一种运用遗传算法确定船舶航向控制模糊规则的方法

为了克服船舶航向控制模糊规则确定过程中的盲目性,引入一种与之相适应的遗传算法,以使结果成为某种意义下的最优解,从而从根本上解决这一重要问题.文中运用Matlab的C语言MEX-文件技术,将遗传算法源代码与Matlab直接结合起来,利用后者的强大建模能力,建立起完整的仿真模型.仿真结果表明,船舶的航向控制能力得到明显改善.     

Approximation results for min‐max path cover problems in vehicle routing

This article studies a min‐max path cover problem, which is to determine a set of paths for k capacitated vehicles to service all the customers in a given weighted graph so that the largest path cost is minimized. The problem has wide applications in vehicle routing, especially when the minimization of the latest service completion time is a critical performance measure. We have analyzed four typical variants of this problem, where the vehicles have either unlimited or limited capacities, and they start from either a given depot or any depot of a given depot set. We have developed approximation algorithms for these four variants, which achieve approximation ratios of max{3 ‐ 2/k,2}, 5, max{5 ‐ 2/k,4}, and 7, respectively. We have also analyzed the approximation hardness of these variants by showing that, unless P = NP , it is impossible for them to achieve approximation ratios less than 4/3, 3/2, 3/2, and 2, respectively. We have further extended the techniques and results developed for this problem to other min‐max vehicle routing problems.© 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

基于改进遗传算法的油料调拨优化

为实现油料快速调拨运输，利用改进的遗传算法求解油料调拨优化问题，采用了一种受贪婪算法启发的新的交叉算子和变异算子，对比模拟退火算法等其他算法而言，具有较好的鲁棒性，能很好地解决油料调拨运输过程中多任务多地点的油料调拨运输问题。     

无人直升机辨识的一种新方法

针对无人直升机的辨识建模问题,提出了把频率响应辨识法和遗传算法相结合的一种新的辨识方法.该方法充分结合了频率响应辨识法抗噪声能力强、对输入信号通用性强、以及遗传算法的全局寻优特性的优点,极大地提高了辨识的准确性.与最小二乘法、Levy方法的对比仿真结果表明,这种方法辨识精度更高,具有重要的工程使用价值.     

新算法在划船和伪划船效应中的应用

划船效应补偿是高精度捷联惯导系统解算的重要环节。通过研究其误差特性,推导了新的通用划船效应补偿公式。同时提出了角振动环境中伪划船效应的存在,对伪划船效应的产生原因、表达方式以及对捷联惯导系统的影响进行了分析。对新的补偿算法在划船效应和伪划船效应下进行了仿真试验。     

基于改进遗传算法的平流层飞艇定点悬停控制(英文)

平流层飞艇定点悬停控制是飞艇应用领域的关键技术。该控制问题可以转化为非线性多目标优化问题来求解。遗传算法是解决现代非线性多目标优化问题的一种重要方法。但是基本遗传算法在解决定点悬停飞艇实际控制问题时,具有易陷入局部最优解、在遗传进化过程中随机性较强、搜索效率低下及耗时多等缺陷。通过借鉴并行遗传算法、模拟退火算法和向量评价遗传算法的基本思想,在基本遗传操作中添加切断算子和拼接算子,设计一种改进遗传算法来改善上述缺陷。仿真结果表明该方法是有效可行的。     

基于改进协同遗传算法的有效载荷系统功能序列规划方法

针对传统回溯算法在求解基于知识模型的有效载荷系统功能序列规划问题中搜索效率低的问题,提出一种基于"择劣变异"(Worst Individual Mutation,WIM)策略的协同遗传算法(Co-evolutionary Genetic Algorithm,CGA)的改进算法WIM-CGA。该算法在遗传过程中采用双路线进化方案,即"择优实施标准遗传过程,择劣实施变异操作",达到提高求解精确度及搜索效率的目的。仿真结果表明,同等测试条件下,当功能规模为50,约束密度为1.0时,WIM-CGA算法在限定时间内最优解的平均精确度比优化的回溯算法提高了54.15%,比CGA算法提高了6.18%,且当所得解的精确度大于90%时,WIM-CGA算法比CGA算法的迭代次数减少了65.79%,耗时降低了48.97%,显著提高了功能序列规划的效率。     

Single batch machine scheduling with deliveries

We consider the problem of scheduling a set of n jobs on a single batch machine, where several jobs can be processed simultaneously. Each job j has a processing time p_j and a size s_j. All jobs are available for processing at time 0. The batch machine has a capacity D. Several jobs can be batched together and processed simultaneously, provided that the total size of the jobs in the batch does not exceed D. The processing time of a batch is the largest processing time among all jobs in the batch. There is a single vehicle available for delivery of the finished products to the customer, and the vehicle has capacity K. We assume that K = rD, where and r is an integer. The travel time of the vehicle is T; that is, T is the time from the manufacturer to the customer. Our goal is to find a schedule of the jobs and a delivery plan so that the service span is minimized, where the service span is the time that the last job is delivered to the customer. We show that if the jobs have identical sizes, then we can find a schedule and delivery plan in time such that the service span is minimum. If the jobs have identical processing times, then we can find a schedule and delivery plan in time such that the service span is asymptotically at most 11/9 times the optimal service span. When the jobs have arbitrary processing times and arbitrary sizes, then we can find a schedule and delivery plan in time such that the service span is asymptotically at most twice the optimal service span. We also derive upper bounds of the absolute worst‐case ratios in both cases. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 470–482, 2015     

改进遗传算法求解电力网攻击无人机分队任务分配

遗传算法因其出色的寻优能力,自提出后就被广泛用于工程技术上的最优化问题求解。根据电力网攻击无人机分队任务分配的实际需要,提出基于遗传算法思想的解决方案,并在原算法基础上,采用种群分组进化的策略进行改进。详细介绍算法的基本原理和仿真步骤,再举出实际算例进行仿真,实验结果证明算法的改进取得了良好效果。     

基于遗传神经网络算法后勤指挥决策能力预测

分析了后勤指挥的主要影响因素,运用信息论中的信息压缩原理,建立和优化了后勤机关指挥决策能力预测指标体系。建立了遗传算法优化的BP神经网络后勤机关指挥能力预测模型,消除了BP神经网络存在的初始权值和阈值难以确定的局限性。给出了预测实例,证明了该算法的合理性和有效性。