基于高速单体无人艇航行性能的并行优化方法

基于高速单体无人艇航行性能建立包括快速性、操纵性和耐波性的综合优化数学模型,构造混合的遗传混沌算法(GAPCA)和混沌遗传算法(PCAGA),并编制相关软件对该模型进行综合优化计算,探讨混合算法和单一算法(遗传算法(GA)和混沌算法(CA))优化性能的优劣,同时研究二次载波搜索域区间比例和区间并行次数对优化效率的影响,得到遗传混沌优化算法和混沌遗传优化算法相比单一优化算法寻优效率高,不同算法对应于不同的最佳二次区间搜索比例。     

基于多目标遗传算法的复杂系统可靠性冗余优化

提出了将多目标遗传算法用于复杂系统可靠性冗余优化设计的方法。根据系统可靠性冗余的模型,采用多目标遗传优化技术,同时考虑可靠性、费用和重量目标,得到了冗余系统的Pareto最优解。设计者能在这些Pareto解中根据偏好选择最后的折中解。计算了1个3目标的可靠性冗余优化的例子,数值结果表明,多目标遗传算法能迅速搜索到可靠性冗余指标的Pareto解,并且这些解的分布均匀。     

遗传模拟退火算法舰船资源装配效率优化系统

为了解决舰船资源装配工作中,由于装配顺序不确定导致的资源装配效率低的问题,从硬件和软件2个方面,利用遗传模拟退火算法设计舰船资源装配效率优化系统。在硬件方面安装ESC500型号的控制器,并改装系统中的微处理器。在硬件设备支持的基础上,分别通过准备舰船装配资源信息、遗传模拟退火算法优化船舶装配序列和装配效率计算与优化3个步骤,实现对舰船资源装配效率的优化。经过系统测试实验分析得出结论,应用设计的装配效率优化系统后,舰船资源的装配效率得到了有效的提升,且可以实现多舰船装配效率的同步优化。     

加速混沌优化算法的改进及其在船型论证中的应用

利用混沌运动所特有的随机性、遍历性和规律性,对加速混沌优化算法进行一些改进,提高了混沌优化算法的收敛速度和精确性.改进的加速混沌优化算法利用混沌运动的性质,同时不断缩小变量的搜索空间,在混沌优化搜索过程中,以具有一定保证的当前最优解为中心,不断地缩小优化变量的搜索区间,调整细化参数,促使搜索能够更快、更有效地向全局最优解收敛.将该改进算法应用到具体的船型论证中,不仅证明了该改进算法简单,容易实现,具有较高的收敛速度和可靠性,而且具有较高的寻优精度和较少的优化迭代次数,也体现了该算法在船型论证领域中的实用性和有效性.     

基于P3软件的网络计划资源优化方法研究

分析传统工期-资源优化方法的不足,提出应用P3软件进行资源优化,将其运用到船台大合拢分段吊装计划管理中,证实了该软件在资源优化方法上具有一定的多样性和灵活性。     

蚁群算法和遗传算法结合的航线生成

为在设计航线时获取最优航线,研究蚁群算法与遗传算法相结合的航线生成方法,讨论海图信息离散化、蚁群搜索产生航线初始种群、遗传优化获取最优航线等问题。介绍海图信息网格结构及网格属性计算方法,蚁群搜索航线的策略及信息素的更新,遗传优化航线的各种操作方法,航线辅助生成系统的功能,验证方法的可行性和模型的准确性。     

多约束条件下多UUV任务分配方法

针对多约束条件下多UUV任务分配问题,提出一种基于改进集合粒子群优化算法的分配方法,该方法首先定义包含UUV与保障船有效通信距离等指标的约束条件,建立以任务总执行时间最短为主要优化对象的目标函数,采用自适应惯性系数提高最优解邻域内的搜索能力,通过结合遗传算法的变异操作提高全局搜索能力,有效降低陷入局部最优解的概率。UUV任务分配仿真实验表明,本文提出的多约束多目标任务分配方法能够获取多UUV的最优任务分配,具有实际应用的可行性。     

双资源约束下的船舶管件柔性生产车间调度优化

针对船舶管件柔性车间的复杂制造环境，建立以完工时间为主要优化目标、以设备资源和辅助资源为约束条件的双资源约束柔性生产车间调度问题模型，设计一种改进布谷鸟算法对其进行求解。针对问题特征及复杂性，在布谷鸟算法核心框架不变的基础上，提出一种莱维飞行自适应步长因子；同时，设计基于2种局部搜索的巢寄生策略，并引入模拟退火算法，以解决工序维、设备维和资源维的调度优化问题。将该算法应用于企业实际生产模型，结果表明：改进算法能有效避免设备资源冲突，具备较好的综合调度特性。     

船型优化的变分伴随方法

提出了1种船型优化的思想方法,力图把CFD计算与最优化理论和方法进行有机结合,进而组成1个自动循环搜索最优船型的数值计算系统。对船型优化过程进行了理论探讨,提出用伴随方法进行最优船型确定的数值计算程序,对船舶流体力学和变分最佳控制理论结合而产生的应用前景进行了展望。     

基于改进遗传算法的舾装件托盘多载具协同拣选方法

为提升舾装件托盘的拣选效率,建立拣选过程的数学模型,提出一种基于改进遗传算法（Improved Genetic Algorithm,IGA）的舾装件托盘多载具协同拣选方法。针对遗传算法（Genetic Algorithm,GA）流程与实际拣选过程的差异,改进GA的初始化过程和染色体交叉方式,并对变异过程进行更贴近实际生产的修改。针对GA难以得到全局最优解的问题,采用变邻域搜索（Variable Neighborhood Search,VNS）策略降低陷入局部最优解的可能性。采用实例计算验证该算法的有效性,可优化传统舾装件托盘拣选方法。     

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.     

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。     

Optimization of Wigley Hull Form in order to Ensure the Objective Functions of the Seakeeping Performance

The research performed in this paper was carried out to investigate the computational procedure to design seakeeping optimized ship hull form. To reach the optimized hull form, four stages should be done, which consists of： generate alternative hull form, seakeeping calculations, objective functions and optimization techniques. There are many parameters that may be determined in ship hull form optimization. This paper deals with developed strip theory for determining the seakeeping performance, genetic algorithm （GA） as optimization method, high order equations for curve fitting of the hull form and finally reaching to the minimum bow vertical motion in regular head waves. The Wigley hull is selected as an initial hull and carried to be optimized. Two cases are considered. For the first case, the only form coefficients of the hull （CB, CM, Cw, Cp） are changed and main dimensions （L, B, 7） are fixed. In the second case both hull form and main dimensions are varied simultaneously. Finally, optimized hull form and its seakeeping performances are presented. The results of optimization procedure demonstrate that the optimized hull forms yield a reduction in vertical motion and acceleration.     

基于GA和SVM的水火弯板焰道布置优化方法

水火弯板是目前船舶建造中普遍采用的曲型外板加工方法,由于机理复杂,其加工过程参数确定往往依赖于工人的加工经验,难以实现标准化和规范化。文章提出基于遗传算法(GA)和支持向量机(SVM)的水火弯板焰道布置优化方法,以成形曲面和设计曲面之间的形状误差最小为优化目标,将焰道布置优化问题分解成计算最小形状误差和搜寻最佳匹配位置两个问题,并分别运用嵌套GA和SVM来实现这两个问题的求解。实例验证表明该方法能快速高效确定最优的加热线布置方案。     

基于GSA-IPM算法的船舶电力系统无功优化

提出GSA-IPM(万有引力-内点)算法求解船舶电力系统无功优化问题,以降低船舶电力系统的有功损耗,提高电压质量,改善安全经济运行水平。将算法应用于某实际船舶电力系统进行仿真测试,结果与万有引力搜索算法(GSA)、遗传算法(GA)及粒子群算法(PSO)作比较,证明了算法能够使有功网损更低,电压质量更佳,从而验证了方法的有效性。     

海上搜救志愿者队伍建设

搜救志愿者已成为海上搜救应急队伍的重要组成部分。我国改革开放和现代化进程的不断深入,为海上搜救志愿者队伍建设提供了有利条件。文章对国内外搜救志愿者队伍的现状和所面临的形势进行了分析,提出了开展搜救志愿者队伍建设的相关建议。     

Analysis of the continuous berth allocation problem in container ports using a genetic algorithm

The optimal usage of berths plays a key role in raising the efficiency of container terminals. The berth allocation problem in a container terminal is defined as the feasible allocation of berths to incoming ships such that the total time that elapses between the arrival of the ships to their exit from their berths is minimized. In the transportation literature, the latter problem is usually formulated as a mixed integer programming model. Optimization methods, like the branch and bound algorithm, are efficient ways to solve this model but become absolutely unusable when the size of the problem increases. An advanced search method such as GA may be suited to such a situation. In this paper, a genetic-based algorithm is proposed for the problem. Computational results for two test problems (a small and a large-sized problem) are also presented. The results from the small test are also compared with the results obtained from the branch and bound algorithm.     

Establishment and Optimization of State Feature System of Diesel Engine Fault Diagnosis

For too many state features are used in the diesel engine state evaluation and fault diagnosis,it is not easy to obtain the rational eigenvalues.In the paper,the cylinder subassembly of diesel engine is used to search for the method of establishing state feature system and optimal approach.The signal of diesel engine has been collected when the piston ring and airtight ring are working at different states,then with the Bootstrap method and Genetic Algorithm(GA),an optimum parameter combination is received.Example shows this method is simple and efficient for establishing diesel engine state feature system,Thus,this method is valuable for the virtual state evaluation of similar complex system.     

Basic design of a series propeller with vibration consideration by genetic algorithm

Genetic algorithms (GAs) can powerfully search for parameters in a large multidimensional design space. Thus, the principle can be applied to preliminary series propeller design problems with multiple considerations. In the present study, B-series propeller design was conducted using a GA for both hydrodynamic efficiency and vibration consideration. The objective function was set by users who could freely weight the relative importance of efficiency and vibration. GAs were successfully shown to be able to obtain an optimal set of parameters leading to efficient performance and low vibration.     

螺旋桨叶剖面优化设计比较研究

比较研究了船舶螺旋桨最高空泡起始航速的叶剖面优化设计方法。优化设计方法由剖面参数表达、空泡斗预报和最佳空泡起始性能遗传算法翼型优化三部分组成。Eppler方法采用10个参数表达任一翼型,已被有效应用到最高起始航速的螺旋桨叶剖面优化设计中。除了Eppler方法中的设计参数,剖面参数表达也可采用B样条曲线的控制点参数实现。对上述两种剖面参数表达方法进行了叶剖面优化设计比较研究,得到了一些结论。