遗传算法的改进策略及其应用

针对基本遗传算法在优化应用中遇到的诸如局部搜索能力差、计算量大、对较大搜索空间适应能力差和早熟收敛等棘手问题,本文将传统的单纯形搜索算法与遗传算法结合,提出了一种混合遗传算法,并在进化操作中将交叉,变异概率与个体的适应度结合提出了一种个体自适应概率,使算法在产生新个体的同时,最大限度地保护优秀个体不受破坏,从而提高了整个算法的运算效率;在上述基础上又引入了加速循环操作以加强对优秀个体的利用率,进一步提高算法的效率.通过典型测试函数的数值算例验证,表明本文方法不但可以有效地克服遗传算法的上述缺陷,而且计算速度、稳定性、精度都有明显提高.     

多性能指标系统控制器自设计方法及其应用研究

针对具有多指标的被控对象,提出一种基于神经网络的控制器自设计方法.算法利用并行遗传算法按照被控对象各项性能指标进化神经网络控制器,在遗传算法每代结束时利用适应性权重法根据各项指标数据计算综合适应度值,选择综合适应度最佳个体进行遗传操作,从而获得综合性能指标最佳的控制器.将算法应用于异步电机矢量控制系统的速度控制器自设计中,仿真实验验证了本方法的有效性.     

遗传算法的改进策略及其应用

针对基本遗传算法在优化应用中遇到的诸如局部搜索能力差，计算量大，对较大搜索空间适应能力差和早熟收敛等棘手问题，本文将传统的单纯形搜索算法与遗传算法结合，提出了一种混合遗传算法，并在进行操作中将交叉，变异概率与个体的适应度结合提出一种个体自适应概率，使算法在产生新个体的同时，最大限度地保护优秀个体不受破霈，从而提高了整个算法的运算效率，在上述基础上又引入了加循环操作以加强对优秀个体的利用率，进一步提高算法的效率，通过典型测试函数的数值算例验证，表明本文方法不但可以有效的克服遗传算法的上述缺陷，而且计算速度，稳定性，精度都有明显提高。     

遗传算法在相控阵雷达波束驻留任务自适应调度中的研究

相控阵雷达在雷达体系的发展史上具有重要地位,其波束指向灵活,对空间和时间的分配可以控制,可以很好地完成搜索和多目标跟踪等任务。本文针对相控阵雷达波束驻留任务的调度问题,提出了自适应遗传算法这种解决方案。通过对雷达约束模型进行分析,设计相应的求解方法。主要运用遗传算法进行求解,包括编码方式、适应度函数、遗传操作等步骤。考虑到进化变异和进化适应的影响,提出变异概率和自适应交叉概率。     

改进遗传算法在船用核动力装置概率因果故障诊断中的应用

传统的遗传算法存在早熟现象严重和局部搜索精度较低的固有缺陷,容易导致分析结果与实际情况不相符,不能很好地用于船用核动力装置概率因果故障诊断。提出了一组综合改进策略,首先定义了奇异个体判断指标;而后设计了一种自适应交叉、变异策略和自适应局部搜索策略,并通过经典案例测试改进算法的有效性;最后构建改进算法与概率因果故障诊断模型,进行船用核动力装置故障诊断实例分析。分析结果对船用核动力装置故障诊断具有重要的指导意义,改进遗传算法是进行船用核动力装置故障诊断有效而实用的方法。     

自适应遗传算法的改进与应用

为提高遗传算法的全局最优和快速收敛,在现有的一些自适应遗传算法的基础上,针对交叉概率和变异概率进行改进,提出了一种根据适应度值自动调整交叉概率和变异概率的新的自适应遗传算法。实验结果表明,该算法在收敛快速性和稳定性等方面都有了明显的改善,达到了预期效果。     

绿色能源双体无人艇艇型综合优化分析

为了实现绿色能源双体无人艇的艇型最优设计,本文对艇型设计的多目标策略和智能优化算法进行研究。首先综合考虑太阳能和风帆以及快速性、操纵性、耐波性和抗倾覆性四大性能对艇型设计的影响,建立综合优化数学模型;然后基于遗传算法改编的综合优化设计软件确定总目标函数最优情况下的遗传次数、种群规模、变异概率和交叉概率;最后采用外部分层策略对遗传算法结合粒子群和混沌算法,进行了混合算法的比较分析。结果表明,相比于单一遗传算法,混合算法的优化效果更好,且在不同载波概率情况下,遗传算法+粒子群算法的优化效果均为最佳,外部分层策略可以有效提高寻优效果。     

虚拟机舱管路布局算法优化设计

针对当前船舶管路布局设计主要依靠经验丰富的设计人员手动敷设完成,为提高管路自动化布局效率,提出了一种基于改进遗传算法的三维空间管路设计方法,以辅助设计者完成管路布局工作。通过在经典遗传算法的整体框架中引入免疫选择机制,增加免疫检测算子和免疫平衡算子,改进了遗传算法仅依靠适应度值选择后代个体易造成算法陷入局部最优解的不足,维护了种群多样性,提高了算法运行效率。以船舶机舱为对象建立虚拟机舱仿真环境,采用栅格法对布局空间进行划分,以十进制浮点数编码方式进行算法编码设计。MATLAB仿真结果验证了改进遗传算法在管路布局优化设计问题上的可行性和搜索效率,采用C#语言编写脚本控制程序,在Unity3D虚拟机舱环境中实现了管路路径的布局设计。     

四旋翼无人飞行器的航迹规划

针对已知的局部地图,对现有的各种遗传算法进行了修改,提出一种改进型的遗传算法对四旋翼无人飞行器进行航迹规划。传统的遗传算法收敛慢,且容易陷入局部最优解或者“早熟”现象。针对这些同题,论文对交叉概率与变异概率进行自适应调整,自适应函数也进行了动态标定。实验结果表明该算法的可行性及可靠性。     

基于改进自适应遗传算法的船舶电力系统滤波装置优化配置

近年来随着大量非线性负载的投入使用，船舶电网谐波问题日益严重，直接影响到船舶电气设备的正常工作和使用寿命。论文结合船舶电力系统动态负荷多等特点采用节点电势法进行基波潮流计算，忽略传输线路压降，对非线性负荷进行迭代求解，因而收敛速度快，精度高。在迭代求解过程中，采用改进的自适应遗传算法。根据适应度值的集中程度自适应地变化整个种群的交叉概率和变异概率，增加了种群多样性，扩大了搜索空间。应用改进的自适应遗传算法对一个12节点船舶电力系统进行滤波器优化配置，优化结果表明了算法的有效性。     

Segmentation of complex objects'' sonar images using parameter-fixed MRF model

The effective method of the recognition of underwater complex objects in sonar image is to segment sonar image into target, shadow and sea-bottom reverberation regions and then extract the edge of the object. Because of the time-varying and space-varying characters of underwater acoustics environment, the sonar images have poor quality and serious speckle noise, so traditional image segmentation is unable to achieve precise segmentation. In the paper, the image segmentation process based on MRF (Markov random field) model is studied, and a practical method of estimating model parameters is proposed. Through analyzing the impact of chosen model parameters, a sonar imagery segmentation algorithm based on fixed parameters' MRF model is proposed. Both of the segmentation effect and the low computing load are gained. By applying the algorithm to the synthesized texture image and actual side-scan sonar image, the algorithm can be achieved with precise segmentation result.     

Segmentation of complex objects＇ sonar images using parameter-fixed MRF model

The effective method of the recognition of underwater complex objects in sonar image is to segment sonar image into target, shadow and sea-bottom reverberation regions and then extract the edge of the object. Because of the time-varying and space-varying characters of underwater acoustics environment, the sonar images have poor quality and serious speckle noise, so traditional image segmentation is unable to achieve precise segmentation. In the paper, the image segmentation process based on MRF （Markov random field） model is studied, and a practical method of estimating model parameters is proposed. Through analyzing the impact of chosen model parameters, a sonar imagery segmentation algorithm based on fixed parameters＇ MRF model is proposed. Both of the segmentation effect and the low computing load are gained. By applying the algorithm to the synthesized texture image and actual side-scan sonar image, the algorithm can be achieved with precise segmentation result.     

一种从红外序列图像中提取感兴趣区ROI的有效方法

红外序列图像中提取感兴趣区（ROI）是图像压缩的关键步骤,ROI检测质量的好坏直接作用影响着后续处理。文章在Stauffer等人提出的自适应混合高斯背景模型基础上,融入帧差法分别将背景区和运动区以不同的更新率进行更新,同时在自适应背景更新的同时,不断改进自适应学习率。试验表明该方法构建的背景模型有较好的适应性,既可以稳定的对背景进行实时更新,又能够快速的提取目标的感兴趣区。     

一种基于PSO优化HWFCM的快速水下图像分割算法

The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.     

基因演算法於船体加强板架之最佳化设计应用

船体结构之最佳化设计是一个复杂非线性的混和离散问题，并且要搜寻到全域的最佳值并不容易。在复杂的设计环境下基因演算法（Genetic Algorithm；GA）却可以搜寻到近似的全域最佳值。本文主要是应用基因演算法对T加强板架（Tee stiffened panel）、平板加强板架（flat-bat stiffened Panel）等常用且最具代表性之船体结构件进行最佳化设计，使结构在满足终极破坡限制（ultimate failure constraints）与耐用破坏限制（serviceability failure constraints）等所有限制条件下，求得最佳目标函数值中各设计变之最佳组合。在过程中并考量不同族群大小、变换机率、突变机率因素对最佳化结果的影响。文中是以制造成本为目标函数，其中同时考量材料成本及劳工成本，且所得之结果与连续性线性规则（Sequential Linear Programming；SLP）最佳化结果作了比较。计算的结果显示基因演算法可以有效地与快速地获得最小重量和最低成本的目标。     

Application of a genetic algorithm to the optimal structural design of a ship's engine room taking dynamic constraints into consideration

The genetic algorithm, known as GA, is used to optimize engine room structure, not only under static constraints, but also under dynamic constraints. A penalty function method is used to handle the complicated constraint conditions based on the numerical results of dynamic and static analyses. There are several ways to take the dynamic effect into account in the optimum design of ship structure. First, the inequality constraint condition is applied to separate the natural frequency and the exciting frequency. Second, generalized design variables are introduced in order to transfer not only the dynamic but also the static equilibrium equations into the equality constraints, resulting in the optimal structural design without the need to solve these equilibrium equations. Third, the magnitudes of the acceleration and displacement are constrained instead of applying the natural frequency constraint condition. In order to achieve better convergency in the optimization with least resources, several operators and methods are considered and then introduced into the structural design of the engine room. The new operator, called either objective elitism or fitness elitism, is introduced to improve the efficiency of the method. The effect of boundary mutation and nonuniform mutation on the performance of the GA is examined. Not only binary representation but also floating-point representation are used to express the design gene in the GA. Fuzzy theory is applied in the GA to handle the uncertainty of the constraint conditions. Two ways of solving fuzzy optimization are investigated in order to obtain a fuzzy solution and a crisp solution. Received: October 2, 2000 / Accepted: November 30, 2000     

A combined tramp ship routing,fleet deployment,and network design problem

In this paper, a tramp ship routing model of fleet deployment in a hub-and-spoke network is presented. This model simultaneously determines the selection of hubs, the assignment of spokes to hubs, the deployment of feeder-containerships as well as containership routing between spokes and spokes, hubs and spokes, and hubs and hubs. Even though some parts have been studied, this complex combination of shipping problems has never been addressed. Because the problem is NP-hard, a genetic algorithm (GA) with local search is proposed. In the algorithm, a cut-off procedure is applied to fleet deployment in a sub-route strategy. A number of randomly generated problem instances are solved by both a mathematical program and the GA with local search. A simple but realistic heuristic algorithm is also developed. Both the GA with local search and the heuristic algorithm are used to solve a number of real case instances. A comparison of the results shows the efficiency of the GA with local search. The developed model can be used as a route-decision support tool for shipping companies that provide long-haul shipping services in a hub-and-spoke network.     

大范围环境下自主式水下潜器两种全局路径规划方法的研究

应用遗传算法(GA)和A·算法对自主式水下潜器(简称AUV)在大范围海洋环境中的全局路径规划问题进行了研究.介绍了基于栅格的环境模型及其数据结构,讨论了GA的染色体编码方式、基于知识的初始种群生成方法与适应度函数,基于领域知识设计了五种遗传算子,给出了A·算法的具体实现方法.通过仿真结果可以看出:GA采用可变长编码方式使路径描述简单、清晰,具有收敛速度快、求解实际问题效率高的特点;A*算法可在较短时间内求得相对栅格优化的路径.两种算法均可满足系统实时性要求.     

基于区域模糊直方图的图像检索

将图像分割算法和模糊理论相结合,提出了基于区域模糊直方图的图像检索方法。用快速聚类算法对图像进行区域分块,以各区域的视觉特征为基础,建立图像模糊直方图并进行图像检索。在1000幅通用图像库上的实验表明,该方法可以得到比较满意的检索结果。