基于改进蚁群算法的侦察无人机航路规划与实现

文章针对侦察无人机航路规划这一问题,分析了影响航路规划的因素,构建了航路规划的模型.结合侦察无人机航路规划的特点与模型,论证了基于蚁群算法求解的理由与优点,并对蚁群算法的初始信息素强度与启发因子进行了改进.最后以岛屿进攻战役这一特定作战任务为例,利用MATLAB实现了侦察多目标时的航路规划问题.     

基于多因素的卡尔曼滤波模型在滑坡变形预测中的应用

分析了滑坡变形预测模型的研究现状.考虑到滑坡的变形主要受到降雨及温度等因素的影响,建立基于时效分量、降雨分量和温度分量的多因素变形预测模型,然后将基于多因素的变形预测模型的模型参数看作带有动态噪声的状态向量,建立基于多因素的卡尔曼滤波模型,以基于多因素的卡尔曼滤波模型为基础,对滑坡的变形进行预测.由于基于多因素的卡尔曼滤波模型在卡尔曼滤波过程中,模型的参数不断发生变化,从而增强了模型适应观测数据的能力,提高了模型的拟合精度和预测精度.实例计算表明用基于多因素的卡尔曼滤波模型对滑坡的变形进行预测,其预测误差较小,预测效果较为理想.     

一种基于模糊多目标决策的炮兵火力优化方法

适应现代炮兵作战的战术思想和要求，深入分析影响炮兵目标威胁度和炮兵火力单位射击有利度的诸因素，构建了基于模糊多目标决策算法的目标威胁度评估和火力优化模型，通过实例仿真，表明所建立的优化模型能有效提高炮兵辅助指挥决策的可信度。     

基于正交实验的突破行动计划拟定

阐述突破行动在进攻作战中的重要地位和面临的挑战,基于正交实验方法在EINSTein平台上获取突破行动作战仿真数据,运用统计分析的方法,分析各作战因素的重要性排序及交互作用,拟定单目标和多目标情况下突破行动的作战计划.     

基于云重心法评估复杂电磁环境下炮兵信息化作战能力

炮兵信息化作战能力评估是一个复杂的多指标综合体系,包含许多随机和模糊因素.特别是在复杂的电磁环境下,对炮兵的信息系统和信息化作战能力影响巨大,更增加了炮兵信息化作战能力的不确定性.结合炮兵信息化作战能力评估模型,为能在建模分析过程中更好的处理较多的模糊指标,所以运用"云重心"理论确定了评估模型中各指标基本评定值,增强了模型的可信度和可操作性.较准确的反映了炮兵在复杂的电磁环境下信息化作战能力,为以后评估炮兵在复杂的电磁环境下信息化作战能力提供了准确的依据和方法.     

利用马尔科夫链模型对多点地质统计建模影响因素分析及优化

多点地质统计方法作为一种随机建模方法,在很多领域得到了广泛的应用,取得了很好的效果.但模拟过程中随机性强,难以控制其模拟效果.以地震尺度的精细离散模型(地震相)为训练图像,通过大量的实验探索,分析多点模式的选取对多点地质统计方法模拟效果的影响,提取模式大小、模式形态(各向异性)以及多级网格约束三个因素对建模结果影响.模式大小应根据模拟目标的尺度进行选择,模式的形态应与模拟地质目标的各向异性一致,多级网格控制的多少应权衡模拟目标的复杂性和计算速度.成功应用马尔科夫链模型,评价模型的空间结构恢复效果,对影响参数遍历设置和组合优化,探索了训练图像的最优多点模式参数设置组合.     

基于熵的多目标多维模糊决策模型

顾及多目标多维决策中存在的模糊性和随机性,基于Jaynes的信息熵最大原理,提出一种模糊环境下带有主观监督因子和信息熵的目标函数,导出了新的计算模糊决策识别矩阵与目标权重的模糊交叉计算公式.该模型将基于目标的客观决策与主观决策有机结合起来,为求解最优模糊决策识别矩阵和确定目标最优权重提供了一种有效途径,并把信息熵作为评价决策优劣的指标,进一步发展了多目标多维模糊决策理论模型.将本文提出的模糊决策方法应用于16家电炉炼钢企业的模糊综合评价,取得了较为满意的效果.     

基于被掩护目标相关性的防空兵力分配模型

反空袭作战中防空兵兵力分配 ,不仅需要考虑被掩护目标的自身价值 ,还要考虑被掩护目标的联合价值 .利用合作对策 ,依据被掩护目标对完成作战任务的相互影响 ,即相关性 ,建立了防空兵兵力分配模型 .     

基于主成分分析和支持向量机的威胁判断模型

目标威胁判断是防空作战中一项重要内容,在建立目标威胁模型时,首先要挑选特征参数,分析了影响威胁度的若干因素.这里采用主成分分析方法选择目标的特征参数;利用支持向量机建立了威胁判断模型,通过实例与神经网络法的结果进行了比较,结果表明支持向量机比较精确和简单.     

截击机机载武器装备最优规划的遗传算法研究

机载武器的合理配置是飞机作战效能得以充分发挥的前提条件.本文给出了截击机拦截多目标时的效能评估指标及其与机载武器系统之间的关系模型,并以遗传算法确定了截击机作战效能与武器装备及相关飞行参数的量化关系.经过仿真计算成功地解决了截击机在一定作战条件下,效能指标达到最大时武器装备的最优规划问题.     

基于改进的证据推理方法的多指标决策模型

对于一个多指标决策问题，证据理论可以通过构造辨识框架和基本概率分配函数、采取递归的证据合成方法。计算出原始数据在反映多个指标联合作用的情况下对不同判别结果的支持程度，并可以在信息复杂或数据不完整的条件下做出评估决策。本文首先建立基于证据推理的多指标评估问题的基本模型，然后引入了模糊数据方法以处理具有模糊概念或推理关系的复杂问题，同时还考虑了实际问题中可能出现加权证据或者相关证据的情况，其目的是为了建立一套具有实用性的、准确有效的多指标评估模型。文章最后设计一个风险评估的算例，分析了该方法的优点以及需要进一步完善之处。     

Genetic algorithm-based multi-criteria project portfolio selection

Project portfolio selection is one of the most important decision-making problems for most organizations in project management and engineering management. Usually project portfolio decisions are very complicated when project interactions in terms of multiple selection criteria and preference information of decision makers (DMs) in terms of the criteria importance are taken into consideration simultaneously. In order to solve this complex decision-making problem, a multi-criteria project portfolio selection problem considering project interactions in terms of multiple selection criteria and DMs?? preferences is first formulated. Then a genetic algorithm (GA)-based nonlinear integer programming (NIP) approach is used to solve the multi-criteria project portfolio selection problem. Finally, two illustrative examples are presented for demonstration and verification purposes. Experimental results obtained indicate that the GA-based NIP approach can be used as a feasible and effective solution to multi-criteria project portfolio selection problems.     

A GRASP metaheuristic for humanitarian aid distribution

Large scale disasters, natural or human-made, have huge consequences on people and infrastructures. After a disaster strikes, the distribution of humanitarian aid to the population affected is one of the main operations to be carried out, and several crucial decisions must be made in a short time. This paper addresses a last-mile distribution problem in disaster relief operations, under insecure and uncertain conditions. A model is presented that takes into account the cost and time of operation, the security and reliability of the routes, and the equity of aid handed out. The output of the model consists of a detailed set of itineraries that can be used to build an implementable distribution plan. Given its high complexity, the resulting problem is solved using a multi-criteria metaheuristic approach. In particular, a constructive algorithm and a GRASP based metaheuristic are developed, which are tested in a case study based on the 2010 Haiti earthquake.     

An Interactive Production Planning System in the Chemical Industry

This paper offers an example of a practical production planning system as used routinely by BP Chemicals Ltd. It is based on the interactive control of a linear programming model embedded in a system for data collection and report production. The model solves a "product mix" type of problem with a multi-criteria objective function.     

基于实验设计的概率语言权重信息多准则决策方法

作为模糊语言集的一种,概率语言集能够反映决策问题中评价准则权重重要程度及其概率,这在决策问题中较为实用。本文运用实验设计(Design of Experiment,DOE)的方法,对权重信息以概率语言给出的多准则决策问题进行研究:通过多次模拟权重重要程度及其概率,设计出多组实验权重,并用GRA法求得每组实验数据的得分,获得描述该决策问题准则值与评价结果相互影响关系的最适用回归方程,将决策问题的实际数据带入方程进行运算,得出方案排序结果。通过快速成型过程、柔性制造系统过程两个案例,进行对比验证。结果表明所提出的方法能较好地处理基于概率语言权重信息的多准则决策问题,计算简单且速度快,可重复性强且颇为实用。     

Shortest-route formulation of mixed-model assembly line balancing problem

A shortest-route formulation of the mixed-model assembly line balancing problem is presented. Common tasks across models are assumed to exist and these tasks are performed in the same stations. The formulation is based on an algorithm which solves the single-model version of the problem. The mixed-model system is transformed into a single-model system with a combined precedence diagram. The model is capable of considering any constraint that can be expressed as a function of task assignments.     

Multi-criteria analysis of the impacts of energy alternatives: A survey and a new comparative approach

This paper deals with the problem of comparison of energy alternatives from the social and public health point of view. First the conditions required for an accurate comparison are outlined. Then a large multi-criteria model is carried out in order to provide the framework for comparison of six energy chains: oil, coal, nuclear, solar thermal Themis, solar thermal Thek and solar photovoltaïc specified in the French conditions.The method used is the ELECTRE type and is therefore based on an analysis of the concordance and discordance of criteria. It also allows us to take into account uncertainty, since modelling of preferences is done by fuzzy outranking relations. We then developed four energy strategies based on realistic hypotheses, which allowed us to take into consideration the points of view of the various social groups involved in this problem of energy choices. The results we arrived at coincide with what are known to be the stands of these groups. This makes the method a particularly useful tool, not only to help justify a choice, but above all as a decision-aid.     

Dynamic multi-criteria evaluation of co-evolution strategies for solving stock trading problems

Risk and return are interdependent in a stock portfolio. To achieve the anticipated return, comparative risk should be considered simultaneously. However, complex investment environments and dynamic change in decision making criteria complicate forecasts of risk and return for various investment objects. Additionally, investors often fail to maximize their profits because of improper capital allocation. Although stock investment involves multi-criteria decision making (MCDM), traditional MCDM theory has two shortfalls: first, it is inappropriate for decisions that evolve with a changing environment; second, weight assignments for various criteria are often oversimplified and inconsistent with actual human thinking processes.In 1965, Rechenberg proposed evolution strategies for solving optimization problems involving real number parameters and addressed several flaws in traditional algorithms, such as their use of point search only and their high probability of falling into optimal solution area. In 1992, Hillis introduced the co-evolutionary concept that the evolution of living creatures is interactive with their environments (multi-criteria) and constantly improves the survivability of their genes, which then expedites evolutionary computation. Therefore, this research aimed to solve multi-criteria decision making problems of stock trading investment by integrating evolutionary strategies into the co-evolutionary criteria evaluation model. Since co-evolution strategies are self-calibrating, criteria evaluation can be based on changes in time and environment. Such changes not only correspond with human decision making patterns (i.e., evaluation of dynamic changes in criteria), but also address the weaknesses of multi-criteria decision making (i.e., simplified assignment of weights for various criteria).Co-evolutionary evolution strategies can identify the optimal capital portfolio and can help investors maximize their returns by optimizing the preoperational allocation of limited capital. This experimental study compared general evolution strategies with artificial neural forecast model, and found that co-evolutionary evolution strategies outperform general evolution strategies and substantially outperform artificial neural forecast models. The co-evolutionary criteria evaluation model avoids the problem of oversimplified adaptive functions adopted by general algorithms and the problem of favoring weights but failing to adaptively adjust to environmental change, which is a major limitation of traditional multi-criteria decision making. Doing so allows adaptation of various criteria in response to changes in various capital allocation chromosomes. Capital allocation chromosomes in the proposed model also adapt to various criteria and evolve in ways that resemble thinking patterns.     

An efficient and simple model for multiple criteria supplier selection problem

Simply looking for vendors offering the lowest prices is not “efficient sourcing” any more. Selection of suppliers is a multiple criteria decision. We propose a weighted linear program for the multi-criteria supplier selection problem. In addition to mathematical formulation, this paper studies a transformation technique which enables our proposed model to be solved without an optimizer. The model for multi-criteria supplier selection problem can be easily implemented with a spreadsheet package. The model can be widely applied to practical situations and does not require the user with any optimization background.     

模糊最短路的一种算法

模糊最短路问题在许多领域有着广泛的应用,研究这一问题具有重要意义。根据多准则决策理论求非被支配路径集合,求最大效用模糊最短路以及利用模糊数排序方法求模糊最短路是常用的三种研究方法,本文利用OERI排序原理,使网络模糊边长具有线性可加性,对具有三角模糊数边权的网络给出了一种标号算法,该算法简单高效,且易于在计算机上实现,算法的时间复杂度为O(n^2)。