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Second, according to model characteristic, such as non-linear, non-convex, multiple-peaked and so on, the paper discusses limitation of traditional solving algorithm, such as branch and bound method, GBD method and OA method, puts forward penalty function-makeup integer algorithm and genetic algorithm, analyses procedures of genetic algorithm, such as selection, crossover and mutation, brings forward modifications for specific realized problems of genetic algorithm, such as selection of initial points, operator design, adoptive value adjustment, constraint treatment and so on, and works out correlated solving program.

其次针对该模型非线性、非凸、多峰等特点,讨论了分支定界法、GBD法和OA法等传统求解算法的局限性,提出了罚函数一凑整算法和遗传算法的求解思路,对遗传算法的选择、交叉、变异等过程进行分析,对遗传算法初始点选择、算子设计、适应值调整和约束处理等具体实现问题提出了改进措施,并编制了相关的求解程序。

The advanced research work of this paper is: we realize two kinds of DOA estimation arithmetic on the MATLAB software environment. One arithmetic is traditional arithmetic, which include Time-delay method and Capon method. The other one is MUSIC arithmetic. Then we qualitatively compare the performance of these arithmetic with each other.

论文的主要研究工作是:在MATLAB仿真软件平台上实现了DOA估计的传统算法(时延—相加算法和Capon最小方差算法)、多重信号分类算法MUSIC的仿真实现;并定性研究比较了三种算法的性能差别。

This paper presents a method of complete coverage path planning based on genetic algorithms,which combine the advantages of cellular decomposition and template algorithm.

提出了一种基于遗传算法的全区域覆盖算法,结合分块算法和模板算法的优点。

The thesis also draw several conclusions as follows:(1) Orthogonal experiment design method can be viewed as a special case of genetic algorithm, i. e. a genetic algorithm with a fixed initial population, an oriented-mutation operator and one evolution epoch.(2) In terms of running steps, genetic algorithm is more complex than orthogonal experiment design method.

论文首次对正交试验设计法和遗传算法这两种独立发展起来的方法进行比较研究,提出(1)正交试验设计法是遗传算法的一种特例,即是一种初始种群固定的、只使用定向变异算子的、只进化一代的遗传算法;(2)遗传算法产生的优化解优于正交试验设计法产生的优化解,且遗传算法处理交互作用项的效率高于正交试验设计法;(3)遗传算法的步骤比正交试验设计法复杂,所需的试验次数也要多。

In this paper, self-orthogonali zation was used to speed up the convergence rate of transform domain adaptive le ast-mean-square algorithm, and the analytical expression of system bit e rror rate was deduced.

但由于必须通过迭代收敛达到稳态解,自适应滤波算法不能锁定快变干扰。文中利用正交加速算法提高最小均方误差算法的收敛速度,推导出了使用变换域自正交加速算法时系统误码率的解析表达式。

According to this theory, hardware was made; the experiments showed different flow case brought different cross correlation function, when the distance of two sensors within 0.5~1. 5 diameter of the pipe, a sharp peak can be detected in the curve of cross correlation function, the frozen model is satisfied.

开展了互相关函数算法的研究,重点研究了极化相关算法,对该算法进行仿真研究;仿真结果与实验处理结果对比,得出只有将流动噪声信号量化为+1、-1时,极化相关算法可以得到准确的峰值位置。

On the base of the developments and the characteristics, a new commingled algorithm that is Probability Clonal Selection Particle Swarm Algorithm is put forward, and the application process of the commingled algorithm is presented after the commingled theory is analyzed.

接着,研究了算法的融合机理,并对各自的算法进行了改进,在改进的基础上,依据三种智能优化算法的特点,提出了一种新的融合算法,即概率克隆选择微粒群算法,并对其进行了理论分析,给出了融合算法的应用流程。

the effect of inertia weight on particle swarm optimization is studied,on basis of which adopts four kinds of strategies of inertia weight to regulate the speed of a new quantum delta potential well based particle swarm optimization.a faster and more stabile algorithm,found by comparing the performances of four equations regulated the inertia weight,solves 0/1 knapsack problem.the result of experiment shows that the modified algorithm improves the precision of optimal solution and has a faster speed and a higher efficiency in convergence.in a word,choosing a parameter of inertia weight suitably can improve the performance of new qdpso.

摘 要:在研究惯性权重对基本pso算法影响的基础上,根据惯性权重对粒子群算法影响的特点,采用4种惯性权重策略对一种新的具有量子行为的粒子群算法的速度进行调节,比较每种算法的性能,从中找到一种新的性能更好的改进算法,将其用于求解0-1背包问题。实验结果表明较好地选择惯性权重参数对算法的性能有很大提高,该改进算法在求解0-1背包问题中具有高效性,提高了最优解的精度,同时具有较快的收敛速度。

We first review the development of decomposition algorithm, discuss and analyze the basic D-W and Benders decomposition algorithm and nested algorithm based on them., and compare them with the normal simplex method.

本文首先对分解算法的发展过程及其主要算法进行了较为全面的回顾,并对基本的 D-W 分解算法,Benders 分解算法和在此基础上的嵌套分解算法进行了较为详尽的讨论和分析,以及与普通单纯形算法的比较等等。

The genetic algorithm proposed in this paper is called pseudo parallel genetic algorithm, which is designed by author. This kind of search way improves the basic genetic algorithm.

本文用到的遗传算法是由作者自行设计的伪并行遗传算法,该算法是对基本遗传算法的改进,在收敛速度和全局搜索能力方面远远优于基本遗传算法。

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