On calibration and validation of eigendeformation-based multiscale models for failure analysis of heterogeneous systems

We present a new strategy for calibration and validation of hierarchical multiscale models based on computational homogenization. The proposed strategy hinges on the concept of the experimental simulator repository (SIMEX) which provides the basis for a generic algorithmic framework in calibration and validation of multiscale models. Gradient-based and genetic algorithms are incorporated into SIMEX framework to investigate the validity of these algorithms in multiscale model calibration. The strategy is implemented using the eigendeformation-based reduced order homogenization (EHM) model and integrated into a commercial finite element package (Abaqus). Ceramic- and polymer- matrix composite problems are analyzed to study the capabilities of the proposed calibration and validation framework.     

GA算子的代数模型

作为对生物进化的模拟,遗传算法的表述方法和用语与生物进化的基本相同。这种表述方法虽然形象,但往往容易掩盖遗传算法的数学本质,为进一步研究带来困难,试图揭示遗传算法的数学本质,建立它的代数表述形式,并为其应用提供一种更简明易读的模型。     

Cure cycle design for composite materials using computer simulation and optimisation tools

In the following we present a computer simulation tool coupled with a numerical optimisation method that have been developed for use in the optimal design of the cure cycle of the production of thermoset- matrix composite parts. Their use permits the fast and straightforward optimal design of the cure cycles and leads to improved quality, reduced production times and reduced scrap. For the simulation of the cure cycle a one-dimensional non-linear transient model has been developed and coupled with the Evolution Strategy, of the Genetic Algorithms family, for the optimal design of the cure cycle of thick composite parts taking into account various realistic restrictions and targets of the production. The effectiveness and the usefulness of the proposed approach are demonstrated for several target performances together with a comparison between optimisation methods for the optimal tuning of the cure cycle.     

Graph partitioning strategy for the topology design of industrial network

Network topology design problem in industrial network is formulated, which is shown to be equivalent to a multi-constraint optimisation problem: the network design should minimise the amount of inter-network communication, and simultaneously balance the communication load and network size evenly over the resultant sub-networks. To solve this optimisation problem, a graph partitioning strategy is proposed, which can give a good network design by partitioning a graph- based representation of the network optimisation problem. Then, the network optimisation procedures using the graph partitioning strategy are detailed and two experimental, examples are studied. In the experiments, the network designs obtained by the graph partitioning strategy are compared with those obtained by a random partitioning method. The experimental results demonstrate the network designs obtained by the graph partitioning strategy are significantly better than those obtained by the random partitioning method.     

用网络技术与遗传算法优选加工工艺过程

在CAPP中一个重要的环节就是加工工艺过程的选择,本文采用网络技术和遗传算法获得最优的工艺路线。     

Genetic algorithms for symbolic clustering

This paper introduces a novel methodology for clustering of symbolic objects by making use of Genetic Algorithms (GAs). GAs are a family of computational models inspired by evolution. These algorithms encode potential solutions to specific problems on simple chromosome-like data structures and apply recombination operators to these structures so as to preserve critical information. A new type of representation for chromosome structure is presented here along with a new method for mutation. The efficacy of the proposed method is examined by application to numeric data of known number of classes and also to assertion type of symbolic objects drawn from the domain of fat oil, microcomputers, microprocessors and botany. The validity of the clusters obtained is examined.     

橡胶悬置元件结构参数优化设计方法

由于橡胶悬置元件的结构比较复杂,截面不规则,无法用传统的优化方法对其结构优化.利用遗传算法和神经网络相结合的策略对橡胶悬置元件的几何结构参数进行优化,即用神经网络学习算法建立橡胶悬置元件几何结构参数与其三个方向刚度的非线性全局映射关系,获得遗传算法求解结构优化问题所需的目标函数,用遗传算法进行优胜劣汰的寻优搜索运算,求出最优解.优化结果表明,橡胶悬置元件结构参数优化设计方法是可行的.     

Development of a post-flood road maintenance strategy: case study Queensland,Australia

Currently, no road authority takes into account flooding in road deterioration (RD) models; as a result, post-flood rehabilitation treatments may be sub-optimal. This paper proposes a new approach to the development of a post-flood maintenance strategy. The recently developed roughness and rutting-based RD models with flooding, by the current authors, are used as input to predict pavement deterioration after a flood (i.e. assuming a flood in year 1). The HDM-4 model has been used to get the post-flood maintenance strategy with constrained and unconstrained budget, where post-flood rehabilitation starts from year 2. The road groups in state road network of Queensland, Australia, are used as the case study. The unconstrained budget solution aims to keep the network in an excellent condition at a cost of $49.7bn with the possible strongest treatments. The constrained budget strategy uses agency cost and pavement performance as constraints in optimisation and provides a reasonable solution. This strategy requires about $26.1bn in life cycle, which is close to the main road authority of Queensland’s post-flood rehabilitation programme. The paper discusses two other strategies on maximise economic benefits and budget optimisation. It is expected that a road authority would properly investigate its flood-damaged roads before implementation. The paper shows pavement performances with the post-flood strategy. The need for a RD model to predict deterioration after a flood and for post-flood treatment selection is also highlighted.     

Wavelet neural networks for modelling high pressure inactivation kinetics of Listeria monocytogenes in UHT whole milk

The aim of the present work was to investigate the applicability of a Wavelet Neural Network to describe the inactivation pattern of Listeria monocytogenes by high hydrostatic pressure in ultra high temperature (UHT) whole milk, and evaluate its performance against models used in predictive microbiology such as the re-parameterized Gompertz and modified Weibull equations. A comparative study with linear partial least squares regression (PLS-R) as well as neural network (NN) models demonstrated on the same dataset has been also considered. Milk was artificially inoculated with an initial population of the pathogen of ca. 10⁷ CFU/ml and exposed to a range of high pressures (350, 450, 550, 600 MPa) for up to 40 min at ambient temperature (ca. 25 °C). Typical survival curves were obtained including a shoulder, a log-linear and a tailing phase. Increasing the magnitude of the applied pressure resulted in increasing levels of inactivation. Modelling approaches provided good fit to experimental training data as inferred by the low values of the root mean squared error (RMSE) and the high values of regression coefficient (R²). Models were validated at 400 and 500 MPa with independent experimental data. First or second order polynomial models were employed to relate the inactivation parameters to pressure, whereas the wavelet network as well as the PLS and NN models were utilised as a one-step modelling approach. The prediction performance of the proposed learning-based network was better at both validation pressures. The development of accurate models to describe the survival curves of micro-organisms in high pressure treatment would be very important to the food industry for process optimisation, food safety and would eventually expand the applicability of this non-thermal process.     

Mathematical modelling and a meta-heuristic for flexible job shop scheduling

This study develops new solution methodologies for the flexible job shop scheduling problem (F-JSSP). As a first step towards dealing with this complex problem, mathematical modellings have been used; two novel effective position- and sequence-based mixed integer linear programming (MILP) models have been developed to fully characterise operations of the shop floor. The developed MILP models are capable of solving both partially and totally F-JSSPs. Size complexities, solution effectiveness and computational efficiencies of the developed MILPs are numerically explored and comprehensively compared vis-à-vis the makespan optimisation criterion. The acquired results demonstrate that the proposed MILPs, by virtue of its structural efficiencies, outperform the state-of-the-art MILPs in literature. The F-JSSP is strongly NP-hard; hence, it renders even the developed enhanced MILPs inefficient in generating schedules with the desired quality for industrial scale problems. Thus, a meta-heuristic that is a hybrid of Artificial Immune and Simulated Annealing (AISA) Algorithms has been proposed and developed for larger instances of the F-JSSP. Optimality gap is measured through comparison of AISA’s suboptimal solutions with its MILP exact optimal counterparts obtained for small- to medium-size benchmarks of F-JSSP. The AISA’s results were examined further by comparing them with seven of the best-performing meta-heuristics applied to the same benchmark. The performed comparative analysis demonstrated the superiority of the developed AISA algorithm. An industrial problem in a mould- and die-making shop was used for verification.     

Hazardous materials transportation: a risk-analysis-based routing methodology

This paper introduces a new methodology based on risk analysis for the selection of the best route for the transport of a hazardous substance. In order to perform this optimisation, the network is considered as a graph composed by nodes and arcs; each arc is assigned a cost per unit vehicle travelling on it and a vehicle capacity. After short discussion about risk measures suitable for linear risk sources, the arc capacities are introduced by comparison between the societal and individual risk measures of each arc with hazardous materials transportation risk criteria; then arc costs are defined in order to take into account both transportation out-of-pocket expenses and risk-related costs. The optimisation problem can thus be formulated as a 'minimum cost flow problem', which consists of determining for a specific hazardous substance the cheapest flow distribution, honouring the arc capacities, from the origin nodes to the destination nodes. The main features of the optimisation procedure, implemented on the computer code OPTIPATH, are presented. Test results about shipments of ammonia are discussed and finally further research developments are proposed.     

Identification of an extended Bouc–Wen model with application to seismic protection through hysteretic devices

In this paper is proposed an extended Bouc–Wen model for improving its capability to approximate experimental symmetric hysteretic loops. On the basis of the generalized equation there are defined integral and differential conditions that describe the essential geometric properties of a hysteretic curve. Next, a new method based on Genetic Algorithms is developed to identify the Bouc–Wen model parameters from experimental hysteretic loops obtained from periodic loading tests. The performance of presented approach is illustrated for two types of seismic protection devices with hysteretic characteristics: elastomeric base isolators and buckling restrained dissipative braces. The applicability of proposed method is highlighted by using the derived models to analyse by numerical simulation the efficiency of these devices for reducing seismic response of a three stories civil structure.     

一种基于的遣传算法的微波测量分析方法

论述了遗传算法在微波测量分析中的应用,首先简要介绍了应用遗传算法处理微波数据信号．然后在微波电路的分析设计中,设计了一种较好的遗传算法多目标优化方法对电路进行全局优化．使整个系统在电路设计等关键的衡量指标同时达到最优．文章对双波段、六单元接换阵列智能天线运用遗传算法进行优化设计,在900MHz和1900MHz两个频段上-10dB带宽分别达到和,天线辐射方向上的最大增益为．最小增益为,并且得到了天线阵列中每个变量的灵敏度分析．通过与文献的比较,结果证明遗传算法的应用可以显著的提高天线的性能．     

Many-objective harmony search for integrated order planning in steelmaking-continuous casting-hot rolling production of multi-plants

This paper investigates a challenging problem of integrated order planning (IOP) in steelmaking-continuous casting-hot rolling production of multiple plants with consideration of four conflicting objectives. The objective functions refer to the earliness/tardiness ratio, the non-hot charge ratio and the imbalance ratio of production capacity utilisation corresponding to SCC plants and HR Plants. The IOP guided by the integration strategy, which includes the vertical integration of production stages and the horizontal integration of steel plants, is regarded as a large-scale many-objective optimisation problem. To deal with the difficulty of large-scale decision variables, we introduce a new concept named ‘order-set’ for modelling. In addition, a novel knee point-driven many-objective global-best harmony search (KGHS) algorithm, mainly integrating a KGHS process and a new knee point-driven Pareto optimisation, is developed to tackle this many-objective problem. The proposed model and algorithm were tested with benchmarks and real production data. Experiments demonstrate that the proposed approach generates effective solutions superior to those generated by the other popular many-objective optimisation methods.     

Recursive genetic algorithm-finite element method technique for the solution of transformer manufacturing cost minimisation problem

The transformer manufacturing cost minimisation (TMCM), also known as transformer design optimisation, is a complex constrained mixed-integer non-linear programming problem with discontinuous objective function. This paper proposes an innovative method combining genetic algorithm (GA) and finite element method (FEM) for the solution of TMCM problem. The main contributions of the proposed method are: (a) introduction of an innovative recursive GA with a novel external elitism strategy associated with variable crossover and mutation rates resulting in an improved GA, (b) adoption of two particular finite element models of increased accuracy and high computational speed for the validation of the optimal design by computing the no-load loss and impedance and (c) combination of the innovative recursive GA with the two particular finite element models resulting in a proposed GA-FEM model that finds the global optimum, as concluded after several tests on actual transformer designs, while other existing methods provided suboptimal solutions that are 3.1?5.8% more expensive than the optimal solution.     

混合智能技术在激光淬火工艺优化中的应用

为探索激光淬火工艺优化设计的新方法,以GCr15激光淬火为例,首先建立神经网络模块,以提取激光淬火(多)工艺参数与(多)性能指标之间的函数映射关系,然后建立遗传算法模块,通过遗传算法的进化搜索来实现激光淬火工艺参数的优化设计.实验结果表明,本研究建立的工艺优化设计模型具有较好的可靠性;将神经网络与遗传算法的混合智能技术引入激光淬火领域,为解决激光淬火工艺优化设计问题提供了一条先进、合理的途径.     

Calibration of adhesion models based on Bayesian inference

The use of composite materials in a myriad of applications fostered the development of reliable procedures to connect components with adhesives. This led to a demand for reliable adhesion models to be used in engineering designs that are based on computer simulations. This paper presents a strategy to be used for calibration of adhesion models. The proposed methodology is built on the formalism of Statistical Inverse Problems. Uncertainties about the unknowns are inferred using Population-Based Markov Chain Monte Carlo and Adaptive Metropolis. It is proposed to perform model assessments based on the analysis of a validation metric. Realizations of the validation metric are computed with the posterior densities of model parameters that are provided by the calibration process. The analysis of the validation metric allows for model selection to be performed. Some numerical experiments are presented with noise-contaminated data. The calibration strategy proved effective when dealing with both the nonlinearity and nondifferentiability of the adhesion constitutive equation.     

Designing spatially heterogeneous strategies for control of virus spread

The spread of a virus--whether in a human population, computer network or cell-to-cell--is closely tied to the spatial (graph) topology of the interactions among the possible infectives. The authors study the problem of allocating limited control resources (e.g. quarantine or recovery resources) in these networks in a way that exploits the topological structure, so as to maximise the speed at which the virus is eliminated. For both multi-group and contact-network models for spread, these problems can be abstracted to a particular decentralised control problem for which the goal is to minimise the dominant eigenvalue of a system matrix. Explicit solutions to these problems are provided, using eigenvalue sensitivity ideas together with constrained optimisation methods employing Lagrange multipliers. The proposed design method shows that the optimal strategy is to allocate resources so as to equalise the propagation impact of each network component, as best as possible within the constraints on the resource. Finally, we show that this decentralised control approach can provide significant advantage over a homogeneous control strategy, in the context of a model for SARS transmission in Hong Kong.     

Single-solution Simulated Kalman Filter algorithm for global optimisation problems

This paper introduces single-solution Simulated Kalman Filter (ssSKF), a new single-agent optimisation algorithm inspired by Kalman Filter, for solving real-valued numerical optimisation problems. In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. In the proposed ssSKF algorithm, the initialisation parameters are not constants, but they are produced by random numbers taken from a normal distribution in the range of [0, 1], thus excluding them from tuning requirement. In order to balance between the exploration and exploitation in ssSKF, the proposed algorithm uses an adaptive neighbourhood mechanism during its prediction step. The proposed ssSKF algorithm is tested using the 30 benchmark functions of CEC 2014, and its performance is compared to that of the original SKF algorithm, Black Hole (BH) algorithm, Particle Swarm Optimisation (PSO) algorithm, Grey Wolf Optimiser (GWO) algorithm and Genetic Algorithm (GA). The results show that the proposed ssSKF algorithm is a promising approach and able to outperform GWO and GA algorithms, significantly.     

A methodology of integrating affective design with defining engineering specifications for product design

Affective design and the determination of engineering specifications are commonly conducted separately in early product design stage. Generally, designers and engineers are required to determine the settings of design attributes (for affective design) and engineering requirements (for engineering design), respectively, for new products. Some design attributes and some engineering requirements could be common. However, the settings of the design attributes and engineering requirements could be different because of the separation of the two processes. In previous studies, a methodology that considers the determination of the settings of the design attributes and engineering requirements simultaneously was not found. To bridge this gap, a methodology for considering affective design and the determination of engineering specifications of a new product simultaneously is proposed. The proposed methodology mainly involves generation of customer satisfaction models, formulation of a multi-objective optimisation model and its solving using a chaos-based NSGA-II. To illustrate and validate the proposed methodology, a case study of mobile phone design was conducted. A validation test was conducted and the test results showed that the customer satisfaction values obtained based on the proposed methodology were higher than those obtained based on the combined standalone quality function deployment and standalone affective design approach.