光线寻优算法的寻优机理分析

光线寻优算法是一种通过模拟光的传播路径进行寻优的最优化方法.该算法用网格划分可行域,将具有不同折射率的介质填充到各网格中,并将光在此变折射率介质中的传播路径设想成算法的寻优路径.光线寻优算法仅用到目标问题的函数值,结构简单容易实现.根据费马原理,利用变分法进行推导,得出了光具有偏向折射率增大方向,偏离折射率减小方向的自动寻优性质.通过求解9个标准测试函数,并将算法与模拟退火算法,标准粒子群算法进行比较,验证了所得结论以及算法的可行性,有效性,潜在的优越性.     

含分数逆幂的矩阵方程对称自反解的双迭代算法

<正>1引言含分数逆幂的矩阵方程在控制理论、梯形网络和动态规划等领域中有重要的应用~([1-3]).考虑有代表性的一类含分数逆幂的双变量矩阵方程A_1X_1B_1+A_2X_2B_2+E_1X_1~(-1/2)F_1+E_2X_2~(-2/3)F_2=G,(1)其中A_i,B_i,E_i,F_i,X_i,G∈R~(n×n)(i=1,2).替换方程(1)中的X_1~(1/2)为X_1,X_2~(1/3)为X_2可得A_1X_1~2B_1+A_2X_2~3B_2+E_1X_1~(-1)F_1+E_2X_2~(-2)F_2=G.(2)近年来,人们对这种类型的非线性矩阵方程进行了许多研究,并建立了一些有效的算法.例如,Li J等~([1])研究了方程X-A~HX~(-p)A=Q唯一正定解的存在性问题,并给出了方     

Frame-Based Ray Search Algorithms in Unconstrained Optimization

This paper describes a class of frame-based direct search methods for unconstrained optimization without derivatives. A template for convergent direct search methods is developed, some requiring only the relative ordering of function values. At each iteration, the template considers a number of search steps which form a positive basis and conducts a ray search along a step giving adequate decrease. Various ray search strategies are possible, including discrete equivalents of the Goldstein–Armijo and one-sided Wolfe–Powell ray searches. Convergence is shown under mild conditions which allow successive frames to be rotated, translated, and scaled relative to one another.     

一种改进的禁忌搜索算法及其在连续全局优化中的应用

禁忌搜索算法是一种元启发式的全局优化算法,是局部搜索算法的一种推广,已被成功地应用于许多组合优化问题中。本文针对有界闭区域上的连续函数全局优化问题,提出了一种改进的禁忌搜索算法,并进行了理论分析和数值实验。数值实验表明,对于连续函数全局优化问题的求解该算法是可行有效的,并且结构简单,迭代次数较少,是一种较好的全局启发式优化算法。     

基于模矢搜索和遗传算法的混合约束优化算法

近年,免梯度方法又开始引起大家的注意，由于不需要计算函数的梯度．特别适合用来求解那些无法得到梯度信息或需要花很大计算量才能得到梯度信息的问题．本文构造了一个基于模矢搜索和遗传算法的混合优化算法．在模矢搜索方法的搜索步，用一个类似于遗传算法的方法产生一个有限点集．算法是全局收敛的.     

基于引力搜索和粒子群混合优化算法的证券投资组合问题研究

本文研究考虑交易成本的投资组合模型,分别以风险价值(VAR)和夏普比率(SR)作为投资组合的风险评价指标和效益评价指标。为有效求解此模型,本文在引力搜索和粒子群算法的基础上提出了一种混合优化算法(IN-GSA-PSO),将粒子群算法的群体最佳位置和个体最佳位置与引力搜索算法的加速度算子有机结合,使混合优化算法充分发挥单一算法的开采能力和探索能力。通过对算法相关参数的合理设置,算法能够达到全局搜索和局部搜索的平衡,快速收敛到模型的最优解。本文选取上证50股2014年下半年126个交易日的数据,运用Matlab软件进行仿真实验,实验结果显示,考虑交易成本的投资组合模型可使投资者得到更高的收益率。研究同时表明,基于PSO和GSA的混合算法在求解投资组合模型时比单一算法具有更好的性能,能够得到满意的优化结果。     

BFGS算法对非凸函数优化问题的收敛性

ＢＦＧＳ算法是无约束最优化中最著名的数值算法之一,对非凸函数ＢＦＧＳ算法是否具有整体收敛性,这是一个ｏｐｅｎ问题,本文考虑Ｗｏｌｆｏ线搜索下目标函数非凸的ＢＦＧＳ算法,我们给出一个使该算法收敛的充分条件。     

Combining a Local Search and Grover’s Algorithm in Black-Box Global Optimization

Grover’s quantum algorithm promises a quadratic acceleration for any problem formulable as a search. For unstructured search problems, its implementation and performance are well understood. The curse of dimensionality and the intractability of the general global optimization problem require any identifiable structure or regularity to be incorporated into a solution method. This paper addresses the application of Grover’s algorithm when a local search technique is available, thereby combining the quadratic acceleration with the acceleration seen in the multistart method. The author thanks Dr. Bill Baritompa for helpful discussions.     

A Filter-Based Pattern Search Method for Unconstrained Optimization

We discuss a filter-based pattern search method for unconstrained optimization in this paper. For the purpose to broaden the search range we use both filter technique and frames, which are fragments of grids, to provide a new criterion of iterate acceptance. The convergence can be ensured under some conditions. The numerical result shows that this method is practical and efficient.     

Dynamic Data Structures for a Direct Search Algorithm

The DIRECT (DIviding RECTangles) algorithm of Jones, Perttunen, and Stuckman (Journal of Optimization Theory and Applications, vol. 79, no. 1, pp. 157–181, 1993), a variant of Lipschitzian methods for bound constrained global optimization, has proved effective even in higher dimensions. However, the performance of a DIRECT implementation in real applications depends on the characteristics of the objective function, the problem dimension, and the desired solution accuracy. Implementations with static data structures often fail in practice, since it is difficult to predict memory resource requirements in advance. This is especially critical in multidisciplinary engineering design applications, where the DIRECT optimization is just one small component of a much larger computation, and any component failure aborts the entire design process. To make the DIRECT global optimization algorithm efficient and robust on large-scale, multidisciplinary engineering problems, a set of dynamic data structures is proposed here to balance the memory requirements with execution time, while simultaneously adapting to arbitrary problem size. The focus of this paper is on design issues of the dynamic data structures, and related memory management strategies. Numerical computing techniques and modifications of Jones' original DIRECT algorithm in terms of stopping rules and box selection rules are also explored. Performance studies are done for synthetic test problems with multiple local optima. Results for application to a site-specific system simulator for wireless communications systems (S ⁴ W) are also presented to demonstrate the effectiveness of the proposed dynamic data structures for an implementation of DIRECT.     

灰色非线性约束规划的改进引力搜索算法求解

灰色非线性约束规划是灰色系统中一个重要的优化问题.为求解灰色非线性约束规划,给出了一种改进引力搜索算法的求解方法.实验结果表明改进引力搜索算法对求解灰色非线性约束规划可行有效.