求解连续空间优化问题的改进蜂群算法

为了有效地解决人工蜂群算法容易陷入局部最优的缺陷,提出了一种改进蜂群算法。首先,利用反向学习方法构建初始种群,以提高初始化解的质量。同时,利用分布估计算法构造优秀个体解空间的概率模型以进行邻域搜索,以改善算法的搜索性能并防止陷入局部最优。对连续空间优化问题进行了仿真实验,结果表明改进算法具有较快的收敛速度,全局寻优能力显著提高。     

具有自适应全局最优引导快速搜索策略的人工蜂群算法

针对人工蜂群算法存在开发与探索能力不平衡的缺点,提出了具有自适应全局最优引导快速搜索策略的改进算法.在该策略中,首先采蜜蜂利用自适应搜索方程平衡了不同搜索方法的探索和开发能力;其次跟随蜂利用全局最优引导邻域搜索方程对蜜源进行精细化搜索,以提高其收敛精度和全局搜索能力.14个标准测试函数的仿真结果表明,相比其他算法,所提出的改进算法有效平衡了算法的开发与探索能力,并提高了其最优解的精度及收敛速度.     

GPU适用的并行纹理合成算法

基于样图的纹理合成是一个大计算量过程,为了利用GPU的并行计算能力进行大规模纹理合成,我们提出一种并行纹理合成算法.该算法综合块查找和全局纹理优化算法分多遍进行纹理的合成和优化,其中每一遍分为串行纹理块定位和并行最优块匹配2个阶段.纹理块定位阶段在CPU端按照扫描线顺序确定待合成的邻域,并将邻域位置传入GPU;最优块匹配阶段在GPU端并行计算待合成邻域与对应样本邻域的全局距离,并查找出最优解得到匹配块.最后根据匹配过程统计数据自适应调整优化规模,在全局范围内对纹理进行迭代优化.实验结果表明,文中算法在保证大规模纹理合成效果的基础上减少了计算时间,能够满足交互式纹理合成的应用.     

基于邻域相关性多阈值新函数寻优法的小波降噪分析

为了更好地获取噪声影响下的原有信号,在邻域小波系数收缩的NeighCoeff方法基础之上,提出了一种邻域相关性多阈值新函数的小波降噪方法.该方法根据小波系数之间的相关性,将邻域窗口内所有小波系数的平方和的大小划分为邻域硬阈值、邻域窗口阈值和邻域扩张阈值.将这些邻域阈值与修正的通用阈值相比较,来实现窗口尺寸的自适应调节和小波系数的保留或收缩,以此达到消噪的目的.此外新函数的收缩因子能够较好体现与被滤波噪声的相互关系,可以进一步提高消噪的精度.然后将多阈值函数与修正的全局阈值相结合,利用混沌粒子群对邻域扩张阈值参数γ和修正的全局阈值参数α进行寻优,以获取最优小波系数的重构信号.所提方法与其它阈值函数去噪方法相比,其仿真结果表明在信号信噪比、降低有用信号失真和抑制噪声等方面都有一定的提高.     

一种基于压缩感知的邻域优化算法

非线性降维方法是目前对降维研究有着重要影响的方法,但在降维过程中经常会遇到局部邻域信息量不足、短路和噪声干扰等问题,严重影响降维效果,很难广泛应用于真实数据的处理中.对以上问题分析发现,其主要原因在于经典降维算法都是采用全局固定的邻域大小.提出了一种基于压缩感知的邻域优化算法,运用压缩感知技术对高维空间目标点近邻进行压缩采样,构建“收—放”模型,自适应得到最优子空间,同时优化邻域组成元素,使得数据的整体降维效果更加稳定.通过手工流形和真实数据集的实验,验证了算法的有效性和稳定性.     

一种改进的禁忌搜索法在函数优化问题中的应用

禁忌搜索法对初始解、邻域个数及禁忌列表的大小等参数有比较严格的要求,这些参数直接影响着算法的优化能力.文章提出了一种改进的禁忌搜索法,它用有效空间来压缩搜索范围,这样可以提高搜索效率和全局搜索能力.用短期和长期禁忌列表存储器来保证算法能搜索到全局空间的每一点,并且不重复搜索.经过验算和分析,证明它是一种较好的全局启发式搜索法.     

并行机间歇过程生产调度的遗传局部搜索算法

研究了一类集成分批的并行机间歇过程调度问题(parallel machine batch process scheduling problem,简称PBPSP),将此问题转化为固定费用运输问题(6xed charge transportation problem,简称FCTP)后,提出了具有集中邻域搜索机制和局部最优逃逸机制的遗传局部搜索算法(genetic local search algorithm,简称GLSA).GLSA算法用先根遍历边排列模式编码生成树解,具有高效的子树补充式单点交叉操作.将基于网络单纯型方法的邻域搜索作为变异算子,并提出了连续随机节点邻域搜索的集中邻域搜索策略以及随机旋转变异与全局邻域搜索相结合的局部最优逃逸策略,极大地强化了遗传局部搜索算法的全局寻优能力.实验表明:GLSA算法获得的解质量优于基于排列编码的遗传算法和基于矩阵编码的遗传算法,得到了所有Benchmark问题的最优解,且具有高鲁棒性.针对一定规模的FCTP问题,GLSA算法比Tabu启发式搜索算法具有更高的获得最优解几率.     

带审敛因子的变邻域粒子群算法

针对基本粒子群算法在求解高维空间中的复杂多峰函数时容易发生早熟收敛而陷入局部最优的问题,汲取变邻域搜索算法全局搜索的优势,提出了带审敛因子的变邻域粒子群算法.首先由基本粒子群的快速搜索能力得到较优的群体;然后通过审敛因子判断发生早熟收敛的粒子,并利用变邻域搜索算法的全局搜索能力对陷入早熟收敛的粒子进行优化,从而得到全局最优.相关实验表明,带审敛因子的粒子群算法的性能较常规粒子群算法更加优越.     

混合分散搜索算法求解带容量约束车辆路径问题

设计一种解决带容量约束车辆路径问题的混合分散搜索算法. 在基本分散搜索的基础上, 保留参考集更新策略和组合策略的全局搜索能力. 采用随机插入法作为解的多样性产生方法, 以扩大搜索空间, 避免陷入局部最优.应用简化的变邻域搜索作为改进策略进行局部开发, 引入邻域半径减少策略提高开发效率. 对改进后的新种群实施精英保留策略, 保证算法收敛. 实验结果分析表明, 混合分散搜索算法优于所对比的算法, 寻优能力可靠.

     

基于动态邻域和自适应惯性权重的微粒群算法

针对微粒群优化解决复杂优化问题时易陷入局部收敛、效率不高的缺点,提出一种基于动态邻域和自适应惯性权重的微粒群优化算法.通过定义动态邻域及其最优维值,提出种群个体的动态邻域最优维值学习策略,使微粒跟踪个体极值和邻域的最优维值进行搜索,以增加学习样本的多样性,避免局部收敛;提出一种基于个体适应度的惯性权重动态调整方法,提高算法的寻优效率.通过优化5个典型测试函数验证了本文所提方法的有效性.     

Optimum design of the 5R symmetrical parallel manipulator with a surrounded and good-condition workspace

This paper concerns the optimum design issue of the 5R symmetrical parallel manipulator with a surrounded workspace. Generally, such a manipulator has a very large workspace. With different working modes, a manipulator will have different singular loci and workspaces. In this paper, the singularity and the usable workspace without singularity inside will be determined for the manipulator with a specified mode. The usable workspace can be used to define the global conditioning index (GCI). In order to obtain the optimum design of the manipulator, a non-dimensional design space is established. Because each of the non-dimensional manipulators in the established design space can represent the performances of all of its possible similarity manipulators, the design space is a very useful tool for guaranteeing a global comparative result. Within the design space, the singularity, usable workspace and control accuracy (evaluated using the GCI) are studied and the corresponding atlases are constructed. Based on the atlases, one can synthesize link lengths of the manipulator studied with respect to specified criteria. One example will be given to show how to use the atlases. In particular, an example will be presented of reaching the optimum dimensional result with respect to a desired practical workspace based on the optimum non-dimensional result identified from the atlases. For the reason that using the atlases presented in this paper a designer can obtain the optimum result with respect to any specification, the optimum design method proposed in this paper may be accepted by others.     

基于量子行为粒子群优化的软子空间聚类算法*

针对软子空间聚类算法搜寻聚类中心点容易陷入局部最优的缺点,提出在软子空间聚类框架下,结合量子行为粒子群优化(QPSO)和梯度下降法优化软子空间聚类目标函数的模糊聚类算法.根据QPSO全局寻优的特点,求解子空间中全局最优中心点,利用梯度下降法收敛速度快的特点,求解样本点的模糊权重和隶属度矩阵,最终获取样本点的最优聚类结果.在UCI数据集上的实验表明,文中算法可提高聚类精度和聚类结果的稳定性.     

基于遗传模糊C-均值聚类算法的地图分割

图像分割和对象提取是从图像处理到图像分析的关键步骤。经典的模糊C-均值聚类算法(FCMA)是将图像分割成C类的常用方法，但依赖于初始聚类中心的选择。该算法通常得到的是局部最优解而非全局最优解。遗传算法是一类全局优化搜索算法。通过将遗传算法(GA)与FCMA相结合，对彩色地图直接按红绿蓝(RGB)三色空间进行聚类，用遗传算法搜索全局最优解，有效地避免了模糊C-均值聚类算法收敛到局部最优的问题，并在此基础上实现了对彩色地图的分割，得到了比较满意的效果。     

Space exploration and global optimization for computationally intensive design problems: a rough set based approach

Modern engineering design problems often involve computation-intensive analysis and simulation processes. Design optimization based on such processes is desired to be efficient, informative and transparent. This work proposes a rough set based approach that can identify multiple sub-regions in a design space, within which all of the design points are expected to have a performance value equal to or less than a given level. The rough set method is applied iteratively on a growing sample set. A novel termination criterion is also developed to ensure a modest number of total expensive function evaluations to identify these sub-regions and search for the global optimum. The significance of the proposed method is twofold. First, it provides an intuitive method to establish the mapping from the performance space to the design space, i.e. given a performance level, its corresponding design region(s) can be identified. Such a mapping could be potentially used to explore and visualize the entire design space. Second, it can be naturally extended to a global optimization method. It also bears potential for more broad application to problems such as metamodeling-based design and robust design optimization. The proposed method was tested with a number of test problems and compared with a few well-known global optimization algorithms.     

Deception,dominance and implicit parallelism in genetic search

This paper presents several theorems concerning the nature of deception, its relationship to hyperplane dominance, and the central role that deception plays in function optimization using genetic algorithms. The theoretical results relate to four general themes. First, the concept of a deceptive attractor is introduced; it is shown that a deceptive attractor must be the complement of the global solution for a problem to be fully deceptive. It is also shown that the deceptive attractor must either be a local optimum in Hamming space, or adjacent to a local optimum in Hamming space if the problem is fully deceptive. Second, it can be shown that the global solution to nondeceptive problems can be inferred (theoretically and often in practice) by determining the winners of the order-1 hyperplanes. The third theme relates the concept of deception and dominance. If a dominance relationship exists between two hyperplanes then deception is impossible between those two partitions of hyperspace; analogously, deception between two hyperplanes precludes a dominance relationship. The fourth theme relates to deception and implicit parallelism. It can be shown that if a genetic algorithm reliably allocates exponentially more trials to the observed best, then implicit parallelism (and the 2-arm bandit analogy) breaks down when deception is present.     

一种多空间FCM算法

FCM是经典的聚类算法,广泛地应用于模式识别、数据挖掘等领域。FCM算法是一种梯度下降优化算法,对初始解敏感并且容易获得局部最优解。空间平滑能够避免启发式局部搜索算法掉入局部最优解。采用空间平滑策略构造一系列光滑程度不同的搜索空间,在不同的搜索空间中执行FCM算法,并利用前层搜索空间的聚类结果来引导本层搜索空间的聚类。FCMS(FCM based on multi-Space)能够跳过局部最优解的"陷阱",增大获得全局最优解的概率,达到提高聚类质量的目的。给出了等距法空间平滑策略,并通过实验对比了FCMS算法与FCM算法的聚类质量。实验结果表明,空间平滑对FCM算法非常有效。     

Ensemble of surrogate based global optimization methods using hierarchical design space reduction

For computationally expensive black-box problems, surrogate models are widely employed to reduce the needed computation time and efforts during the search of the global optimum. However, the construction of an effective surrogate model over a large design space remains a challenge in many cases. In this work, a new global optimization method using an ensemble of surrogates and hierarchical design space reduction is proposed to deal with the optimization problems with computation-intensive, black-box objective functions. During the search, an ensemble of three representative surrogate techniques with optimized weight factors is used for selecting promising sample points, narrowing down space exploration and identifying the global optimum. The design space is classified into: Original Global Space (OGS), Promising Joint Space (PJS), Important Local Space (ILS), using the newly proposed hierarchical design space reduction (HSR). Tested using eighteen representative benchmark and two engineering design optimization problems, the newly proposed global optimization method shows improved capability in identifying promising search area and reducing design space, and superior search efficiency and robustness in identifying the global optimum.     

基于多维网格空间的改进K-means聚类算法

K-means算法是被广泛使用的一种聚类算法,传统的K-means算法中初始聚类中心的选择具有随机性,易使算法陷入局部最优,聚类结果不稳定。针对此问题,引入多维网格空间的思想,首先将样本集映射到一个虚拟的多维网格空间结构中,然后从中搜索出包含样本数最多且距离较远的子网格作为初始聚类中心网格,最后计算出各初始聚类中心网格中所包含样本的均值点来作为初始聚类中心。此法选择出来的初始聚类中心与实际聚类中心拟合度高,进而可据此初始聚类中心稳定高效地得到最终的聚类结果。通过使用计算机模拟数据集和UCI机器学习数据集进行测试,结果表明改进算法的迭代次数和错误率比较稳定,且均小于传统K-means算法测试结果的平均值,能有效避免陷入局部最优,并且聚类结果稳定。     

A genetic algorithm-based search space splitting pattern and its application in hydraulic and coastal engineering problems

This article reports a search space splitting pattern that can be applied to genetic algorithms in order to ensure that the entire search space is investigated. Hence, by keeping the genetic algorithm simple, in a reasonable time and with a high degree of accuracy, the initial solutions can be improved toward the global optimum point. The simplicity of the presented method is an advantage that makes it useful for applied hydraulic and coastal engineering problems. The performance of the proposed method was evaluated by a benchmark optimization problem, Levy No. 5, and three hydraulic and coastal engineering problems: inverse problem of Manning’s equation, the equation of equilibrium beach profiles, and the settling velocity equation of natural sediment particles. The results indicated that the nonlinear complex problems can be solved by the proposed method with a high degree of accuracy. The proposed genetic algorithm-based search space splitting pattern can either be used exclusively or alternatively it can be combined with improved operators in the literature.

     

遗传算法的机理与收敛性研究

采用一种新的基于解空间分解的定量分析方法,对遗传算法的种群进化过程进行分析,阐明了选择、交叉和变异操作的寻优机理,给出了子代种群在解空间上的概率分布情况;理论上,证明了遗传算法具备寻找全局最优解的能力,并给出了具备寻找全局最优解能力的充分必要条件,即证明了积木块假设的结论是成立的.同时,建立了二进制编码有限群体的M arkov链模型,计算出在用于静态优化问题的交叉和变异操作下,种群在解空间上概率分布情况以及收敛到最优解的概率,并讨论了产生早熟现象和GA-欺骗问题的原因.