油田注水系统拓扑布局优化的混合遗传算法

以投资最小为目标函数,建立了注水系统拓扑布局优化数学模型.根据模型特点,将优化问题分为两层,分别采用遗传算法和非线性优化方法进行求解.并对遗传算法的操作过程进行了改进,调整了适应函数,改进了交叉和变异操作,结合了模拟退火算法,在操作过程中使约束条件得到满足,减少了不可行解的产生,使遗传算法的优化性能得到了提高.优化算例说明了该方法的有效性.     

基于VAR风险控制的LOG-最优资产组合模型

在证券收益率服从正态分布的假设下,提出了基于V AR风险控制下的单周期LOG-最优资产组合问题,建立了数学模型,证明了最优解的存在性与唯一性,设计了求解该模型的新兴智能优化算法——遗传算法并进行了实例计算与分析.     

基于遗传算法的水泵机组优化调度研究

从供水系统整体优化和能量损耗最小的角度出发,结合供水厂生产的实际特点,建立了基于遗传算法的水泵机组优化组合的数学模型,并对该数学模型进行求解,得到较为满意的最优解或次优解.实际生产表明,利用遗传算法求解水泵机组优化组合的数学模型,对水泵机组进行优化调度,取得了良好的经济效益.     

复杂供水系统优化调度的数学模型与求解方法研究

从供水管网系统整体优化和最小供水费用的角度出发,结合复杂供水系统的生产调度特点,建立了复杂供水系统整体优化调度的数学模型.利用多维编码的遗传算法求解数学模型,对其编码规则、染色体的评估以及遗传操作进行了深入的研究.并给出了详细的求解步骤.采用Matlab软件编写了基于遗传算法的优化调度程序.对某市供水管网系统的生产调度进行了仿真研究.仿真结果表明,应用遗传算法求解管网系统优化调度,可以得到问题的全局最优解,并将调度结果和自来水公司实际生产情况比较,采用优化调度程序进行生产调度可以节省运行费用.     

有时间窗装卸问题的多策略分组编码遗传算法

根据有时间窗装卸问题(PDPTW)的数学模型,设计了多策略分组编码遗传算法,将禁忌思想用于产生可行解的启发式插入算法之中,对计算实例进行了求解,结果表明,此算法可以有效求得有时间窗装卸问题的近似最优解.     

人口密集场所紧急疏散问题的数学模型及其优化解

在人口密集场所(馆)观众席位区及疏散通道分布模拟图的基础上,着眼于紧急疏散方案制定中的主要问题,分析人群疏散过程中的主要矛盾,建立了属于非线性规划问题的人员紧急疏散的数学模型.在转化为整数线性规划问题后,可用分枝定界法求解,并用L ingo计算程序实现.所求得的最优解为布局比较简单的场馆制定紧急疏散方案提供了依据.     

基于遗传算法的大学课程表问题研究

课程表问题是时间表问题之一 ,也是 NP难问题 .根据大学授课形式的特点建立了大学课程表问题的数学模型 ,给出了求解该问题的遗传算法 .根据模型和大学课程表问题的特点设计了一种全新的编码 ,提出了一种新形式的交叉方式 .实验结果表明该方法是可行和有效的 .     

基于模拟退火算法的矩形优化排样问题的研究

考虑了一种矩形优化排样系统中遗传算法和模拟退火算法的结合算法.首先建立了该系统的通用数学模型.然后给出了求解该问题的遗传模拟退火算法.最后用VC++6.0模拟算例的结果表明该算法是一种行之有效的方法.     

第3方冷链物流配送路径优化研究

根据冷链物流配送具有的特殊性,进一步探索冷链物流配送优化研究的方案.在保证货物不超载的情况下,建立以一个冷链配送中心、多个顾客的冷链物流的配送数学模型,用改进遗传算法加以分析求解,并采用Matlab软件对算法进行编程,最后通过算例进行验证.得到如下结论:利用改进遗传算法得到的最优配送路线优于标准遗传算法求解结果.     

设备空间磁场预测的传感器布阵优化

本文研究了铁磁性设备周围空间传感器布阵的问题。我们建立了关于传感器位置和数量优化的数学模型,并通过遗传算法对模型进行求解。首先,本文选用对传感器数量和距离要求较少的旋转椭球体作为磁场远场换算的模型。在旋转椭球体模型中,传感器分布位置不当会导致磁场计算系数矩阵的条件数过大,模型将出现病态,因而计算得到的远场磁场结果不可靠。所以,本文以旋转椭球体模型中的系数矩阵条件数为优化目标,建立数学模型优化单个设备上方传感器的数量与位置分布,并利用遗传算法对模型求解。其次,通过实验验证了本模型对于单个设备的传感器位置和数量优化是有效的,且所用传感器数量少,计算结果可靠。最后,将单个设备传感器位置和数量的优化模型推广到多个设备,以两个设备为代表用同时优化和分别优化两种方法计算传感器位置,根据实验计算这两种方法都具有较高的远场磁场计算精度,但分开优化的方法在实际计算更加简便、容易操作。     

A hybrid heuristic for the maximum clique problem

In this paper we present a heuristic based steady-state genetic algorithm for the maximum clique problem. The steady-state genetic algorithm generates cliques, which are then extended into maximal cliques by the heuristic. We compare our algorithm with three best evolutionary approaches and the overall best approach, which is non-evolutionary, for the maximum clique problem and find that our algorithm outperforms all the three evolutionary approaches in terms of best and average clique sizes found on majority of DIMACS benchmark instances. However, the obtained results are much inferior to those obtained with the best approach for the maximum clique problem.     

一种改进遗传算法在最大独立子集问题中的应用

最大独立子集问题是组合优化问题中的一个重要问题,该问题是一个NP难题,其目标是在一个环图中找到一个最大的独立子集.提出了一种改进的遗传算法来解决这个问题,用一种基于条件的遗传算子来代替通常的基于概率的遗传算子.实验结果表明提出的算法是有效的.     

基于遗传算法的工艺路径柔性调度算法

针对具有工艺路径柔性的车间调度问题,提出基于OR子图和子路径的工艺路径柔性描述方法,该描述方法形式简单且允许OR子图多层嵌套。以此为基础,设计了基于遗传算法的工艺路径柔性调度算法,并采用以工艺路径编码、机器编码和工件调度编码为基础的三维染色体编码策略,其中,工艺路径编码和机器编码分别通过最大子路径数量和最大机器数量随机产生,其优势在于任意染色体均表示可行解,并可以使用简单的交叉算子和变异算子实现遗传操作且其后代亦为可行解。最后通过实验证明了算法的优化能力。     

二维恒定各向同性介质渗透系数反演的遗传算法

给出了利用遗传算法求解二维恒定各项同性介质渗透系数反演的一种新方法,该方法把参数反演问题转化为优化问题通过遗传算法求解.数值模拟结果表明:该方法具有精度高、收敛速度快、编程简单、易于计算机实现等优点,值得在实际工作采用.     

A Genetic Algorithm for the Allocation of Buffer Storage Capacities in a Production Line with Unreliable Machines

In this paper, we consider a flow-line manufacturing system organized as a series of workstations separated by finite buffers. The failure and repair times of machines are supposed to be exponentially distributed. The production rate of each machine is deterministic, and different machines may have different production rates. The buffer allocation problem consists in determining the buffer capacities with respect to a given optimality criterion, which depends on the average production rate of the line, the buffer acquisition and installation cost, and the inventory cost. For this problem we propose a genetic algorithm where the tentative solutions are evaluated with an approximate method based on the Markov-model aggregation approach.     

Optimal equi-partition of rectangular domains for parallel computation

We present an efficient method for the partitioning of rectangular domains into equi-area sub-domains of minimum total perimeter. For a variety of applications in parallel computation, this corresponds to a load-balanced distribution of tasks that minimize interprocessor communication. Our method is based on utilizing, to the maximum extent possible, a set of optimal shapes for sub-domains. We prove that for a large class of these problems, we can construct solutions whose relative distance from a computable lower bound converges to zero as the problem size tends to infinity. PERIX-GA, a genetic algorithm employing this approach, has successfully solved to optimality million-variable instances of the perimeter-minimization problem and for a one-billion-variable problem has generated a solution within 0.32% of the lower bound. We report on the results of an implementation on a CM-5 supercomputer and make comparisons with other existing codes.This research was partially funded by Air Force Office of Scientific Research grant F496-20-94-1-0036 and National Science Foundation grants CDA-9024618 and CCR-9306807.     

Enhanced Direct and Indirect Genetic Algorithm Approaches for a Mall Layout and Tenant Selection Problem

During our earlier research, it was recognised that in order to be successful with an indirect genetic algorithm approach using a decoder, the decoder has to strike a balance between being an optimiser in its own right and finding feasible solutions. Previously this balance was achieved manually. Here we extend this by presenting an automated approach where the genetic algorithm itself, simultaneously to solving the problem, sets weights to balance the components out. Subsequently we were able to solve a complex and non-linear scheduling problem better than with a standard direct genetic algorithm implementation.     

招标采购中的采购物品打包模型及其优化算法

招标采购是采购物品(货物或服务)的有效方式, 采购物品打包是招标采购的重要环节并对采购绩效有重要影响。采购物品打包是确定一组互斥的采购包集合, 在充分的市场竞争条件下购买全部采购物品, 且每个采购包的候选供应商应对该采购包中的所有物品进行投标。目前采购物品打包问题的研究文献较少, 本文通过定义采购包和采购打包方案等概念, 建立了采购物品打包问题的0-1整数规划模型。由于该模型具有NP-hard特征, 为此首先将其转化为旅行商问题, 并基于遗传算法设计采购物品打包问题的求解算法。实验表明:与整数规划求解软件和双聚类算法相比, 本文所提出的算法在解决采购物品打包问题时具备更好的优化性能和计算效率。     

A Genetic Algorithm for the Multidimensional Knapsack Problem

In this paper we present a heuristic based upon genetic algorithms for the multidimensional knapsack problem. A heuristic operator which utilises problem-specific knowledge is incorporated into the standard genetic algorithm approach. Computational results show that the genetic algorithm heuristic is capable of obtaining high-quality solutions for problems of various characteristics, whilst requiring only a modest amount of computational effort. Computational results also show that the genetic algorithm heuristic gives superior quality solutions to a number of other heuristics.     

Efficient Genetic Algorithms Using Simple Genes Exchange Local Search Policy for the Quadratic Assignment Problem

In this paper, we describe an approach for solving the quadratic assignment problem (QAP) that is based on genetic algorithms (GA). It will be shown that a standard canonical GA (SGA), which involves genetic operators of selection, reproduction, crossover, and mutation, tends to fall short of the desired performance expected of a search algorithm. The performance deteriorates significantly as the size of the problem increases. To address this syndrome, it is common for GA-based techniques to be embedded with deterministic local search procedures. It is proposed that the local search should involve simple procedure of genome reordering that should not be too complex. More importantly, from a computational point of view, the local search should not carry with it the full cost of evaluating a chromosome after each move in the localized landscape. Results of simulation on several difficult QAP benchmarks showed the effectiveness of our approaches.