随机仿真优化的一类遗传序优化框架

针对仿真优化问题存在随机性，计算费时，航空间距大，多极小等难点，结合遗传算法的并行遗传搜索，最优计算量分配以及优化的目标致化和序比较思想提出一类遗传序优化框架，进而讨论了该方法的收敛性和具体实施问题，最后指出了进一步的研究内容。     

基于两阶段随机仿真优化算法的体检顾客预约调度

针对体检机构顾客排队等待时间长的问题,研究随机服务时间下的体检顾客调度,采用多人时间槽预约策略,并在预约调度策略的基础上优化每位顾客的体检项目顺序,提出一种包含粗糙仿真评估和精确仿真评估两阶段随机仿真优化算法.运用序优化思想将基于亲和度评估的多种群遗传算法作为迭代优化策略,并利用改进的最优计算量分配方法排除超级个体的影...     

随机优化问题基于假设检验的遗传算法

为了有效解决具有不确定性和多极小性的随机优化问题 ,提出了一类基于假设检验的遗传算法 .该方法通过多次评价来进行解性能的合理估计 ,利用遗传操作来进行解空间的有效搜索 ,采用假设检验来增加种群的多样性和算法的探索能力 ,从而避免遗传算法的早熟收敛 .基于典型的随机函数优化和组合优化问题 ,仿真研究了假设检验、性能估计次数、噪声幅度对算法性能的影响 ,验证了所提方法的有效性和鲁棒性     

随机优化问题一类基于假设检验的模拟退火算法

针对随机优化问题的不确定性，提出一类基于假设检验的模拟退火算法．该方法通过多次评价来合理估计解的性能，利用假设检验减少重复性搜索，采用突跳性搜索避免局部极小，并通过温度控制调节突跳能力．数值仿真研究了假设检验、性能估计、噪声幅度对算法性能的影响，其结果验证了该方法的有效性和鲁棒性．     

一类随机摄动系统的鲁棒控制与仿真

在内模控制(IMC)结构下对一类随机摄动系统的鲁棒控制及其仿真研究作了探讨.首先针对一类随机模型误差的描述定义了一个实际敏感度和标称敏感度之间的加权敏感度误差,然后应用谱分解的方法调整标称控制器来最小化加权敏感度误差在整个频段上的方差,为一类随机摄动系统提供了一种鲁棒控制器设计方法,可使系统期望的标称性能对模型误差具有良好的鲁棒性.最后根据随机摄动系统的特点进行仿真研究,进一步说明了所得控制方法的有效性.     

一类人流仿真系统中的随机路径搜索算法

为了大型展览会的顺利举行,在进行展会场馆规划时,需要对展览会中行人交通流的行为建模预测,并利用人流仿真系统模拟展会举行时的情况,以期在展览会举行前预知将会出现的问题.在利用元胞自动机建立人流离散数学模型的基础上,根据行人交通流的基本理论,提出人流在行进时,路线选择的特征.并在仿真系统中采用对电子地图进行深度优先遍历的方法搜索路径.而后根据人流行进路线的特征,在所得的路径中筛选出相对合理的路径,最后随机选择,得到搜索结果.通过测试说明算法所得结果符合人流特征,并且效率也可以满足需要.     

基于二次优化的随机优化算法结果的改进

统计研究发现,随机优化算法多次运行后的优化结果满足正态分布,且期望值更接近最优解。为此,提出一种基于统计学理论并结合牛顿法的二次优化方法来改进随机优化算法的求解结果,以克服将多次优化结果的平均值作为最优解时不能满足精度要求的缺陷。以遗传算法对4个经典测试函数的多次优化为例,分别运用平均法和二次优化法来综合其优化结果。多次实验表明,二次优化法在处理多次随机运行结果时,比平均法精度更高、稳定性更好。     

生产作业计划仿真优化研究

将仿真技术和遗传算法相结合,根据生产车间的资源情况、优化目标等建立了生产调度仿真模型,然后对仿真输出结果进行统计,针对统计结果应用遗传算法对调度决策进行优化。仿真优化结果说明了该集成优化方法是有效性的。     

基于HFSS-MATLB-API的天线布局优化仿真

随着用于车载平台的天线数量日益增多, 汽车的电磁兼容问题日益严重. 针对天线布局缺少统一合理模型和理论方法指导的现状,利用HFSS-Matlab-Api脚本库在Matlab中调用HFSS建模进行天线布局的电磁兼容性仿真, 采用遗传算法对布局进行优化. 此方法省去人为数学模型推导过程, 减少用户反复绘制模型、修改参数的重复工作. 不仅充分利用HFSS仿真的高精度、可靠性和便捷性, 而且采用遗传算法减少盲目的试探带来的时间和成本的浪费. 最后, 得到与理论相符合的实验结果, 从而验证了布局与优化方法的可行性.     

一类非线性船舶在随机波上的倾覆概率仿真

研究一类非线性船舶在随机波上的倾覆概率仿真。船舶在给定随机波上的、直到颠覆的大幅度横摇可用一个四元非线性方程表示;假定噪声为白噪声的情况下,该方程通过转化可被改写成对应的二阶随机微分方程;基于半离散数值解法,设计了一类非线性船舶在随机波上的倾覆概率仿真算法,该方法简单,易于工程应用;给出实例演示了方法的应用,验证了算法的有效性。     

Multi-objective ordinal optimization for simulation optimization problems

Ordinal optimization (OO) has been successfully applied to accelerate the simulation optimization process with single objective by quickly narrowing down the search space. In this paper, we extend the OO techniques to address multi-objective simulation optimization problems by using the concept of Pareto optimality. We call this technique the multi-objective OO (MOO). To define the good enough set and the selected set, we introduce two performance indices based on the non-dominance relationship among the designs. Then we derive several lower bounds for the alignment probability under various scenarios by using a Bayesian approach. Numerical experiments show that the lower bounds of the alignment probability are valid when they are used to estimate the size of the selected set as well as the expected alignment level. Though the lower bounds are conservative, they have great practical value in terms of narrowing down the search space.     

A Hybrid approach for integer programming combining genetic algorithms, linear programming and ordinal optimization

Hybrid methods are promising tools in integer programming, as they combine the best features of different methods in a complementary fashion. This paper presents such a framework, integrating the notions of genetic algorithm, linear programming, and ordinal optimization in an effort to shorten computation times for large and/or difficult integer programming problems. Capitalizing on the central idea of ordinal optimization and on the learning capability of genetic algorithms to quickly generate good feasible solutions, and then using linear programming to solve the problem that results from fixing the integer part of the solution, one may be able to obtain solutions that are close to optimal. Indeed ordinal optimization guarantees the quality of the solutions found. Numerical testing on a real-life complex scheduling problem demonstrates the effectiveness and efficiency of this approach.     

基于遗传算法的随机性（Q，r）库存系统仿真优化

（Q，r）模型是库存管理中的重要控制模型。对于随机性（Q，r）库存系统，难以用解析方法求解最优的库存控制策略。运用仿真优化技术，基于离散事件系统仿真原理，建立了随机性（Q，r）库存系统的仿真模型，设计了一种改进的遗传算法并应用它优化库存系统的库存控制策略。采用面向对象方法实现了仿真模型和改进的遗传算法。仿真实例表明所提出的仿真优化技术是可行且有效的。     

Standard clock simulation and ordinal optimization applied to admission control in integrated communication networks

In this paper we apply the ideas of ordinal optimization and the technique of Standard Clock (SC) simulation to the voice-call admission-control problem in integrated voice/data multihop radio networks. This is an important problem in networking that is not amenable to exact analysis by means of the usual network modeling techniques. We first describe the use of the SC approach on sequential machines, and quantify the speedup in simulation time that is achieved by its use in a number of queueing examples. We then develop an efficient simulation model for wireless integrated networks based on the use of the SC approach, which permits the parallel simulation of a large number of admission-control policies, thereby reducing computation time significantly. This model is an extension of the basic SC approach in that it incorporates fixed-length data packets, whereas SC simulation is normally limited to systems with exponentially distributed interevent times. Using this model, we demonstrate the effectiveness of ordinal-optimization techniques, which provide a remarkably good ranking of admission-control policies after relatively short simulation runs, thereby facilitating the rapid determination of good policies. Moreover, we demonstrate that the use of crude, inaccurate analytical and simulation models can provide highly accurate policy rankings that can be used in conjunction with ordinal-optimization methods, provided that they incorporate the key aspects of system operation.     

An efficient framework for optimal robust stochastic system design using stochastic simulation

The knowledge about a planned system in engineering design applications is never complete. Often, a probabilistic quantification of the uncertainty arising from this missing information is warranted in order to efficiently incorporate our partial knowledge about the system and its environment into their respective models. This leads to a robust stochastic design framework where probabilistic models of excitation uncertainties and system modeling uncertainties can be introduced; the design objective is then typically related to the expected value of a system performance measure, such as reliability or expected life-cycle cost. For complex system models, this expected value can rarely be evaluated analytically and so it is often calculated using stochastic simulation techniques, which involve an estimation error and significant computational cost. An efficient framework, consisting of two stages, is presented here for the optimization in such robust stochastic design problems. The first stage implements a novel approach, called stochastic subset optimization (SSO), for iteratively identifying a subset of the original design space that has high plausibility of containing the optimal design variables. The second stage adopts some other stochastic optimization algorithm to pinpoint the optimal design variables within that subset. The focus is primarily on the theory and implementation issues for SSO but also on topics related to the combination of the two different stages for overall enhanced efficiency. An illustrative example is presented that shows the efficiency of the proposed methodology; it considers the optimization of the reliability of a base-isolated structure considering future near-fault ground motions.     

基于梯度投影法与随机优化算法的约束优化方法

针对带有线性等式和不等式约束的无确定函数形式的约束优化问题,提出一种利用梯度投影法与遗传算法、同时扰动随机逼近等随机算法相结合的优化方法。该方法利用遗传算法进行全局搜索,利用同时扰动随机逼近算法进行局部搜索,算法在每次进化时根据线性约束计算父个体处的梯度投影方向,以产生新个体,从而能够严格保证新个体满足全部约束条件。将上述约束优化算法应用于典型约束优化问题,其仿真结果表明了所提出算法的可行性和收敛性。     

求解随机相关机会规划的有效算法

随机相关机会规划作为一类重要的随机规划,存在于许多领域中.为了寻找更为有效的求解随机相关机会规划的算法,采用随机仿真来逼近机会函数,在微粒群算法中利用随机仿真估计适应值,提出一种将随机仿真与微粒群算法相结合的随机相关机会规划算法.通过实例仿真测试该算法的性能,并与遗传算法进行比较,结果表明本算法具有一定的优势.     

遗传算法优化框架中使用嵌入的混合可视化和数据分析的过程设计优化

非线性、非凸、不连续的数学模型的使用,使得过程优化问题难以求解。虽然确定性方法已经取得了重大的进步,但随机方法,特别是遗传算法提供了一种更有优势的方法。然而,遗传算法的性质决定了其不适合求解带有高约束的问题。本文提出了一个适用于高度约束问题的目标遗传算法,算法中的算子：交叉和变异,是在数据分析步骤得到的关于可行区域和目标函数行为信息的基础上定义。数据分析是以平行坐标系中的可视化描述为基础,一种模式匹配算法,扫描园算法,通过学习向量量化的使用被扩展来自动地确定目标函数和搜索空间的关键特征,这些特征被用于确定遗传算子。对石油稳定问题应用新的目标遗传算法,其结果证明了方法的有用、高效和健壮性。作为数据分析的核心,可视化技术的使用也可以用于解释优化过程得到的结果。