泾河年径流量BP神经网络模型研究

以1957—2000年的实测降水序列和泾河年径流量序列为研究对象,利用EMD法和GA建立了泾河年径流量的BP神经网络模型。分析结果表明:泾河流域年降水量变化可能存在准2～3、5～7、10～13、18～22 a的周期;基于EMD的年径流量BP神经网络模型预测值的相对误差为-4.71%～8.21%,基于GA的年径流量BP神经网络模型预测值的相对误差为2.25%～12.22%。     

遗传算法在图像匹配中的研究与改进

图像匹配是计算机视觉和图像分析中的一个基本问题,针对常规遗传算法在图像匹配中存在的问题,在文中引入混沌理论改进遗传算法,实验表明,改进的算法在图像匹配中具有更高的精度和速度,取得了较好的效果.     

最大访问间隔时间与成像侦察卫星星座

以区域成像侦察为背景,基于遗传算法提出了一个成像侦察卫星星座设计方法。研究了通过遗传算法模型优化设计侦察卫星轨道高度、轨道倾角、右升交点角及平近点角等轨道参数的方法。并对有3颗低轨道卫星的侦察卫星星座进行了优化仿真。结果表明,优化后的星座不仅在指定目标区域具有优异的最大访问间隔时间性能,而且具有一定的全球侦察能力。     

遗传算法在WSN路由协议设计中的研究与应用

和传统无线网络的节点相比,无线传感器网络的节点有其特殊的地方:电源能量有限,通信能力有限以及计算能力有限,网络拓扑结构更加不稳定,这些特性使得以前研究很多的无线自组织网的网络路由协议不能直接应用于无线传感器网络.提出基于遗传算法思想来设计和优化无线传感器网络的路由协议,使得源节点和目的节点之间以及中间节点之间存在多条最佳路径,节点在进行路由选择的同时,最大限度来保证网络各节点的总体能量消耗最少,最终保证整个网络的残存性能有进一步的提高.     

基于面向对象的智能决策支持系统模型库自动选择

面向对象的智能决策支持系统(IDSS)模型库管理系统的模型自动选择方法有多种.基于人工神经网络方法,通过人机交互接口将问题分解出的文字特征分解为问题的目标和初始态要素,再用智能识别器推理并选择模型.基于遗传算法的模型自动选择通过决策树表示以实现.基于自然语言理解的模型自动选择方法,通过加入问题识别模块实现.基于灰色关联度方法,通过自然语言理解法对用户的问题进行分析再选择适合问题的模型.     

基于改进遗传算法的标签印刷生产调度技术

针对标签印刷生产过程中存在的多品种、小批量、客户定制化程度高、部分生产工序存在不确定性等问题建立了以最小化最大完工时间为目标的柔性作业车间调度模型,提出了一种改进遗传算法(GA).首先,在标准遗传算法的基础上采用整数编码;然后,在选择操作阶段采用轮盘赌法,并通过引入精英解保留策略以确保算法收敛性;最后,提出动态自适应交...     

Generalized quadratic multiple knapsack problem and two solution approaches

The Quadratic Knapsack Problem (QKP) is one of the well-known combinatorial optimization problems. If more than one knapsack exists, then the problem is called a Quadratic Multiple Knapsack Problem (QMKP). Recently, knapsack problems with setups have been considered in the literature. In these studies, when an item is assigned to a knapsack, its setup cost for the class also has to be accounted for in the knapsack. In this study, the QMKP with setups is generalized taking into account the setup constraint, assignment conditions and the knapsack preferences of the items. The developed model is called Generalized Quadratic Multiple Knapsack Problem (G-QMKP). Since the G-QMKP is an NP-hard problem, two different meta-heuristic solution approaches are offered for solving the G-QMKP. The first is a genetic algorithm (GA), and the second is a hybrid solution approach which combines a feasible value based modified subgradient (F-MSG) algorithm and GA. The performances of the proposed solution approaches are shown by using randomly generated test instances. In addition, a case study is realized in a plastic injection molding manufacturing company. It is shown that the proposed hybrid solution approach can be successfully used for assigning jobs to machines in production with plastic injection, and good solutions can be obtained in a reasonable time for a large scale real-life problem.     

Biological-based genetic algorithms for optimized disaster response resource allocation

An effective disaster response requires rapid coordination of existing resources, which can be considered a resource optimization problem. Genetic algorithms (GAs) have been proven effective for solving optimization problems in various fields. However, GAs essentially use generation succession to search for optimal solutions. Therefore, their use of reproduction, crossover, and mutation operations may exclude optimal chromosomes during generation succession and prevent full use of previous search experience. Meanwhile, premature convergence caused by inadequate diversity of chromosome populations limits the search to a local optimum. Genetic algorithms also incur high computational costs. The biological-based GAs (BGAs) proposed in this study address these problems by including mechanisms for elite reserve areas, nonlinear fitness value conversion, and migration. This study performed experimental simulations to compare BGAs with immune algorithms (IAs) and GAs in terms of effectiveness for allocating disaster refuge site staff and for planning relief supply distribution. The simulation results show that, compared to other methods, BGAs can compute optimal solutions faster. Therefore, they provide a more useful reference when performing the decision-making needed to solve disaster response resource optimization problems.     

A class of multi-period semi-variance portfolio for petroleum exploration and development

Variance is substituted by semi-variance in Markowitz's portfolio selection model. For dynamic valuation on exploration and development projects, one period portfolio selection is extended to multi-period. In this article, a class of multi-period semi-variance exploration and development portfolio model is formulated originally. Besides, a hybrid genetic algorithm, which makes use of the position displacement strategy of the particle swarm optimiser as a mutation operation, is applied to solve the multi-period semi-variance model. For this class of portfolio model, numerical results show that the mode is effective and feasible.