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.     

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。     

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.     

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。     

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.     

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.     

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.     

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。     

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.     

An enhanced copy-move forgery detection using machine learning based hybrid optimization model

Multimedia Tools and Applications - In recent years, one of the forgery detection methods is the Copy-Move Forgery Detection (CMFD), which is mostly used approach amongst all the forgery...     

Design of materials using hybrid cellular automata

This paper presents a new approach for the topological design of materials with extreme properties. The method is based on hybrid cellular automaton (HCA), which is an implicit optimization technique that uses local rules to update design variables iteratively until meeting the described optimality conditions. By means of an energy-based homogenization approach, the effective properties of the considered material are calculated in terms of element mutual energies. By this method, no sensitivity information is required to find the optimal topology for the considered design objectives: bulk modulus, shear modulus, and negative Poisson’s ratio. The proposed method is validated by a series of numerical examples.     

A cellular automata model for edge relaxation

An edge detection method based on a fuzzy cellular automata model which serves as the relaxation labeling process constraint is described. An initial estimate of edge locations is made and the remaining ambiguities are resolved by thinning and enhancing the edges through several iterations. An efficient fixed step algorithm is presented and its performance is evaluated for different noise level images. The method is useful for the detection of linear image features in three-dimensional robot vision systems.     

基于混合模型的持续二进制优化框架

提出了一项基于混合模型的二进制优化框架,能在运行时和运行后进行持续的程序优化.该框架集成了控制流分析等常用的二进制优化功能,并且提供了安全的编程接口以供其他研究者实现自定义的优化模块.该框架被设计为集成到操作系统内核中,并提供透明和自适应的优化服务,从而在没有用户交互的前提下,利用富余的计算资源帮助二进制程序自适应计算环境,达到加速的目的.描述了该框架的设计与实现以及关键问题的解决方法.     

Variable factorization model based on numerical optimization for hyperspectral anomaly detection

The objective of this article is to develop an anomaly detector as an analytical expression for detecting anomalous objects in remote sensing using hyperspectral imaging. Conventional anomaly detectors based on the subspace model have a parameter which is the dimension of the clutter subspace. The range of possible values for this parameter is typically large, resulting in a large number of images of detector output to be analyzed. An anomaly detector with a different parameter is proposed. The pixel of known random variables from a data cube is modeled as a linear transformation of a set of unknown random variables from the clutter subspace plus an error of unknown random variables in which the transformation matrix of constants is also unknown. The dimension of the clutter subspace for each spectral component of the pixel can vary, hence some elements in the transformation matrix are constrained to be zeros. The anomaly detector is the Mahalanobis distance of the resulting residual. The experimental results which are obtained by implementing the anomaly detector as a global anomaly detector in unsupervised mode with background statistics computed from hyperspectral data cubes with wavelengths in the visible and near-infrared range show that the parameter in the anomaly detector has a significantly reduced number of possible values in comparison with conventional anomaly detectors.     

A quantum model for autonomous learning automata

The idea of information encoding on quantum bearers and its quantum-mechanical processing has revolutionized our world and brought mankind on the verge of enigmatic era of quantum technologies. Inspired by this idea, in present paper, we search for advantages of quantum information processing in the field of machine learning. Exploiting only basic properties of the Hilbert space, superposition principle of quantum mechanics and quantum measurements, we construct a quantum analog for Rosenblatt’s perceptron, which is the simplest learning machine. We demonstrate that the quantum perceptron is superior to its classical counterpart in learning capabilities. In particular, we show that the quantum perceptron is able to learn an arbitrary (Boolean) logical function, perform the classification on previously unseen classes and even recognize the superpositions of learned classes—the task of high importance in applied medical engineering.     

Frequent itemset mining using cellular learning automata

A core issue of the association rule extracting process in the data mining field is to find the frequent patterns in the database of operational transactions. If these patterns discovered, the decision making process and determining strategies in organizations will be accomplished with greater precision. Frequent pattern is a pattern seen in a significant number of transactions. Due to the properties of these data models which are unlimited and high-speed production, these data could not be stored in memory and for this reason it is necessary to develop techniques that enable them to be processed online and find repetitive patterns. Several mining methods have been proposed in the literature which attempt to efficiently extract a complete or a closed set of different types of frequent patterns from a dataset. In this paper, a method underpinned upon Cellular Learning Automata (CLA) is presented for mining frequent itemsets. The proposed method is compared with Apriori, FP-Growth and BitTable methods and it is ultimately concluded that the frequent itemset mining could be achieved in less running time. The experiments are conducted on several experimental data sets with different amounts of minsup for all the algorithms as well as the presented method individually. Eventually the results prod to the effectiveness of the proposed method.     

DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization

Differential evolution (DE) is a fast and robust evolutionary algorithm for global optimization. It has been widely used in many areas. Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based migration operator to share the information among solutions. In this paper, we propose a hybrid DE with BBO, namely DE/BBO, for the global numerical optimization problem. DE/BBO combines the exploration of DE with the exploitation of BBO effectively, and hence it can generate the promising candidate solutions. To verify the performance of our proposed DE/BBO, 23 benchmark functions with a wide range of dimensions and diverse complexities are employed. Experimental results indicate that our approach is effective and efficient. Compared with other state-of-the-art DE approaches, DE/BBO performs better, or at least comparably, in terms of the quality of the final solutions and the convergence rate. In addition, the influence of the population size, dimensionality, different mutation schemes, and the self-adaptive control parameters of DE are also studied.     

GSA-LA: gravitational search algorithm based on learning automata

ABSTRACT

Regardless of the performance of gravitational search algorithm (GSA), it is nearly incapable of avoiding local optima in high-dimension problems. To improve the accuracy of GSA, it is necessary to fine tune its parameters. This study introduces a gravitational search algorithm based on learning automata (GSA-LA) for optimisation of continuous problems. Gravitational constant G(t) is a significant parameter that is used to adjust the accuracy of the search. In this work, learning capability is utilised to select G(t) based on spontaneous reactions. To measure the performance of the introduced algorithm, numerical analysis is conducted on several well-designed test functions, and the results are compared with the original GSA and other evolutionary-based algorithms. Simulation results demonstrate that the learning automata-based gravitational search algorithm is more efficient in finding optimum solutions and outperforms the existing algorithms.