Convergence analysis of hybrid cellular automata for topology optimization

The hybrid cellular automaton (HCA) algorithm was inspired by the structural adaptation of bones to their ever changing mechanical environment. This methodology has been shown to be an effective topology synthesis tool. In previous work, it has been observed that the convergence of the HCA methodology is affected by parameters of the algorithm. As a result, questions have been raised regarding the conditions by which HCA converges to an optimal design. The objective of this investigation is to examine the conditions that guarantee convergence to a Karush-Kuhn-Tucker (KKT) point. In this paper, it is shown that the HCA algorithm is a fixed point iterative scheme and the previously reported KKT optimality conditions are corrected. To demonstrate the convergence properties of the HCA algorithm, a simple cantilevered beam example is utilized. Plots of the spectral radius for projections of the design space are used to show regions of guaranteed convergence.     

Multiscale topology optimization for non-uniform microstructures with hybrid cellular automata

Based on hybrid cellular automata (HCA), we present a two-scale optimization model for heterogeneous structures with non-uniform porous cells at the micros     

Improved hybrid cellular automata for crashworthiness optimization of thin-walled structures

Thin-walled structures are of great importance in automotive crashworthiness design, because of their high crash energy absorption capability and their high potential for light weighting. To identify the best compromise between these two requirements, numerical optimization is needed. Size and shape optimization is relatively well explored while topology optimization for crash is still an open issue. Hence, this paper proposes an approach based on hybrid cellular automata (HCA) for crashworthiness topology optimization with a special focus on thin-walled structures. First approaches have been published, e.g. Duddeck et al. (Struct Multidiscip Optim 54(3):415–428, 2016), using a simple rule to define the target mass for the inner loop of the HCA. To improve the performance, a modified scheme is proposed here for the outer optimization loop, which is based on a bi-section search with limited length. In the inner loop, hybrid updating rules are used to redistribute the mass and a mass correction technique is proposed to make the real mass converge to the target mass strictly. The efficiency and correctness of the proposed method is compared with LS-OPT for axial crash case. Two different methods of defining the target mass in the outer loop are studied, the proposed bi-section search with limited length shows its advantage in two types of three-point bending crash optimization cases. Another advantage of this method is that it requires no significantly increasing number of evaluations when the number of design variables increases. This is demonstrated by applying this method to a crashworthiness optimization problem with 380 design variables.     

结合元胞自动机的果蝇优化算法

果蝇优化算法(FOA)作为一类新的优化搜索算法,广泛应用于各种优化问题。针对该算法后期求解精度低、容易陷入局部最优且收敛缓慢的缺点,提出一种结合元胞自动机的果蝇优化算法(CAFOA)。该算法在首次求解时利用元胞演化规则选择果蝇最优个体邻域,然后对选择后的果蝇个体位置进行随机扰动,分别用邻域个体复制更新演化前个体位置,再次进行迭代寻优,从而有效克服算法陷入局部最优。对6种常见测试函数进行了运算仿真。实验结果表明,所提算法比传统算法的平均收敛精度提高10%,达到稳定全局最优值的平均迭代次数减少870次,从而论证了算法的有效性。     

一种基于元胞自动机的无向图剖分优化算法

运用元胞自动机理论,针对无向图剖分优化问题进行了分析和建模,提出了一种元胞自动机模型以及基于该模型的无向图剖分优化算法。在该元胞自动机模型中,元胞对应于无向图中的结点,元胞的邻居对应于邻接结点,元胞空间对应于无向图中的结点集,元胞的状态对应于所在的结点子集。实验及分析表明该算法不仅能找到无向图的近似最优剖分,而且有效地降低了空间复杂度和时间复杂度。     

基于元胞自动机的传染病传播模型研究

从复杂适应系统的观点,通过建立元胞自动机模型的方法模拟疾病传播这个复杂的过程.并对SARS地传播过程成功地进行了模拟。同时以此为基础针对可能对传染病产生影响的几种因素作了具体地考察,如人员的移动、及时就医等,考察这些因素对控制传染病达到稳定的具体影响,并给出一些控制这类问题的建议。     

基于元胞自动机的动态回溯搜索优化算法

针对传统回溯搜索优化算法存在收敛速度慢、搜索精度不高等问题,提出了一种基于元胞自动机和正交实验设计的改进算法。首先将正交实验设计方法引入算法的交叉算子中,得出具有代表性的优质子代个体;然后在元胞自动机邻居模型的基础上,对个体展开领域内多父代正交交叉操作,提高算法的开采能力和搜索效率;最后对参与交叉的种群引入动态优秀个体比例权重进行选择更新,并采用新的动态变异方程,平衡算法的全局搜索和局部搜索能力。通过对12个标准测试函数进行仿真实验,并与其他六种表现良好的算法进行比较,结果表明,改进的算法在收敛速度以及寻优精度方面都具有明显优势。     

基于空间信息多级网格的CA模型研究

具备时空计算特征的元胞自动机(CA)模型与GIS集成极大促进了GIS对地理过程的模拟能力。论文简要介绍了空间信息多级网格(SIMG)——一种既能适合网格计算环境又充分考虑到地球空间的自然特征和社会属性的差异性及经济发展不平衡的特点的空间信息表示新方法。充分研究了SIMG与CA之间的联系,分别讨论了在SIMG上CA元胞及状态的确定、元胞空间的确定、规则的定义、时间粒度确定等,提出了空间信息多级网格元胞自动机模型(SIMGCA),并提出了SIMGCA模型在土地利用/覆被变化中的应用框架。     

基于元胞自动机的网络舆论激励模型

为了研究网络舆论的传播过程及发展趋势，建立了一个基于元胞自动机的网络舆论激励模型，用于模拟网络舆论形成过程中个体发表言论数的变化以及个体观点的变化。在模型中，将网络空间的个体抽象为以情感描述的元胞，用情感倾向度和情感倾向度门限来确定元胞发表言论的状态，用情感激励来描述元胞的移动规则。同时，该模型考虑了个体数量增减和社会突发事件对网络舆论传播的影响，从而更能准确地模拟网络现实事件。     

Asynchronous cellular learning automata

Cellular learning automata is a combination of cellular automata and learning automata. The synchronous version of cellular learning automata in which all learning automata in different cells are activated synchronously, has found many applications. In some applications a type of cellular learning automata in which learning automata in different cells are activated asynchronously (asynchronous cellular learning automata) is needed. In this paper, we introduce asynchronous cellular learning automata and study its steady state behavior. Then an application of this new model to cellular networks has been presented.     

A new real-coded Bayesian optimization algorithm based on a team of learning automata for continuous optimization

Estimation of distribution algorithms have evolved as a technique for estimating population distribution in evolutionary algorithms. They estimate the distribution of the candidate solutions and then sample the next generation from the estimated distribution. Bayesian optimization algorithm is an estimation of distribution algorithm, which uses a Bayesian network to estimate the distribution of candidate solutions and then generates the next generation by sampling from the constructed network. The experimental results show that the Bayesian optimization algorithms are capable of identifying correct linkage between the variables of optimization problems. Since the problem of finding the optimal Bayesian network belongs to the class of NP-hard problems, typically Bayesian optimization algorithms use greedy algorithms to build the Bayesian network. This paper proposes a new real-coded Bayesian optimization algorithm for solving continuous optimization problems that uses a team of learning automata to build the Bayesian network. This team of learning automata tries to learn the optimal Bayesian network structure during the execution of the algorithm. The use of learning automaton leads to an algorithm with lower computation time for building the Bayesian network. The experimental results reported here show the preference of the proposed algorithm on both uni-modal and multi-modal optimization problems.     

Genetic learning automata for function optimization

Stochastic learning automata and genetic algorithms (GAs) have previously been shown to have valuable global optimization properties. Learning automata have, however, been criticized for having a relatively slow rate of convergence. In this paper, these two techniques are combined to provide an increase in the rate of convergence for the learning automata and also to improve the chances of escaping local optima. The technique separates the genotype and phenotype properties of the GA and has the advantage that the degree of convergence can be quickly ascertained. It also provides the GA with a stopping rule. If the technique is applied to real-valued function optimization problems, then bounds on the range of the values within which the global optima is expected can be determined throughout the search process. The technique is demonstrated through a number of bit-based and real-valued function optimization examples.     

基于二维概率元胞自动机的HBV动力学模型

为描述免疫系统中细胞微粒的多样性以及微粒运动和相互作用的随机性,在传统元胞自动机基础上,借用粗粒化思想,将多个状态变量引入元胞之中,并提出了“次级元胞”的概念,以表示不同类型的细胞粒子;应用该模型模拟乙型肝炎病毒（HBV）在无药物治疗情况下的自然感染和抗HBV药物药效比较,以及考察肝细胞感染率的影响因素,获得了较好的效果。     

消费者口碑传播行为及交互影响的模拟仿真研究

基于元胞自动机模型对消费者正面口碑、负面口碑和中立口碑传播行为之间的影响作用和动态演变进行了模拟仿真,讨论了在不同消费者初始状态、行为保持性、行为传播性和实施不同政策力度条件下,消费者口碑传播行为演化的趋势和状态。模拟结果得到如下结论：a）口碑传播网络中消费者的初始状态对系统演化的走向起重要作用,要重视对现有消费者构成的调查和研究;b）行为传播力度增大,对口碑传播的影响增大,能够引起口碑系统的连锁反应,系统构成发生很大改变;c）消费者自身口碑态度的保持性对口碑系统的演化有重要影响;d）对正面口碑的鼓励政策能够在很大程度上提高消费者的正面传播,从而提高整个系统的消费者忠诚度。研究能够帮助企业正确理解消费者口碑行为及相互影响作用,制定针对性的营销与售后管理策略,从而有效控制和引导消费者口碑行为。     

基于元胞自动机的模糊控制换道模型

根据元胞自动机理论建立改进的交通流模型,给出每辆车的演化规则。在此基础上依据实际车辆行为建立换道规则,利用模糊推理来模拟人在换道过程中的主观判断过程,建立换道模型。仿真表明该方法能较好的模拟车辆的实际行为。     

A hybrid method for learning multi-dimensional Bayesian network classifiers based on an optimization model

Bayesian networks, which have a solid mathematical basis as classifiers, take the prior information of samples into consideration. They have gained considerable popularity for solving classification problems. However, many real-world applications can be viewed as classification problems in which instances have to be assigned to a set of different classes at the same time. To address this problem, multi-dimensional Bayesian network classifiers (MBCs), which organize class and feature variables as three subgraphs, have recently been proposed. Because each subgraph has different structural restrictions, three different learning algorithms are needed. In this paper, we present for the first time an MBC learning algorithm based on an optimization model (MBC-OM) that is inspired by the constraint-based Bayesian network structure learning method. MBC-OM uses the chi-squared statistic and mutual information to estimate the dependence coefficients among variables, and these are used to construct an objective function as an overall measure of the dependence for a classifier structure. Therefore, the problem of searching for an optimal classifier becomes one of finding the maximum value of the objective function in feasible fields. We prove the existence and uniqueness of the numerical solution. Moreover, we validate our method on five benchmark data sets. Experimental results are competitive, and outperform state-of-the-art algorithms for multi-dimensional classification.     

复杂网络病毒传播元胞自动机模型及研究

为了研究免疫有效时间对复杂网络中病毒传播的影响,基于元胞自动机建立复杂网络不完全免疫的病毒传播模型,并分别在最近邻耦合网络、Erdos-Renyi随机网络、Watts-Strogatz小世界网络和Barabasi-Albert无标度网络中进行仿真研究。结果表明：节点免疫有效时间的增大,能够有效地遏制复杂网络病毒传播范围并增大病毒传播阈值。     

Topology optimization for crashworthiness of thin-walled structures under axial impact using hybrid cellular automata

Although topology optimization is well established in most engineering fields, it is still in its infancy concerning highly non-linear structural applications like vehicular crashworthiness. One of the approaches recently proposed and based on Hybrid Cellular Automata is modified here such that it can be applied for the first time to thin-walled structures. Classical methods based on voxel techniques, i.e., on solid three-dimensional volume elements, cannot derive structures made from thin metal sheets where the main energy absorption mode is related to plastic buckling, folding and failure. Because the main components of car structures are made from such thin-walled beams and panels, a special approach using SFE CONCEPT was developed, which is presented in this paper.     

考虑坡道的元胞自动机交通流模型研究*

为了更好地研究坡道交通流的特征,基于Gipps安全驾驶的思想,考虑了坡道长度和纵坡度等因素,建立了一个新的更为精细的坡道交通流元胞自动机模型,并通过计算机数值模拟,对坡道交通流特征进行了分析。获得如下新的发现：坡道加剧了道路的拥堵,而且拥堵流和自由流之间存在一条十分明显的分界线,冲击波传递到分界线时发生了消散。此外,在最大通行能力附近,交通流密度-流量曲线会在一定坡道长度范围内（如40 < Lp < 80）进行集聚,坡道长度在这个范围对通行能力的影响相接近。坡道的纵坡度相对于坡道的长度而言,对道路的通行能力影响更为明显。     

基于元胞自动机的普通超市火灾疏散模型的构建

通过分析已有的元胞自动机理论,结合人员疏散的特点,构建了考虑出口吸引力、火灾排斥力、摩擦力、排斥力和从众吸引力的普通超市火灾疏散模型。该模型充分考虑了多个因素对疏散过程的影响,对影响因素进行归一化处理,以综合影响因素建立的元胞转移强度作为行人移动规则。针对超市疏散人数、出口宽度及相隔距离、从众心理对疏散时间的影响进行了研究,并采用仿真疏散软件Pathfinder+FDS对模型进行验证,说明疏散模型具有一定可信性。研究表明行人疏散时间随人数呈线性正相关,人数存在临界值;出口宽度越宽或出口越多疏散时间相对越短,当达到出口阈值时对疏散时间影响不大;在陌生疏散环境或紧急情况下适当的从众行为会提高疏散效率。