非精确搜索一维全局最优化方法

研究一维全局最优化问题的确定性求解方法。运用逐次建立目标函数的线性下界函数,将不含全局最优解的子区域删除,并基于非精确搜索结合下降算法而得出非精确搜索一维全局最优化方法,使计算量减少且使迭代收敛加快。迭代结束时该算法得到一维全局最优化问题的ε－全局最优解。该方法具有有限收敛性且不需精确的局部优化过程。文中的数值实例表明该算法的有效性。     

一种用于认知无线电资源分配的并行免疫遗传算法

提出了用于认知无线电自适应调制和资源分配的并行免疫遗传算法,并对该算法、简单遗传算法和静态调制分配算法进行了仿真.仿真结果显示,该算法具有很强的全局搜索能力和较快的收敛速度,在误码率和功率受限条件下,该算法比简单遗传算法和静态调制方式的性能更好,同时明显降低了计算复杂度.     

海上油田油轮外输系统优化分析

对海上油田油轮外输系统进行了分析 ,建立了系统优化的数学模型。选用模矢搜索法、单纯形算法和模拟退火算法三种优化方法进行了计算 ,得出在对某些非线性问题进行优化时 ,利用模拟退火算法和直接最优化方法 (如单纯形法 )相结合 ,既能保证得到全局最优解 ,又能减少计算时间。通过实例计算 ,得出油轮外输系统适用于离岸距离较远、产量较小的油田 ,其外输费用随离岸距离的增加变化非常小 ,但随油田产量的增加而增加较大     

基于头脑风暴优化算法与BP神经网络的海水水质评价模型研究

针对基于传统BP神经网络的海水水质评价模型存在易陷入局部极小等问题,提出了一种新的利用头脑风暴优化算法(BSO)优化BP神经网络的海水水质评价模型(BSO-BP)。该模型引入具有全局寻优特点的头脑风暴优化算法,用于模拟人类提出创造性思维解决问题的过程,具有强大的全局搜索和局部搜索的能力,同时利用BP神经网络所具有良好的非线性映射能力、学习适应能力和容错性,最大程度上考虑到海洋水质评价因素的非线性和非平稳的关系,得到BP神经网络的各层权值、阈值的最优解,使得海水水质评价结果准确合理。并以胶州湾海域的12个监测站位的监测数据作为评价样本进行水质评价,实验结果表明该评价模型能够克服局部极小问题,评价结果准确性较高,并具有一定的实用性。     

基于PSO-SVM的MODIS数据海上云检测

为利用中分辨率成像光谱仪(MODIS)数据实现海上云的检测,提出一种结合粒子群优化算法(PSO)与支持向量机(SVM)的检测方法。根据海洋下垫面的特点,选取了合适的特征量,利用具有较强全局搜索能力的粒子群优化算法对支持向量机中的参数进行优化调整,改善了支持向量机的分类性能,试验结果表明,该方法能获得较高的分类精度,可以较好的实现海上云的检测。     

一类全局最优化问题的动态隧道方法

研究了连续变量函数的全局最优化问题 ,给出了动态隧道方法。该动态隧道方法由局部搜索和动态隧道 2个阶段构成。在局部搜索阶段用了动态系统方法。对全局最优化问题的实例进行了数值实验 ,数值结果表明了该方法的稳健性和有效性。     

基于径向基神经网络的水下自主航行器寻源算法研究

针对水下航行器在二维信号场中的场源搜索问题,提出了一种基于径向基函数神经网络（radial basis function neural network,简称RBFNN）的在线自主寻源算法。在神经网络中引入全局正则化参数以保证泛化性和稳定性,通过最小化广义交叉验证误差（generalized cross-validation,简称GCV）进行正则化参数的迭代优化,并利用增量式奇异值分解（incremental SVD）对迭代过程进行加速,此外通过基于样本新颖性的资源分配网络算法（resource-allocating network,简称RAN）进行径向基函数的分配,在此基础上使用动量梯度算法进行航行器运动方向的规划。最后,以热泉区硫化氢浓度分布场中的搜索作业为背景,使用该算法与其他研究中的算法进行单峰值信号场的场源搜索模拟计算对比,结果显示该算法对于信号场梯度的估计更加准确,且搜索过程的路径更短。此外在多峰值信号场的寻源模拟中该算法能够以较高的成功率通过局部最大值区域。证明该算法具有良好的拟合、预测性能以及稳定性,并且能在一定程度上避免陷入局部最优解。     

基于RRT和DWA的欠驱动UUV路径规划

针对无人潜航器(UUV)在未知水下复杂环境的路径规划问题,设计了随机树以及动态窗口的融合算法。该算法基于快速扩展随机树(RRT)以及动态窗口(DWA)两层规划设计,第一层利用随机树算法快速规划出全局路径,在此基础上第二层加载全局路径,针对 UUV 模型的欠驱动和非线性,利用动态窗口算法完成局部路径规划,保证约束条件下 UUV 路径的安全性。通过融合参数 μ 修正内外框架的融合度,有效地弥补了全局路径算法的无法躲避动态障碍物的缺点以及局部路径算法全局能力低下的问题。最后,通过对比仿真验证了融合算法相比于随机树全局算法和动态窗口局部算法的优越性。     

一种新型混沌遗传优化算法的研究

在对遗传算法交叉、变异后结果的处理方法进行改进并在将进化过程分段的基础上,提出1种新的混沌遗传算法。该算法利用混沌运动的遍历性择优产生初始群体,对每一轮遗传操作所得到的部分当前最优个体进行变尺度混沌搜索。仿真结果表明,该算法具有较高的优化效率,并能求得全局最优解。     

一种求解旅行商问题的改进蚁群算法

提出一种求解旅行商问题的改进蚁群算法。该算法在信息素更新过程中,利用信息素局部更新和全局动态更新结合的方法,使得当前最优路径上的信息素值能够动态地调配,避免算法陷入停滞状态;在局部搜索过程中,仅对部分走出更优路径的售货员使用2-opt方法,加快了最优解的收敛速度。仿真实验验证了算法的有效性,与其它算法相比,本文算法在解的质量和收敛速度上都显示出了良好的性能。     

混合最优化方法在潮汕坳陷重力资料反演中的应用

选用目前非线性方法中的2个研究热点——BP神经网络和模拟退火算法,用优化后的BP神经网络为主框架,结合位场反演的特点,在反演过程中引入模拟退火算法,这样既利用了BP神经网络具有指导学习的功能来提高局部搜索性能,也利用了模拟退火算法的概率突跳性来实现最终的全局收敛性,从而提高了反演的速度和精度。通过模型试验验证了其有效性之后,将该方法应用于珠江口盆地潮汕坳陷的重力资料反演,结果较好地反映了剖面的地质情况。     

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.     

Bayesian geoacoustic inversion for the Inversion Techniques 2001 Workshop

This paper applies a Bayesian formulation to range-dependent geoacoustic inverse problems. Two inversion methods, a hybrid optimization algorithm and a Bayesian sampling algorithm, are applied to some of the 2001 Inversion Techniques Workshop benchmark data. The hybrid inversion combines the local (gradient-based) method of downhill simplex with the global search method of simulated annealing in an adaptive algorithm. The Bayesian inversion algorithm uses a Gibbs sampler to estimate properties of the posterior probability density, such as mean and maximum a posteriori parameter estimates, marginal probability distributions, highest-probability density intervals, and the model covariance matrix. The methods are applied to noise-free and noisy benchmark data from shallow ocean environments with range-dependent geophysical and geometric properties. An under-parameterized approach is applied to determine the optimal model parameterization consistent with the resolving power of the acoustic data. The Bayesian inversion method provides a complete solution including quantitative uncertainty estimates and correlations, while the hybrid inversion method provides parameter estimates in a fraction of the computation time.     

Optimization of variational methods

Lord Rayleigh proposed his now classical method (based on a one-term deflection function) in 1870. On the other hand, he suggested in 1894 the possibility of optimizing the eigenvalues (natural frequencies or buckling loads) by minimizing them with respect to an exponential parameter contained in the coordinate function. This optimization approach has become increasingly popular in the last decade and it has been extended to other variational methods and bounding techniques. The present paper reviews recent developments in this area which consists, essentially, in a nonlinear optimization process of the coordinate functions in a global sense. The technique is applicable in a large variety of applied mechanics problems of considerable interest to ocean engineers dealing with the analysis of mechanical systems.     

A Double-Stage Surrogate-Based Shape Optimization Strategy for Blended-Wing-Body Underwater Gliders

In this paper, a Double-stage Surrogate-based Shape Optimization (DSSO) strategy for Blended-Wing-Body Underwater Gliders (BWBUGs) is proposed to reduce the computational cost. In this strategy, a double-stage surrogate model is developed to replace the high-dimensional objective in shape optimization. Specifically, several First-stage Surrogate Models (FSMs) are built for the sectional airfoils, and the second-stage surrogate model is constructed with respect to the outputs of FSMs. Besides, a Multi-start Space Reduction surrogate-based global optimization method is applied to search for the optimum. In order to validate the efficiency of the proposed method,DSSO is first compared with an ordinary One-stage Surrogate-based Optimization strategy by using the same optimization method. Then, the other three popular surrogate-based optimization methods and three heuristic algorithms are utilized to make comparisons. Results indicate that the lift-to-drag ratio of the BWBUG is improved by 9.35%with DSSO, which outperforms the comparison methods. Besides, DSSO reduces more than 50%of the time that other methods used when obtaining the same level of results. Furthermore, some considerations of the proposed strategy are further discussed and some characteristics of DSSO are identified.     

单纯形投影算子理论及在资料分析中的应用 Ⅰ.在方程模型资料预测中的应用

本文根据混沌系统奇怪吸引子在相空间中的伸缩和折叠的几何特征,建立了单纯形投影算子（simplexprojectionoperator）预测方法．该方法主要侧重考虑了被预测点的局部非线性特征及Lyapunov特征指数,避免了传统预测函数和方程的模拟问题,减少了模拟参量．对Lorenz方程和Logistic映射资料进行预测分析的结果表明,此方法简单易行,缩短了计算时间,在预测时问尺度及精度上具有较强的预测能力．     

Design of sparse linear arrays by Monte Carlo importance sampling

The formation of acoustic images in real-time requires an enormous computational burden. To reduce this demand the use of sparse arrays for beamforming is mandated. The design of these arrays for adequate mainlobe width and low sidelobe level is a difficult nonlinear optimization problem. A new approach to the joint optimization of sensor placement and shading weights is discussed. Based on the concept of importance sampling an optimization method is presented and some examples given to illustrate its effectiveness.