共查询到20条相似文献,搜索用时 15 毫秒
1.
讨论一类大规模系统的优化问题,提出一种递阶优化方法.该方法首先将原问题转化为多目标优化问题,证明了原问题的最优解在多目标优化问题的非劣解集中,给出了从多目标优化问题的解集中挑出原问题最优解的算法,建立了算法的理论基础.仿真结果验证了算法的有效性. 相似文献
2.
刘淳安 《计算机工程与应用》2011,47(22):61-63
动态非线性约束优化是一类复杂的动态优化问题,其求解的困难主要在于如何处理问题的约束及时间(环境)变量。给出了一类定义在离散时间(环境)空间上的动态非线性约束优化问题的新解法,从问题的约束条件出发构造了一个新的动态熵函数,利用此函数将原优化问题转化成了两个目标的动态优化问题。进一步设计了新的杂交算子和带局部搜索的变异算子,提出了一种新的多目标优化求解进化算法。通过对两个动态非线性约束优化问题的计算仿真,表明该算法是有效的。 相似文献
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机械手臂是一个复杂、强耦合、非线性的系统,其运动学逆问题的求解常常是一个多解或无解的过程,传统方法求解/较为困难,本文将其转化为连续性空间的优化问题,并应用蚁群优化算法对其进行求解。蚁群优化算法是随机搜索、全局优化的算法,不仅能够很好地解决任意的优化组合问题,还能较好地解决连续性空间解的优化问题。通过MATLAB仿真求解,证实了该算法的优越性,分析了参数的设置对蚁群优化算法性能的影响。 相似文献
5.
A fuzzy particle swarm optimization algorithm for computer communication network topology design 总被引:4,自引:4,他引:0
Particle swarm optimization (PSO) is a powerful optimization technique that has been applied to solve a number of complex
optimization problems. One such optimization problem is topology design of distributed local area networks (DLANs). The problem
is defined as a multi-objective optimization problem requiring simultaneous optimization of monetary cost, average network
delay, hop count between communicating nodes, and reliability under a set of constraints. This paper presents a multi-objective
particle swarm optimization algorithm to efficiently solve the DLAN topology design problem. Fuzzy logic is incorporated in
the PSO algorithm to handle the multi-objective nature of the problem. Specifically, a recently proposed fuzzy aggregation
operator, namely the unified And-Or operator (Khan and Engelbrecht in Inf. Sci. 177: 2692–2711, 2007), is used to aggregate the objectives. The proposed fuzzy PSO (FPSO) algorithm is empirically evaluated through a preliminary
sensitivity analysis of the PSO parameters. FPSO is also compared with fuzzy simulated annealing and fuzzy ant colony optimization
algorithms. Results suggest that the fuzzy PSO is a suitable algorithm for solving the DLAN topology design problem. 相似文献
6.
This paper discusses the problem of structural optimization of product families subjected to multiple load cases, evaluated
by computationally costly finite element analysis. Product families generally have a complex composition of shared components
that makes individual product optimization difficult as the relation between the shared variables is not always intuitive.
More optimal is to treat the problem as a product family optimization problem. Though, for product families subjected to multiple
and computationally costly crash loads, the optimization problem takes too long time to solve with traditional methods. Therefore,
a new optimization algorithm is presented that decomposes the family problem into sub-problems and iteratively reduces the
number of sub-problems, decouple and solve them. The algorithm is applicable for module based product families with predefined
composition of generalized commonality, subjected to multiple load cases that can be analyzed separately. The problem reduction
is performed by only considering the constraints that are critical in the optimal solution. Therefore the optimization algorithm
is called the Critical Constraint Method, CCM. Finally the CCM algorithm is evaluated by two product family optimization problems. 相似文献
7.
This paper presents an interactive method for the selection of design criteria and the formulation of optimization problems within a computer aided optimization process of engineering systems. The key component of the proposed method is the formulation of an inverse optimization problem for the purpose of determining the design preferences of the engineer. These preferences are identified based on an interactive modification of a preliminary optimization result that is the solution of an initial problem statement. A formulation of the inverse optimization problem is presented, which is based on a weighted-sum multi-objective approach and leads to an explicit optimization problem that is computationally inexpensive to solve. Numerical studies on structural shape optimization problems show that the proposed method is able to identify the optimization criteria and the formulation of the optimization problem which drive the interactive user modifications. 相似文献
8.
Yunpeng Chai Author Vitae Author Vitae Yinong Chen Author Vitae 《Journal of Systems and Software》2009,82(8):1344-1361
The optimization of Clustered Streaming Media Servers (CSMS), which aims at using as few hardware resources and as cost-effective as possible, while providing satisfactory performance and QoS, has a great impact on the practicability and efficiency of CSMS. Based on the analysis and formulization of critical performance factors of CSMS and the relationship among the performance, QoS, and the costs in CSMS, a stepwise optimization algorithm is developed to solve the optimization problem efficiently. The algorithm is based on an approach that models the optimization problem into a directed acyclic graph and then addresses the complex optimization problem step by step. The algorithm applies a divide and conquer model that not only reduces the complexity of the optimization problem, but also accelerates the optimization process. Progressive information is collected in the process and used in solving the problem. Furthermore, a simulation system of CSMS is necessary for the optimization algorithm to generate the accurate information produced in the entire streaming service process. Thus, we designed and implemented such a simulation system based on the theoretical performance model of CSMS and the parameters measured in practical CSMS testbed. Finally, a case study of the optimization problem is given to demonstrate the process of the algorithm, and an appropriate plan for designing practical CSMS system is illustrated. 相似文献
9.
Harbir Antil Matthias Heinkenschloss Ronald H. W. Hoppe Danny C. Sorensen 《Computing and Visualization in Science》2010,13(6):249-264
We introduce a technique for the dimension reduction of a class of PDE constrained optimization problems governed by linear
time dependent advection diffusion equations for which the optimization variables are related to spatially localized quantities.
Our approach uses domain decomposition applied to the optimality system to isolate the subsystem that explicitly depends on
the optimization variables from the remaining linear optimality subsystem. We apply balanced truncation model reduction to
the linear optimality subsystem. The resulting coupled reduced optimality system can be interpreted as the optimality system
of a reduced optimization problem. We derive estimates for the error between the solution of the original optimization problem
and the solution of the reduced problem. The approach is demonstrated numerically on an optimal control problem and on a shape
optimization problem. 相似文献
10.
Nanoscale crossbar architectures have received steadily growing interests as a result of their great potential to be main building blocks in nanoelectronic circuits. However, due to the extremely small size of nanodevices and the bottom-up self-assembly nanofabrication process, considerable process variation will be an inherent vice for crossbar nanoarchitectures. In this paper, the variation tolerant logical mapping problem is treated as a bilevel multiobjective optimization problem. Since variation mapping is an NP-complete problem, a hybrid multiobjective evolutionary algorithm is designed to solve the problem adhering to a bilevel optimization framework. The lower level optimization problem, most frequently tackled, is modeled as the min–max-weight and min-weight-gap bipartite matching (MMBM) problem, and a Hungarian-based linear programming (HLP) method is proposed to solve MMBM in polynomial time. The upper level optimization problem is solved by evolutionary multiobjective optimization algorithms, where a greedy reassignment local search operator, capable of exploiting the domain knowledge and information from problem instances, is introduced to improve the efficiency of the algorithm. The numerical experiment results show the effectiveness and efficiency of proposed techniques for the variation tolerant logical mapping problem. 相似文献
11.
Nikos D. Lagaros Manolis Papadrakakis 《Structural and Multidisciplinary Optimization》2007,33(6):457-469
Stochastic performance measures can be taken into account, in structural optimization, using two distinct formulations: robust
design optimization (RDO) and reliability-based design optimization (RBDO). According to a RDO formulation, it is desired
to obtain solutions insensitive to the uncontrollable parameter variation. In the present study, the solution of a structural
robust design problem formulated as a two-objective optimization problem is addressed, where cross-sectional dimensions, material
properties and earthquake loading are considered as random variables. Additionally, a two-objective deterministic-based optimization
(DBO) problem is also considered. In particular, the DBO and RDO formulations are employed for assessing the Greek national
seismic design code for steel structural buildings with respect to the behavioral factor considered. The limit-state-dependent
cost is used as a measure of assessment. The stochastic finite element problem is solved using the Monte Carlo Simulation
method, while a modified NSGA-II algorithm is employed for solving the two-objective optimization problem. 相似文献
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This study presents a comparison of global optimization algorithms applied to an industrial engineering optimization problem. Three global stochastic optimization algorithms using continuous variables, i.e. the domain elimination method, the zooming method and controlled random search, have been applied to a previously studied ride comfort optimization problem. Each algorithm is executed three times and the total number of objective function evaluations needed to locate a global optimum is averaged and used as a measure of efficiency. The results show that the zooming method, with a proposed modification, is most efficient in terms of number of objective function evaluations and ability to locate the global optimum. Each design variable is thereafter given a set of discrete values and two optimization algorithms using discrete variables, i.e. a genetic algorithm and simulated annealing, are applied to the discrete ride comfort optimization problem. The results show that the genetic algorithm is more efficient than the simulated annealing algorithm for this particular optimization problem. 相似文献
14.
Yu. P. Laptin 《Cybernetics and Systems Analysis》2009,45(3):497-502
An approach to reducing a constrained convex programming problem to an unconstrained optimization problem is considered. An
initial internal feasible point is supposed to be specified. An equivalent unconstrained optimization problem is formulated
in such a way that the calculated values of gradients (subgradients) of original functions do not violate the initial constraints.
Properties of introduced functions are investigated. Convexity conditions are formulated for the unconstrained optimization
problem. The results may by useful for the development of algorithms for solving constrained optimization problems. 相似文献
15.
Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization 总被引:8,自引:0,他引:8
G. Venter J. Sobieszczanski-Sobieski 《Structural and Multidisciplinary Optimization》2004,26(1-2):121-131
The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The papers new contributions to multidisciplinary optimization are the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations for the utilization of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization, as well as the numerical noise and discrete variables present in the current example problem. 相似文献
16.
《国际计算机数学杂志》2012,89(8):1713-1729
In this paper, we consider an optimal control problem of switched systems with a continuous-time inequality constraint. Because of the complexity of this constraint, it is difficult to solve this problem by standard optimization techniques. To overcome this difficulty, the problem is divided into a bi-level optimization problem involving a combination of a continuous-time optimal control problem and a discrete optimization problem. Then, a modified Broyden-Fletcher-Goldfarb-Shanno algorithm and a discrete filled function method is first proposed to solve this bi-level optimization problem. Finally, a numerical example is presented to illustrate the efficiency of our method. 相似文献
17.
Baocang Ding 《Asian journal of control》2013,15(1):302-309
The dynamic output feedback robust model predictive controller for a system with both polytopic uncertainty and bounded disturbance is addressed in this paper. This controller utilizes a main optimization problem to find the control law and a simple auxiliary optimization problem to refresh the bounds on the true state. The main optimization problem, which is not necessarily solved at each sampling instant, achieves the near‐optimal solution. The auxiliary optimization, which is solved at each sampling instant, is followed with a simple criterion which determines whether or not to solve the main optimization problem at the next sampling time. By applying the proposed method, the augmented state of the closed‐loop system is guaranteed to converge to the neighborhood of the equilibrium point. 相似文献
18.
投资组合优化问题是一个复杂的组合优化问题,属于NP难问题,传统算法很难解决这一问题。将二次粒子群算法应用到投资组合优化问题中,并采用参数的自适应变化。数值模拟表明该算法在投资组合优化问题中能避免陷入局部最优,加快达到全局最优的收敛速度,并在一定意义下优于标准粒子群算法。 相似文献
19.
对供电网络优化设计提出了一种新算法。把供电网络优化设计问题抽象成图论问题,应用图论最优化方法解决该问题。同时提出了多边形变换方法,用模拟退火算法对供电网络进行优化设计,最终得到一个费用最小电网。 相似文献
20.
LIU JingFa& LI Gang School of Computer Software Nanjing University of Information Science & Technology Nanjing China School of Mathematics Physics 《中国科学:信息科学(英文版)》2010,(5):885-895
With the background of the satellite module layout design, the circular packing problem with equilibrium behavioral constraints is a layout optimization problem and NP-hard problem in math. For lack of a powerful optimization method, this problem is hard to solve. The energy landscape paving (ELP) method is a class of stochastic global optimization algorithms based on the Monte Carlo sampling. Based on the quasiphysical strategy and the penalty function method, the problem is converted into an unconstrained... 相似文献