一种改进Best-First算法的主题爬虫搜索算法

针对传统的基于页面内容相似度的Best-First算法只考虑词频,忽略了特征关键词的位置信息这一不足,以及BestFirst算法存在很大贪婪性,难以在全局范围内得到最优解的局限性,提出以Best-First算法为基础,利用网页HTML标签的修饰功能改进相似度的计算方法,不仅注重搜索与主题相似度很高的链接,同时还考虑某些蕴涵很大远期价值的链接。实验结果表明,改进算法相比传统算法"准确率"和"召回率"都有所提高,它是有效的,并且能在一定程度上获得全局范围的最优解。     

一种改进的基于遗传算法的聚类分析方法

C－均值聚类收敛速度快，但是它容易陷入局部最优，且对初始解很敏感。遗传算法是一种全局搜索方法，但是它收敛速度慢。为了在搜索能力和收敛速度两方面都取得较好的效果，本文提出了一种改进的基于遗传算法的聚类分析方法。实验结果表明：本文提出的算法在聚类分析中搜索到全局最优解（或近似全局最优解）的能力要优于经典遗传算法及C－均值聚类算法；且通过对变异概率的巧妙设置，提高了算法的自适应能力。     

一种混合遗传LM算法求解非线性最小二乘问题

文章提出了一种混合遗传LM算法,并将其用于求解非线性最小二乘问题,该方法利用遗传算法摆脱局部最小值,在全局极小值的领域内估计解,找到全局最小值的近似后,利用遗传算法找到的全局最优解作为LM算法的起点。像遗传算法这样的随机搜索算法可以很容易地在全局最小值附近计算出一个解,但由于搜索的随机性,需要很长时间才能收敛到精确的最小值。因此,该算法协同结合了确定性局部搜索和启发式随机全局搜索的优点,高效地计算出精确的解。利用了一个圆柱拟合的实验来验证该算法,结果表明该算法在拟合问题上有良好的性能。     

禁忌粒子群算法在几何约束求解中的应用

约束问题可以转化为优化问题,针对粒子群优化算法在算法的后期易陷入局部最优的缺点,提出TPSO(禁忌粒子群优化算法),在算法的前期采用粒子群算法快速产生全局最优解信息素的初始分布,后期引入禁忌搜索算法,记录已经达到的局部最优解,在下一次搜索中,不再或者有选择地搜索这些点,从而跳出局部最优点,并且在搜索过程中允许接受劣解,充分利用禁忌搜索的记忆能力及较强的爬山能力,大大提高了获得全局最优解的概率.该算法综合了粒子群优化算法的快速性,随机性和全局收敛性以及禁忌搜索局部寻优的能力.在确保全局收敛性的基础上,能够快速搜索到高质量的优化解.该方法用于几何约束求解的性能明显高于标准粒子群算法,算法具有良好的优化性能和时间性能.     

基于自适应信息素调整的连续空间优化蚁群算法

对于基本蚁群算法(ACA)不适用求解连续空间问题,并且极易陷入局部最优的缺点,提出了一种基于自适应的蚁群算法。路径搜索策略采用基于目标函数值搜索筛选局部最优解的策略,确保能够迅速找到可行解。信息素更新策略采用自适应的启发式信息素分配策略,使算法能够快速收敛到全局最优解。对2个求函数极值问题进行优化并与其他算法进行比较,结果表明该算法能很好的应用于对连续对象的优化,同时具有较高的寻优精度高,搜索速率快,良好的全局优化性能。     

基于多元优化算法的路径规划

本文提出了一种基于多元优化算法和贝塞尔曲线的启发式智能路径规划方法.该方法通过用贝塞尔曲线描述路径的方法把路径规划问题转化成最优化问题.然后,使用多元优化算法来寻找最优的贝塞尔曲线控制点以获得最优路径.多元优化算法智能搜素个体协同合作交替的对解空间进行全局、局部迭代搜索以找到最优解.多元优化算法的搜索个体（元）按照分工不同可以分为全局元和局部元.在一次迭代中,全局元首先探索整个解空间以找出更优的潜在解区域.然后,局部元在各个潜在解区域进行局部开采以改善解质量.可见,搜索元具有分工不同的多元化特点,多元优化算法也就因此而得名.分工不同的搜索元之间高效的沟通和合作保证了多元优化算法的良好性能.为了评估多元优化算法的性能,我们基于标准测试地图比较了多元优化算法与其它三种经典启发式智能路径规划算法.结果表明,我们提出的方法在最优性,稳定性和有效性上方面优于其它方法.     

一种新型的变领域搜索算法

一般变邻域搜索算法在连续优化问题的可行解空间上难以找到局部最优解。提出了一种结合SQP算法的变邻域搜索算法,该算法将SQP算法引入到变邻域搜索算法的局部搜索过程中,以SQP算法寻找局部最优解,以变邻域搜索算法跳出局部最优解的低谷,进而寻找到全局最优解。另外还对变邻域搜索算法的初始解和扰动过程进行了改进。数值实验表明,该算法具有良好的收敛性和搜索精度,求解效果优于文献算法。     

一种非线性回归模型参数估计的混合基因算法

提出了一种新的结合可变多面体法和基因算法的混合基因算法(HGA),它通过对问题的解空间交替进行全局和局部搜索,达到快速收敛至全局最优解,较好地解决了基因算法在达到全局最优解前收敛慢的问题。非线性回归模型参数估计的实验表明该算法具有较好的通用性和有效性。     

非线性回归模型参数估计的区间分析方法

现有很多方法都属局部搜索方法,不能保证得到问题的全部全局最优解,而基于区间分析的区间全局优化算法则能在给定精度范围内求出问题的全部全局最优解,并能给出满足要求的包含最优解的任意小区间。基于此,给出了非线性回归模型参数估计的区间全局优化算法,论述了算法求解问题的基本思想、解算步骤、基本算法和加速工具等,并将其应用于非线性回归模型参数估计中,仿真实验结果验证了所给算法的可行性和有效性．     

模拟退火法用于气体红外光谱数据的识别分类

本文介绍了一种新的分类方法,它克服了传统的线性局部搜索的缺点,引入了跳跃式搜索,使得算法能够跳出局部极优解的陷阱,从而找到全局最优解。文中详细地介绍了其原理,并利用模拟退火的跳跃式搜索方法进行算法训练。通过实例比较,说明本方法应用于大气遥感红外光谱数据的分类和识别时,是行之有效的。     

Penalty function approach in heuristic algorithms for constrained redundancy reliability optimization

To solve the problem of constrained redundancy reliability optimization, several heuristic algorithms have been developed in the literature. Most of these algorithms search for the solutions remaining within the feasible boundary e.g. [15], [20]. Perhaps the most interesting & efficient heuristic algorithm in terms of solution quality is that given by KYA, in which the search is made not only in the feasible region but also into the bounded infeasible region by making an excursion, which returns to the feasible region with a possibly improved solution. In this paper, a heuristic algorithm based on the penalty function approach is proposed to solve the constrained redundancy optimization problem for complex systems. An excursion is made into the infeasible region, but an adaptive penalty function helps the search not to go too far into the infeasible region. Thus, promising feasible & infeasible regions of the search space are explored efficiently & effectively to identify finally an optimal or near optimal solution. Computational experiments are conducted on 11 sets of problems (10 with linear constraints, and 1 with nonlinear constraints); each with 10 different randomly generated initial solutions. Comparison is made between the proposed algorithm P-Alg, N-N algorithm [15], Shi algorithm [20], and KYA [9] . It is observed that P-Alg performs consistently better than others, showing an overall improvement in various measures of performance. Besides, as P-Alg does not require any assumptions on the nature of the objective & constraint functions, it can solve a wide variety of problems.     

Control Structure Analysis and Recovery of Embedded Binaries

Existing decompilers use rule-based algorithms to transform unstructured Control flow graph (CFG) into equivalent high-level programming language constructs with "goto" statements. One problem of such approaches is that they generate a large number of "goto"s in the output code, which reduce the readability and hinder the understanding of input binaries. A global search algorithm is proposed based on structural analysis. This algorithm restructures a CFG and generates fewer number of "goto" statements than the rule-based algorithm does. We also present a Genetic algorithm (GA) for the global search approach to locate near optimal solutions for large CFGs. Evaluation results on a set of real CFGs show that the genetic algorithm-based heuristic for global search is capable of finding high-quality solutions.     

Speeding Up Fractal Image Compression by Genetic Algorithms

The main problem with all fractal compression implementation is execution time. Algorithms can spend hours to compress a single image. Most of the major variants of the standard algorithm for speeding up computation time have led to a bad-quality or a lower compression ratio. For example, the Fishers [7] proposed classification pattern greatly accelerated the algorithm, but image quality was poor due to the search-space reduction imposed by the classification, which eleminates a lot of good solutions.By using genetic algorithms to address the problem, we optimize the domain blocks search. We explore all domain blocks present in the image but not in exhaustive way (like a standard algorithm) and without omitting any possible block (solution) as a classification pattern does. A genetic algorithm is the unique method for satisfying these constraints. And it is a way to do be a random search because the genetic one is directed by fitness selection, which produces optimal solutions.Our goal in this work is to use a genetic algorithm to solve the IFS inverse problem and to build a fractal compression algorithm based on the genetic optimization of a domain blocks search. we have also implemented standard Barnsley algorithm, the Y. Fisher based on classification, and the genetic compression algorithm with quadtree partitioning. A population of transformations was evolved for each range block, and the result is compared with the standard Barnsely algorithm and the Fisher algorithm = based classification.We deduced an optimal set of values for the best parameters combination, and we can also specify the best combination for each desired criteria: best compression ratio, best image quality, or quick compression process. By running many test images, we experimentally found the following set of optimal values of all the algorithm parameters that ensure compromise between execution time and solutions optimality: Population size = 100, Maximum generations = 20, Crossover rate = 0.7, Mutation rate = 0.1, RMS limit = 5, Decomposition error limit = 10, Flips and isometrics count = 8.In our proposed algorithm, results were much better than those obtained both vences and Rudomin [5] and Lankhorst [4] approaches.First online version published in May 2005     

基于基因表达式编程的混合蚁群算法简

蚁群算法具有分布式并行全局搜索能力,通过信息素的积累和更新收敛于最优路径上,但初期信息素匮乏,求解速度慢。针对此问题,本文提出了一种先用基因表达式编程生成信息素分布,再利用蚁群算法求优化解的新的混合算法。并通过求解复杂TSP问题的仿真数据实验验证了这种基于基因表达式编程的混合蚁群算法的高效性。     

多目标量子编码遗传算法

如何使算法快速收敛到真正的Pareto前沿,并保持解集在前沿分布的均匀性是多目标优化算法重点研究解决的问题。该文提出一种基于量子遗传算法的多目标优化算法,利用量子遗传算法的高效全局搜索能力,在整个解空间内快速搜索多目标函数的Pareto最优解,利用量子遗传算法维持解集多样性的特点,使搜索到的Pareto最优解在前沿均匀分布。通过求解带约束的多目标函数优化问题,对该文算法的多目标优化性能进行了考察,并与NSGAII,PAES,MOPSO和Ray-Tai-Seows算法等知名多目标优化算法进行比较,结果证明了该文算法的有效性和先进性。     

A comparative study of three evolutionary algorithms incorporating different amounts of domain knowledge for node covering problem

This paper compares three different evolutionary algorithms for solving the node covering problem: EA-I relies on the definition of the problem only without using any domain knowledge, while EA-II and EA-III employ extra heuristic knowledge. In theory, it is proven that all three algorithms can find an optimal solution in finite generations and find a feasible solution efficiently; but none of them can find the optimal solution efficiently for all instances of the problem. Through experiments, it is observed that all three algorithms can find a feasible solution efficiently, and the algorithms with extra heuristic knowledge can find better approximation solutions, but none of them can find the optimal solution to the first instance efficiently. This paper shows that heuristic knowledge is helpful for evolutionary algorithms to find good approximation solutions, but it contributes little to search for the optimal solution in some instances.     

基于两阶段搜索算法的多峰函数优化

多峰优化问题需要搜索多个最优值（全局最优／局部最优）,这给传统的优化算法带来很大程度上的挑战。本文提出了一种两阶段算法求解多峰优化问题。第一阶段采用带有邻域变异策略的排挤差分演化算法进行粗粒度搜索,在适应度景观上尽可能多的找到最优解的大概位置。搜索一定代数之后,调用DMC聚类方法把搜索种群划分成多个聚类,然后在每个聚类上调用协方差矩阵自适应演化策略算法进行精细搜索。另外,本文还提出搜索点补充策略用于平衡每个聚类的大小及增加算法初期的搜索能力。我们提出的方法和9个较新的经典算法在两个基准测试集上进行了大量对比测试,结果表明新算法是有效的,在大多数测试函数上都优于其它算法。     

Wavelength Converters Placement in All Optical Networks Using Particle Swarm Optimization

Placement of wavelength converters in an arbitrary mesh network is known to be a NP-complete problem. So far, this problem has been solved by heuristic strategies or by the application of optimization tools such as genetic algorithms. In this paper, we introduce a novel evolutionary algorithm: particle swarm optimization (PSO) to find the optimal solution to the converters placement problem. The major advantage of this algorithm is that does not need to build up a search tree or to create auxiliary graphs in find the optimal solutions. In addition, the computed results show that only a few particles are needed to search the optimal solutions of the placement of wavelength converters problem in an arbitrary network. Experiments have been conducted to demonstrate the effectiveness and efficiency of the proposed evolutionary algorithm. It was found that the efficiency of PSO can even exceed 90% under certain circumstances. In order to further improve the efficiency in obtaining the optimal solutions, four strategic initialization schemes are investigated and compared with the random initializations of PSO particles.     

Memetic Algorithms for Optimizing Adaptive Linear Array Patterns by Phase-Position Perturbation

In this paper, based on the phase-position perturbation method, an innovative optimal adaptive antenna technique is proposed, where the deduced radiation pattern formulas available for searching optimal solutions are used to search the optimal weighting vector. The optimal radiation pattern designs of adaptive antenna are studied by the phase-position perturbation method. Memetic algorithms are used to search the optimal weighting vector of the phase-position perturbations for the array factor. The design for an optimal radiation pattern of an adaptive antenna can not only adjustably suppress the interferers by placing nulls at the directions of the interfering sources, but at the same time provide a maximum main lobe in the direction of the desired signal, i.e., to maximize the signal-to-interference ratio. To achieve this goal, a new convergent method, referred to as the two-way convergent method for memetic algorithms, is proposed. The memetic algorithm combines a genetic algorithm and local search heuristics to solve combinatorial optimization problems. The memetic algorithm is a kind of improved type of the traditional genetic algorithm. By using a local search procedure, it can avoid the shortcomings of the traditional genetic algorithm, whose termination criteria are set up by using the trial and error method. This proposed method is also able to solve the multipath problem, which exists at the same time in this communication system. The optimal radiation pattern concept can be implemented in practical wireless communication systems. Simulation results are also given in this paper.     

半定规划上一种有效的内点法

在半定规划的内点算法中,中心参数的选择对于算法的复杂性和有效性是尤为重要的。但以往半定规划的论文中,中心参数是固定的,这大幅增加了算法的复杂性并降低了有效性。文中基于宽邻域提出了一种有效可地行内点算法,使中心参数与步长成多项式的关系,这样中心参数会随着步长的变化而更新。从而每次迭代均取到最优参数,且在文中,基于NT方向,证明了该算法在理论上的复杂性和有效性均是最优的。