随机搜索

本文详尽地讲述了随机搜索法的基本概念及其算法，应用这个算法，完成了一套电机CAD优化设计系统；

In this paper , both the theory and algorithm of random search approach are described in detail .

微粒群优化算法（ParticleSwarmoptimization，PSO）是通过模拟鸟群觅食行为而发展起来的一种基于群体协作的随机搜索算法。

Particle Swarm Optimization is a random search algorithm based group collaboration by simulating the behavior of birds foraging .

并将选择性参数BIAS引入快速随机搜索树算法，加快了趋近速度。

During producing local target , bias parameter is introduced to speed the search .

PSO算法本质上属于迭代的随机搜索算法，其主要优点在于收敛速度快、能以较大的概率找到优化问题的全局最优解等。

PSO being iterations random algorithm does better in fast constringency and finding the optimization solution with biggish probability .

试验结果表明，初始种群克服了GA自适应随机搜索局部最优和过早收敛问题。

The experiment result shows that problems of local optimization and premature constringency were resolved with the original group in GA adaptive random search .

利用遗传算法（GA）建立了相贯线扫查冗余机器人能量最小优化的综合规划模型，给出了其随机搜索策略。

A comprehensive optimization model of minimal energy trajectory for redundant manipulator applied in intersection curve scanning is developed based on genetic algorithm ( GA ) .

作为近期发展起来的一种随机搜索方法，tabu搜索法有较强的解决复杂组合问题的能力。

As a newly developed random search method , TABU method has strong ability to solve complex combined problem .

模拟退火算法是基于MonteCarlo迭化求解法的一种启发式随机搜索法，该算法能通过模拟退火过程快速找到全局的最优解。

The simulated annealing algorithm is an enlightening random search method based on Monte Carlo recursion , and it can find global optimal solution by simulating annealing process .

一类自适应随机搜索算法及DEDS优化

An Adaptive Random Search Algorithm and DEDS Optimization

第三章对VAR模型的计算方法进行了改进，重点介绍了随机搜索化的德尔塔正态方法和蒙特卡罗模拟法的改进；

Chapter 3 improves the calculating method of VAR model . It introduces importantly the random searching delta-normal method and the improvement the Monte Carlo simulation method .

近些年来，遗传算法（GA）作为一种全新的随机搜索与优化算法迅速地发展起来，并且在很多领域被广泛的应用。

In recent years , genetic algorithm ( GA ) which has developed rapidly is a new random search and optimization algorithm . It is widely used in many fields .

本文提出了一种混沌搜索与随机搜索相结合的混沌优化方法（COA）。

In this paper , an algorithm ( COA ) combined with chaos search and stochastic search is presented .

而其它算法，如DPLL算法、局部随机搜索算法等也各有其优缺点。

Other algorithms such as DPLL algorithm and Local Search algorithm have their own advantages and defections .

由Holland首先提出的遗传算法是模拟生物在自然环境中的遗传和进化过程而形成的一种随机搜索的全局优化算法，该方法可以全局搜索得到最优参数。

Genetic algorithm first put forwarded by Holland is a global optimization algorithm of random search that simulate the process of inheritance and evolution formed in natural condition .

文章以演化计算为工具，以BAN逻辑为基本的推理准则，在第一阶段随机搜索候选协议，然后在第二阶段通过冗余协议约简方案得出优化的协议。

In the first stage , it uses evolutionary computing random search candidate of security protocols with BAN logic ; in the second stage , optimal protocol is gained from redundancy protocols with reduction scheme .

这种方法是把随机搜索法、动态规划法和B样条函数法有机地联系在一起的一种方法。本文介绍了这种方法的数学理论和如何解决在局部区域内的线路自动优化问题。

There are many direct search approaches of optimization available in electric machine optimum design . The paper gives an explanation of the mathematical theory of this method and on how to solve the problem of automatic optimization of railway line within parts of an area .

本文分析和介绍了一种新的通用启发式优化方法&模拟退火算法，它是基于Monte-Carlo迭代求精法的一种随机搜索算法。

This paper introduces and analyses a kind of new heuristic optimization method & simulated annealing algorithm . It is a general random search algorithm based on Monte-Carlo iterative improvement method .

FNN是一种重要的神经网络结构，混合遗传算法是一种正在兴起的启发式随机搜索技术，两者在智能优化中得到了广泛的应用。

FNN is an important neural network structure . Hybrid genetic algorithms is a heuristic random search technology springing up . Both are used widely in intelligence optimization .

它是在控制随机搜索算法（简称CRS法）基础上做了如下改进：将CRS法任意选择搜索方向的方式改成特定选择方式；

The CRS method has been improved as follows . The special selection way is substituted for the random selection way of searching directions in the CRS method ;

在这种情形下，在GLM的框架下提出了基于随机搜索技术的贝叶斯变量与模型选择方法。

In such case , an approach of Bayesian variable and model selection is proposed by using the stochastic search technique based on GLM .

经过分析与研究，遗传算法是一种全局并行随机搜索优化算法，具有很强的全局搜索能力，而BP算法的局部搜索能力较强。

The genetic algorithm ( GA ) is a kind of optimization algorithm with which global , parallel and random searching can be achieved , and its global searching performance is very good , while the BP algorithm does quite well in local searching .

而基于进化计算的优化方法，采用自适应随机搜索全局优化技术，具有广泛的适应性和包容性；基于多Agent的建模与仿真方法，则能够胜任复杂智能系统的综合效能仿真分析任务。

The Evolutionary Computation ( EC ) based optimization methods , adopting self-adaptive stochastic searching technology for global optimization , possess comprehensive adaptability and compatibility , moreover , the Multi-Agent Based Modeling and Simulation ( MABMS ) methods are competent for analyzing the integrated effectiveness of complex intelligent systems .

差分进化算法（DE）是由RainerStorn和KennethPrice为求解切比雪夫多项式而于1996年共同提出的一种采用浮点矢量编码在连续空间中进行随机搜索的优化算法。

Differential Evolution ( DE ) by Rainer Storn and Kenneth Price for solving Chebyshev Polynomials in 1996 jointly proposed a vector-encoded using floating-point margin continued space random search optimization algorithm .

采用快速傅立叶变换（FFT）频谱分析技术、比值校正方法和随机搜索算法，完成了检波器输出信号的计算机分析程序的设计、调试和实际应用研究。

The design and debugging and the research on practical application of the computer analysis program for the Geophone output signals were accomplished with the FFT spectrum analysis technology , the ratio spectrum correction and the random search algorithm .

遗传算法是一种借鉴于生物界自然选择和进化机制发展起来的高度并行、自适应的随机搜索算法，是一种非常有效的解决NP完全的组合问题的方法。

Genetic Algorithm is a high-effective Paralleling Processing , randomly searching and self-applicable algorithm based on the development of the nature evolution and option , it is a very effective algorithm to resolve NP-Completed Combination Optimization Problem .

采用马尔可夫链方法证明了HGA是以概率1收敛的全局随机搜索算法。

A further result on the Markov chain model of HGA shows that it is a global stochastic searching method and can converge with probability one .

BCI是一种线性迭代方法，FSA是一种具有全局寻优特点的随机搜索方法，正则化方法是通过对目标函数施加先验约束，来解决反演的病态问题&多解性和不稳定性。

BCI is a linear iterative method , FSA is a random searching method with global optimization , and regularization can solve ill-posed inverse problem by prior term , including multi-result and instability .

Mendel的遗传学说的基础上产生和发展起来的一类随机搜索优化方法，常用于求解许多传统方法难以解决的高维的、多模态的、非线性的和多目标的复杂问题。

Mendel 's genetic theory . Its application predominance lives in higher-dimensional , multimodal , non-linear and multiobjective complicated problems , which are difficult for traditional searching methods .

采用常规的优化方法难以解决一般DEDS的优化问题，本文提出了一种新的基于仿真的自适应随机搜索技术，用于解决这类问题。

General DEDS optimization problems are intractable via conventional approaches . In this paper , we present a new adaptive random search algorithm based on simulation to solve the problems .

针对蚁群在解空间作随机搜索的局限性，引入确定性搜索方法变尺度法，提出了改进蚁群算法VACA。

Facing the limitations of the random search in the solution space , we use the certainty search method , Variable Metric Algorithm , the improved algorithm VACA has been gotten .