Weibull分布参数的粒子群算法估计

基于广西涠洲岛海洋监测站3个方向的年极值波高观测资料,在假设其服从Weibull分布的基础上,运用最小二乘法,矩估计法和最速下降法对Weibull分布的参数进行估计,同时引入粒子群算法确定Weibull分布的3个参数,对文中4种方法得出的拟合结果及运算效率进行比较分析,说明了粒子群算法在估计极值分布参数中的优势.     

基于改进量子粒子群算法的AUV路径规划研究

针对海洋环境下自主水下机器人（AUV）的路径规划问题,提出了一种基于框架四叉树的改进量子粒子群算法（QPSO）,首先使用框架四叉树的方法对障碍物建模,该方法提高了建模的精度且对后续算法的效率也有极大的改进,之后设计改进的量子粒子群算法,并且结合水下环境的特殊性设计适应度函数,综合考虑航线路径长度、偏转角度以及海流影响,使得算法可以在水下环境中寻得能耗最短的解路径。最后通过仿真试验验证,相比于传统的栅格法和粒子群算法,改进量子粒子群算法的运算时间更短,收敛速度更快,其独特的适应度函数可以使AUV能更好适应水下多变的环境,且能利用海流设计能耗更小的路径,具有很大的实用价值。     

Research on Bulbous Bow Optimization Based on the Improved PSO Algorithm

In order to reduce the total resistance of a hull, an optimization framework for the bulbous bow optimization was presented. The total resistance in calm water was selected as the objective function, and the overset mesh technique was used for mesh generation. RANS method was used to calculate the total resistance of the hull. In order to improve the efficiency and smoothness of the geometric reconstruction, the arbitrary shape deformation (ASD) technique was introduced to change the shape of the bulbous bow. To improve the global search ability of the particle swarm optimization (PSO) algorithm, an improved particle swarm optimization (IPSO) algorithm was proposed to set up the optimization model. After a series of optimization analyses, the optimal hull form was found. It can be concluded that the simulation based design framework built in this paper is a promising method for bulbous bow optimization.     

PSO-BP网络算法在运动要素解算中的应用

为了改善被测目标运动要素计算精度,提出了采用 PSO-BP 神经网络算法作为运动要素解算的方程。 该算法将粒子群算法作为 BP 神经网络的学习算法,提高 BP 网络的全局收敛性和收敛速度,将观测到的运动目标参数作为 PSO-BP 神经网络的输入,并将运动目标的方位作为主要输出量,将运动目标的方位值与误差期望值进行比较并作为 PSO 的输入修改 BP 网络权值,进而得到高精度 BP 神经网络。 对该算法进行仿真计算,结果表明:基于该算法的运动目标运动要素解算,尤其是运动方位的解算器精度可以达到 0. 128°,提高了运动要素解算的精度和速度。     

在建导管架平台圆管风致涡激振动及减振研究

研究了大型导管架平台卧式建造时出现单边约束圆管的风致涡激振动问题。将建造过程中单边约束的导管架圆管简化成悬臂梁模型,采用van der Pol尾流振子模型模拟圆管受到的流体力,建立了圆管风致涡激振动动力学方程。使用伽辽金方法对建立的运动方程进行求解,数值分析了圆管在特定约化风速下的涡激振动特性。在圆管上附加非线性能量阱(NES)作为被动减振装置对圆管进行减振,并采用粒子群优化算法对NES的非线性刚度和阻尼参数进行了优化。结果表明,在约化速度为4.8时,得到的NES优化的非线性刚度和阻尼参数组合可以有效减小悬臂梁自由端风致涡激振动位移。本研究说明采用NES和粒子群优化算法可以有效减小圆管的风致涡激振动,为导管架安全建造提供一定的参考。     

顾及恶劣海况的船舶救援路径规划

为了保障救援船舶在恶劣海况环境下能够安全、高效地到达救援区域,本文改进人工鱼群算法开展海上救援路径规划研究。选取地形、海风和海浪等指标进行风险度量,采用GIS（geographical information system）多指标决策方法构建海洋环境威胁场,通过粒子群算法计算救援路径规划的最佳参数,并实现海上救援路径搜索。实验表明,基于改进的人工鱼群算法规划的船舶救援路径效果更优,可为海上应急救援提供辅助决策参考。     

A CPSO-SVM Model for Ultimate Bearing Capacity Determination

In this study, the CPSO-SVM models, which combine chaotic system, particle swarm optimization (PSO) and support vector machine (SVM), are presented and applied to predict the ultimate bearing capacity of shallow foundations. Chaotic mapping enjoys certainty, ergodicity and the stochastic property. Chaotic PSO (CPSO) increases the convergence rate of PSO and precision of the results through introducing chaos mapping into the particle swarm optimization algorithm. Since the selection of parameters for SVM is crucial to its performance of prediction, the CPSO is adopted to search for the optimal parameters. The proposed methods are used to predict the ultimate bearing capacity of shallow foundations based on data of load tests. Results indicate that the proposed methods can appropriately describe the relationship between ultimate bearing capacity and its affective factors, and make good predictions.     

Tidal Turbine Array Optimization Based on the Discrete Particle Swarm Algorithm

In consideration of the resource wasted by unreasonable layout scheme of tidal current turbines, which would influence the ratio of cost and power output, particle swarm optimization algorithm is introduced and improved in the paper. In order to solve the problem of optimal array of tidal turbines, the discrete particle swarm optimization (DPSO) algorithm has been performed by re-defining the updating strategies of particles’ velocity and position. This paper analyzes the optimization problem of micrositing of tidal current turbines by adjusting each turbine’s position, where the maximum value of total electric power is obtained at the maximum speed in the flood tide and ebb tide. Firstly, the best installed turbine number is generated by maximizing the output energy in the given tidal farm by the Farm/Flux and empirical method. Secondly, considering the wake effect, the reasonable distance between turbines, and the tidal velocities influencing factors in the tidal farm, Jensen wake model and elliptic distribution model are selected for the turbines’ total generating capacity calculation at the maximum speed in the flood tide and ebb tide. Finally, the total generating capacity, regarded as objective function, is calculated in the final simulation, thus the DPSO could guide the individuals to the feasible area and optimal position. The results have been concluded that the optimization algorithm, which increased 6.19% more recourse output than experience method, can be thought as a good tool for engineering design of tidal energy demonstration.     

智能优化平台在等效水深截断系统设计中应用

构建一个智能优化平台,包括粒子群算法、蚁群算法、遗传算法、模拟退火算法、混沌算法和复合形法。该平台包括四个模块,分别是优化问题选择,优化参数设置,优化过程显示以及优化结果输出。将该优化平台应用在等效水深截断系统的优化设计中,对截断系泊系统的构造参数进行优化,能在很短时间内给出符合海洋工程需要的等效水深截断系泊系统。     

Steady-State Configuration and Tension Calculations of Marine Cables Under Complex Currents via Separated Particle Swarm Optimization？

Under complex currents, the motion governing equations of marine cables are complex and nonlinear, and the calculations of cable configuration and tension become difficult compared with those under the uniform or simple currents. To obtain the numerical results, the usual Newton-Raphson iteration is often adopted, but its stability depends on the initial guessed solution to the governing equations. To improve the stability of numerical calculation, this paper proposed separated the particle swarm optimization, in which the variables are separated into several groups, and the dimension of search space is reduced to facilitate the particle swarm optimization. Via the separated particle swarm optimization, these governing nonlinear equations can be solved successfully with any initial solution, and the process of numerical calculation is very stable. For the calculations of cable configuration and tension of marine cables under complex currents, the proposed separated swarm particle optimization is more effective than the other particle swarm optimizations.     

Steady-State Configuration and Tension Calculations of Marine Cables Under Complex Currents via Separated Particle Swarm Optimization

Under complex currents, the motion governing equations of marine cables are complex and nonlinear, and the calculations of cable configuration and tension become difficult compared with those under the uniform or simple currents. To obtain the numerical results, the usual Newton?Raphson iteration is often adopted, but its stability depends on the initial guessed solution to the governing equations. To improve the stability of numerical calculation, this paper proposed separated the particle swarm optimization, in which the variables are separated into several groups, and the dimension of search space is reduced to facilitate the particle swarm optimization. Via the separated particle swarm optimization, these governing nonlinear equations can be solved successfully with any initial solution, and the process of numerical calculation is very stable. For the calculations of cable configuration and tension of marine cables under complex currents, the proposed separated swarm particle optimization is more effective than the other particle swarm optimizations.     

An Improvement of Long Baseline System Using Particle Swarm Optimization to Optimize Effective Sound Speed

Abstract

In long baseline (LBL) positioning system, errors due to uncertain sound speed are the major facts to its positioning accuracy. In this study, the problem is solved by setting acoustic signal travels between the target and different hydrophones with different sound speed and using particle swarm optimization algorithm to solve the multi-parameter optimization problem to obtain the sound speeds. Presented simulation results show that the proposed algorithm can effectively improve the positioning accuracy of the LBL system compared to existing algorithms and its computational efficiency is high enough.     

基于粒子群算法的浮筒配置优化问题的研究

针对传统的验证法在解决浮筒配置问题中存在的计算量大、计算时间长、工作重复性强等缺点,提出了一种将浮筒纵向位置配置和浮筒充气量综合考虑、一体优化的方法。文章将各种合理性校验准则作为约束条件,通过逼近沉艇起浮瞬间和浮起后的平衡状态,来求取各对浮筒的纵向位置和充气量,将浮筒配置问题转换为一个多目标、多约束的最优化问题。在此基础上,根据粒子群优化算法的特点,提出了一种双向搜索策略,来求解浮筒配置优化问题。这种方法简单实用,计算时间短,无需人工干预,算法运行一次可获得多组配置方案供决策人员选择。算例表明用这种方法来解决浮筒配置问题是可行的、有效的。     

基于SVM的甲壳动物线粒体基因分析方法

甲壳动物线粒体基因组蕴涵了物种进化历程中重要的遗传信息,如何有效地利用这些保留在基因组中的基因序列和基因顺序信息,是甲壳动物线粒体基因组研究的一个重点方向。为了进一步探讨甲壳动物稳定、可靠的系统发育关系,本文利用支持向量机的分类功能实现了甲壳动物线粒体基因组基因区与基因间区、编码区与非编码区的准确分类和预测,同时为了提高分类学习机的泛化能力,使用了交叉验证方法和粒子群算法优化选取支持向量机相关训练参数。通过MATLAB仿真分析的方法,对10种甲壳动物线粒体基因组序列的基因区和基因间区进行分类,以及对5种甲壳动物进行线粒体基因组序列中编码区和非编码区的分类,获得了较好的分类准确率。仿真结果表明本文方法是可行的和有效的,能够出色地应用于甲壳动物线粒体基因组序列的研究分析。     

Robust optimization for ship conceptual design

The paper presents an approach for the robust optimization of a bulk carrier conceptual design, subject to uncertain operating and environmental conditions. The uncertainties involved in the optimization process are addressed and a general formulation for robust design is given. Specifically, the uncertainties involved in the decision making process are taken into account by means of their probabilistic distributions. The expected values and the standard deviations of the relevant quantities are assessed and included in the optimization objectives, whereas the constraints are evaluated in the worst case. This leads to a robust design able to keep a good performance in the whole probabilistic operating scenario. A particle swarm optimization algorithm is used for the global minimization process, minimizing the expectation and the standard deviation of the unit transportation cost.     

Lossless compression of hydroacoustic image data

Despite rapid progress in improving mass-storage density and digital communication system performance, compression of hydroacoustic image data is still significant in many engineering and research areas since it can overcome data storage and transmission bandwidth limitations. In this paper, we present a novel and effective approach for lossless compression of hydroacoustic image data which consists of two stages. The first stage reduces the information redundancy. We propose several new techniques to remove redundancy between data samples, data blocks, and data frames. The second stage uses a newly developed cascade coding scheme. This simple scheme can achieve an efficiency of 97%. A decomposition algorithm is presented for finding the optimal cascade coding parameters. The algorithm decomposes a multivariable optimization problem into a series of one-variable optimizations. Our two-stage algorithm offers a compression ratio of 2-3 and provides an exact recovery of the original data. Because of its simplicity, the algorithm can be incorporated into a variety of echo sounder systems. The compression algorithms can also be implemented using low-level assembly language to meet the requirements of real-time applications     

动力定位船舶伪逆法与混沌粒子群法相融合的推力分配算法研究

动力定位船舶一般都装备冗余推进器,推力分配求解将直接影响最终定位的效果。针对伪逆法在推力分配中存在问题,提出一种把伪逆法和混沌粒子群法(CPSO)相融合的推力分配算法,该组合算法方便处理推力器各种限制条件,计算简单,能兼顾船舶的能耗及操作性,解决奇点问题。最后以某挖泥船为例,仿真结果表明该推力分配算法具备一定的实用指导价值,能有效提高船舶的定位性能。     

Numerical study on attenuation of bubble pulse through tuning the air-gun array with the particle swarm optimization method

A numerical method for improving the quality of the far-field signal generated by a marine seismic air-gun array is presented, in which particle swarm optimization is used in conjunction with the theory of the oscillating spherical bubble to tune the array. Two sets of objective functions, including the primary-to-bubble ratio and the variance of the normalized amplitude spectrum, are built to screen the array. With this method, attempts are made to improve the far-field signals generated by four typical arrays, including the planar array, spatial array, irregular array and delayed array. It is concluded that the far-field signal can be largely improved just through the simple adjustment of the chamber volume, the firing depth, the horizontal location or the firing time of each individual gun in the array. We believe that this method will be useful in the air-gun array design and helpful to find a desirable array from a population of air-gun array distributions for a brief time.     

基于SVM回归模型的SAR图像有效波高估测

A new method for estimating significant wave height(SWH) from advanced synthetic aperture radar(ASAR) wave mode data based on a support vector machine(SVM) regression model is presented. The model is established based on a nonlinear relationship between σ0, the variance of the normalized SAR image, SAR image spectrum spectral decomposition parameters and ocean wave SWH. The feature parameters of the SAR images are the input parameters of the SVM regression model, and the SWH provided by the European Centre for Medium-range Weather Forecasts(ECMWF) is the output parameter. On the basis of ASAR matching data set, a particle swarm optimization(PSO) algorithm is used to optimize the input kernel parameters of the SVM regression model and to establish the SVM model. The SWH estimation results yielded by this model are compared with the ECMWF reanalysis data and the buoy data. The RMSE values of the SWH are 0.34 and 0.48 m, and the correlation coefficient is 0.94 and 0.81, respectively. The results show that the SVM regression model is an effective method for estimating the SWH from the SAR data. The advantage of this model is that SAR data may serve as an independent data source for retrieving the SWH, which can avoid the complicated solution process associated with wave spectra.     

海底双相随机介质声反射的SVM神经网络分类识别研究

考虑海底沉积介质为双相介质,为了更好地模拟实际海底底质的不均匀性,将随机介质理论引入双相介质理论。首先,通过基于随机-双相介质理论的高阶有限差分数值技术模拟计算海底底质分别为泥质砂、泥、泥质砾时的地震反射波信号。然后利用小波变换分别求取不同底质的一次反射波的包络作为其特征向量,最后利用基于粒子群智能算法优化的支持向量机神经网络对这些反射波信号进行分类识别。为了进一步考察所用方法的抗噪能力,对正演得到的海底底质反射波信号分别加入10%、30%、50%的高斯白噪音之后再进行分类,支持向量机仍然取得了较好的分类预测效果。基于上述正演模拟及分类识别方法的论证,提出了一套行之有效的微机软件模拟海底沉积物分类识别的一般化流程,这将有利于开展海底沉积物反射特征的进一步研究。