefore, a penalized design has little chance to leave its off-springs. The following sections introduce the idealizations on the analysis of mast behavior and pose the optimization problem. Next, the optimization method is explained in detail. Then, the paper will present and discuss the numerical application of the proposed technique to the case study of a two-level guyed mast subjected to turbulent wind.

نتیجه گیری انگلیسی

Computer hardware that is common to a typical civil engineering design bureau and a reasonable computation time for an engineering practice does not allow precise, exhaustive global optimization of tall masts. However, one run of global optimization of masts using a simplified linear statical analysis program and stochastic GAs is feasible in less than one hour on a common PC. Provided a several-core PC is available, the whole optimization process (i.e., several tens of numerical experiments) can be executed per one night. The design may serve as a hint for the subsequent, more precise nonlinear dynamic analysis. As to the specific mast scheme optimized here, the obtained topology of mast structure nearly resembles the typical industrial mast schemes, but the mast is considerably lighter due to more slender leg and bracing elements. A more thorough comparison is not correct since the optimized mast design lies in the search space on the constraints’ boundaries, while in the industrial design the carrying capacity of a mast usually does not approach the constraints conditions. Still one advantage of the proposed technique is that, optimization usually renders a number of designs of different topology but close objective function values; the designer may choose the most appropriate design.

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