共查询到20条相似文献,搜索用时 187 毫秒
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现有解决电力系统负荷优化的方法通常分为两个步骤进行,即首先确定机组组合,然后再在机组组合的基础上进行经济负荷优化分配。针对这一问题,将一种改进的演化算法应用于机组负荷优化问题的求解中,并根据问题的特点采用了独特的编码形式,在求解过程中同时解决了机组组合和负荷分配问题。实际应用表明:该算法结构简单,容易实现,具有一定的应用价值。 相似文献
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针对目前元启发式算法在求解组合优化问题中的旅行商问题(TSP)时求解缓慢的问题,受量子理论中波函数的启发提出一种多尺度自适应的量子自由粒子优化算法。首先,在可行域中随机初始化表示城市序列的粒子,作为初始的搜索中心;然后,以每个粒子为中心进行当前尺度下的均匀分布函数的采样,并交换采样位置上的城市编号产生新解;最后,根据新解相较上一次迭代中最优解的优劣进行搜索尺度的自适应调整,并在不同的尺度下进行迭代搜索直到满足算法结束条件。将该算法和混合粒子群优化(HPSO)算法、模拟退火(SA)算法、遗传算法(GA)和蚁群优化算法应用在TSP上进行性能测试,实验结果表明自由粒子模型算法适合求解组合优化问题,在TSP数据集上相比目前较优算法在求解速度上平均提升50%以上。 相似文献
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实际中大多数生产调度问题具有多目标优化的性质,本文讨论在不确定加工时间和机器故障的情况下.如何优化多目标流水车间调度问题.首先设计最大流程时间和最大延迟时间两类指标的求解方法,在此基础上提出一种多目标遗传算法,用来迭代求解不确定条件下两类目标的最优化问题.模拟实验的结果表明,本文算法方案可较好解决不确定条件下的流水车间调度问题. 相似文献
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解决了具有不确定收益的投资组合问题。从一个新的视角给出了不确定投资组合的风险定义,在此基础上,提出了新的投资组合优化模型,并设计出新的混合智能算法来解决这一新的优化问题。在新的算法中,99方法被用来计算期望值和机会值,与之前的算法相比,大大减少了计算的工作量,加快了求解过程。最后,提出一个数值例子来验证新的优化模型和所提算法的可行性和正确性。 相似文献
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基于二阶段随机规划的不确定条件下过程优化研究 总被引:1,自引:1,他引:0
在基于二阶段随机规划的不确定条件下过程优化研究中,Ierapetritou and Pistikopoulos(1994)提出了可行域求解策略,Liu and Sahinidis(1996)在此基础上用蒙特卡洛积分策略代替了高斯积分策略,但对于可行域的限定条件尚有欠缺。本文分析和比较了前人的工作,将蒙特卡罗积分策略与基于对偶理论的可行域限定条件相结合,提出了新的求解策略,不仅避免了可行域求解策略中求解一系列子问题而引起的计算负荷随不确定参数数目呈指数增加的不足,而且使蒙特卡洛积分策略算法中的可行域限定条件更加合理,应用文献中的算例进行了仿真实验,证明了该算法的有效性。 相似文献
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随着我国城市轨道交通网络规模快速扩张,线路间协调配合的高度复杂性给城市轨道交通的运营组织与管理带来极大挑战.针对客流需求及其分布双重不确定条件下的城市轨道交通网络末班车衔接优化问题,提出一种分布鲁棒机会约束规划模型,即在给定容忍度下最小化最坏条件下的换乘失败客流量.通过分析分布鲁棒优化模型与其对应鲁棒优化模型之间的联系,证明该模型为鲁棒优化模型的推广形式.基于有限的期望和方差信息构造高斯分布非精确集,采用对偶理论将原模型转化为可利用CPLEX求解的混合整数二阶锥规划形式,并通过数值实验验证所构建模型的有效性.算例结果表明:分布鲁棒模型对于小规模网络可利用CPLEX快速求得精确解;相比鲁棒模型可有效避免产生过于保守的优化结果;相比随机模型可有效降低极端情况下换乘失败客流量,具有较强的鲁棒性. 相似文献
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研究了服务率不确定情况下的单站点传送带给料加工站(CSPS)系统的鲁棒优化控制问题。在仅知服务率区间的条件下,以CSPS系统的前视距离作为控制变量,将鲁棒优化控制问题建模成不确定参数的半马尔可夫决策过程(SMDP)的极大极小优化问题,在状态相关的情况下,给出全局优化算法进行鲁棒控制策略求解。首先,运用遗传算法求解固定策略下的最差性能值;其次,根据求解得到的最差性能值,运用模拟退火算法求解最优鲁棒控制策略。仿真结果表明,服务率不确定的CSPS系统的最优鲁棒性能代价与服务率固定为区间中值系统的最优性能代价相差不大,并且随着不确定区间的缩小,两者的差值越小,说明了全局优化算法的有效性。 相似文献
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In this paper, the general problem of Euclidean combinatorial optimization under uncertainty is formulated for the first time
and the concepts of a stochastic multiset, a multiset of fuzzy numbers, a stochastic Euclidean combinatorial set, and general
Euclidean combinatorial set of fuzzy stochastic numbers that combines the properties of both types of uncertainty are introduced.
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Translated from Kibernetika i Sistemnyi Analiz, No. 5, pp. 35–44, September–October 2008. 相似文献
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M.S.R. Martins S.C. Fuchs L.U. Pando R. Lüders M.R. Delgado 《Computers & Industrial Engineering》2013
This paper proposes a PSO-based optimization approach with a particular path relinking technique for moving particles. PSO is evaluated for two combinatorial problems. One under uncertainty, which represents a new application of PSO with path relinking in a stochastic scenario. PSO is considered first in a deterministic scenario for solving the Task Assignment Problem (TAP) and hereafter for a resource allocation problem in a petroleum terminal. This is considered for evaluating PSO in a problem subject to uncertainty whose performance can only be evaluated by simulation. In this case, a discrete event simulation is built for modeling a real-world facility whose typical operations of receiving and transferring oil from tankers to a refinery are made through intermediary storage tanks. The simulation incorporates uncertain data and operational details for optimization that are not considered in other mathematical optimization models. Experiments have been carried out considering issues that affect the choice of parameters for both optimization and simulation. The results show advantages of the proposed approach when compared with Genetic Algorithm and OptQuest (a commercial optimization package). 相似文献
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Dennis Weyland Roberto Montemanni Luca Maria Gambardella 《Journal of Parallel and Distributed Computing》2013
In this work we propose a general metaheuristic framework for solving stochastic combinatorial optimization problems based on general-purpose computing on graphics processing units (GPGPU). This framework is applied to the probabilistic traveling salesman problem with deadlines (PTSPD) as a case study. Computational studies reveal significant improvements over state-of-the-art methods for the PTSPD. Additionally, our results reveal the huge potential of the proposed framework and sampling-based methods for stochastic combinatorial optimization problems. 相似文献
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Linear unconstrained problem of combinatorial optimization on arrangements under stochastic uncertainty is being solved. The minimum is defined as the result of sequential comparison of numerical characteristics of random variables. The properties of the solution of the optimization problem under study are obtained. These properties use the properties of special constructed deterministic problems. The authors also propose the reduction method to solve linear unconstrained problem of combinatorial stochastic optimization, which is based on obtained solution’s properties. 相似文献
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Simulation based optimization of stochastic systems with integer design variables by sequential multipoint linear approximation 总被引:1,自引:1,他引:0
S.J. Abspoel L.F.P. Etman J. Vervoort R.A. van Rooij A.J.G. Schoofs J.E. Rooda 《Structural and Multidisciplinary Optimization》2001,22(2):125-139
Optimization problems are considered for which objective function and constraints are defined as expected values of stochastic
functions that can only be evaluated at integer design variable levels via a computationally expensive computer simulation.
Design sensitivities are assumed not to be available. An optimization approach is proposed based on a sequence of linear approximate
optimization subproblems. Within each search subregion a linear approximate optimization subproblem is built using response
surface model building. To this end, N simulation experiments are carried out in the search subregion according to a D-optimal
experimental design. The linear approximate optimization problem is solved by integer linear programming using corrected constraint
bounds to account for any uncertainty due to the stochasticity. Each approximate optimum is evaluated on the basis of M simulation
replications with respect to objective function change and feasibility of the design. The performance of the optimization
approach and the influence of parameters N and M is illustrated via two analytical test problems. A third example shows the
application to a production flow line simulation model.
Received April 28, 2000 相似文献
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Real-world simulation optimization (SO) problems entail complex system modeling and expensive stochastic simulation. Existing SO algorithms may not be applicable for such SO problems because they often evaluate a large number of solutions with many simulation calls. We propose an integrated solution method for practical SO problems based on a hierarchical stochastic modeling and optimization (HSMO) approach. This method models and optimizes the studied system at increasing levels of accuracy by hierarchical sampling with a selected set of principal parameters. We demonstrate the efficiency of HSMO using the example problem of Brugge oil field development under geological uncertainty. 相似文献
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We first propose a formal definition for the concept of probabilistic combinatorial optimization problem (under the a priori method). Next, we study the complexity of optimally solving probabilistic maximum independent set problem under several a priori optimization strategies as well as the complexity of approximating optimal solutions. For the different strategies studied, we present results about the restriction of probabilistic independent set on bipartite graphs. 相似文献
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Elias B. Kosmatopoulos Author Vitae 《Automatica》2009,45(3):716-723
Adaptive optimization (AO) schemes based on stochastic approximation principles such as the Random Directions Kiefer-Wolfowitz (RDKW), the Simultaneous Perturbation Stochastic Approximation (SPSA) and the Adaptive Fine-Tuning (AFT) algorithms possess the serious disadvantage of not guaranteeing satisfactory transient behavior due to their requirement for using random or random-like perturbations of the parameter vector. The use of random or random-like perturbations may lead to particularly large values of the objective function, which may result to severe poor performance or stability problems when these methods are applied to closed-loop controller optimization applications. In this paper, we introduce and analyze a new algorithm for alleviating this problem. Mathematical analysis establishes satisfactory transient performance and convergence of the proposed scheme under a general set of assumptions. Application of the proposed scheme to the adaptive optimization of a large-scale, complex control system demonstrates the efficiency of the proposed scheme. 相似文献
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An approximation algorithm for interval data minmax regret combinatorial optimization problems 总被引:1,自引:0,他引:1
Adam Kasperski 《Information Processing Letters》2006,97(5):177-180
The general problem of minimizing the maximal regret in combinatorial optimization problems with interval data is considered. In many cases, the minmax regret versions of the classical, polynomially solvable, combinatorial optimization problems become NP-hard and no approximation algorithms for them have been known. Our main result is a polynomial time approximation algorithm with a performance ratio of 2 for this class of problems. 相似文献