Selection of appropriate multilayered plate theories by using a genetic like algorithm

This paper evaluates a number of classical and refined two-dimensional theories for the analysis of metallic and composite layered plates. Thin-plate, shear deformation and higher order plate theories are compared for various plate problems related to different mechanical and geometrical boundary conditions (BCs), as well as geometries and staking sequence lay-out. The theories are implemented by referring to a Unified Formulation (UF) proposed by the first author. The UF allows displacement fields with any order N in the thickness plate direction to be introduced and any variables in the N-order displacement field to be discarded. The finite element method is applied to include anisotropy and complex BCs. The accuracy of given theories for each fixed problem is established in terms of displacement and stress fields. The best plate theories, that is the most accurate plate theories with few computational efforts, is then determined by exploring various possibilities and by selecting appropriate unknown variables upon application of genetic algorithms. A best plate curve is established which shows the best plate theories (number of terms and their meanings) in terms of accuracy. It is concluded that a best plate theory changes with changing geometry, lay-out and BCs. The genetic algorithm used allows the least expensive computational model of each given problem to be detected.     

Part family identification using a simple genetic algorithm

Past research in part family identification has focused mainly on the development of efficient procedures for manufacturing-oriented part family formation in which similarities among parts are established primarily on machine or operation requirements. While these part families are essential in cellular manufacturing, they are not well suited for other areas of production, in particular, part design and process planning. A new part family identification technique using a simple genetic algorithm is proposed in this paper to first determine a set of part family differentiating attributes, and second to use these attributes to guide the formation of part families. The technique is implemented in C using a SUN SPARC workstation 1+. Empirical analyses of the technique on both artificially generated data and a real application are performed and discussed.     

Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm

This paper proposes a genetic algorithm (GA) for a redundancy allocation problem for the series-parallel system when the redundancy strategy can be chosen for individual subsystems. Majority of the solution methods for the general redundancy allocation problems assume that the redundancy strategy for each subsystem is predetermined and fixed. In general, active redundancy has received more attention in the past. However, in practice both active and cold-standby redundancies may be used within a particular system design and the choice of the redundancy strategy becomes an additional decision variable. Thus, the problem is to select the best redundancy strategy, component, and redundancy level for each subsystem in order to maximize the system reliability under system-level constraints. This belongs to the NP-hard class of problems. Due to its complexity, it is so difficult to optimally solve such a problem by using traditional optimization tools. It is demonstrated in this paper that GA is an efficient method for solving this type of problems. Finally, computational results for a typical scenario are presented and the robustness of the proposed algorithm is discussed.     

Price negotiation for capacity sharing in a two-factory environment using genetic algorithm

Uncertain and lumpy demand forces capacity planners to maximize the profit of individual factory by simultaneously taking advantage of outsourcing to and/or being outsourced from its supply chain and even competitors. This study develops a resource-planning model of a large manufacturer with two profit-centered factories. The proposed model enables a collaborative integration for resource and demand sharing which is highly attractive to the high-tech industries against the challenges of short product life cycle, intensive capital investment and decreasing marginal profit. Each of the individual factories applies an economic resource-planning model and a genetic algorithm to improve its objective while purchasing extra capacity requirement from its peer factory or selling extra capacity of resources to the others through a negotiation algorithm. This study makes a contribution in successfully building a mutual negotiation model for a set of customer tasks to be realized by the negotiating parties, each with private information regarding company objectives, cost and price. Experimental results reveal that near-optimal solutions for both of the isolated (a single factory) and negotiation-based (between two factories) environments are obtained.     

A new hybrid real-coded genetic algorithm and its application to parameters identification of soils

Inverse analysis using an optimization method based on a genetic algorithm (GA) is a useful tool for obtaining soil parameters in geotechnical fields. However, the performance of the optimization in identifying soil parameters mainly depends on the search ability of the GA used. This study aims to develop a new efficient hybrid real-coded genetic algorithm (RCGA) being applied to identify parameters of soils. In this new RCGA, a new hybrid strategy is proposed by adopting two crossovers with outstanding ability, namely the Simulated Binary Crossover and the simplex crossover. In order to increase the convergence speed, a chaotic local search technique is used conditionally. The performance of the proposed RCGA is first validated by optimizing mathematical benchmark functions. The results demonstrate that the RCGA has an outstanding search ability and faster convergence speed compared to other hybrid RCGAs. The proposed new hybrid RCGA is then further evaluated by identifying soil parameters based on both laboratory tests and field tests, for different soil models. All the comparisons demonstrate that the proposed RCGA has an excellent performance of inverse analysis in identifying soil parameters, and is thus recommended for use based on all the evaluations carried out in this paper.     

Optimisation of fibre steering in composite laminates using a genetic algorithm

An optimization procedure using a genetic algorithm has been applied to define the optimum orientation of fibres in a uni-directional laminate in which the fibres were allowed to vary continuously across the domain. The domain was divided into two-dimensional finite elements and anisotropic properties corresponding to a carbon fibre laminate with all layers aligned in the zero element axis direction were applied to the laminate. The orientation of the material axis on each element was then prescribed as an independent variable for the genetic algorithm.     

Pareto optimization using the struggle genetic crowding algorithm

Many real-world engineering design problems involve the simultaneous optimization of several conflicting objectives. In this paper, a method combining the struggle genetic crowding algorithm with Pareto-based population ranking is proposed to elicit trade-off frontiers. The new method has been tested on a variety of published problems, reliably locating both discontinuous Pareto frontiers as well as multiple Pareto frontiers in multi-modal search spaces. Other published multi-objective genetic algorithms are less robust in locating both global and local Pareto frontiers in a single optimization. For example, in a multi-modal test problem a previously published non-dominated sorting GA (NSGA) located the global Pareto frontier in 41% of the optimizations, while the proposed method located both global and local frontiers in all test runs. Additionally, the algorithm requires little problem specific tuning of parameters.     

Structural damage identification based on modified Artificial Bee Colony algorithm using modal data

A method for structural damage identification based on a modified Artificial Bee Colony algorithm is presented. A new formula is introduced to the onlooker bee phase to improve the convergence rate and the Tournament Selection Strategy is adopted instead of roulette to enhance global search ability of the algorithm. Test functions are introduced as benchmarks to verify the proposed algorithm. And then two numerical examples, including a supported beam and a plate, are conducted to investigate the efficiency and correctness of the proposed method. Final estimated results show that the present technique can produce more accurate damage identification results, comparing with other evolutionary algorithms, even with a few noise contaminated measurements.     

Improved artificial bee colony algorithm for crack identification in beam using natural frequencies only

An improved artificial bee colony algorithm (I-ABC) is proposed for crack identification in beam structures. ABC is a heuristic algorithm and swarm technique with simple structure, which is easy to implement but with slow convergence rate. In the I-ABC, the differential evolution (DE) mechanism is introduced to employed bee phase, roulette selection strategy is replaced by tournament selection strategy and a new formula is used to simulate onlooker bee’s behaviour. A discrete open crack is used for vibration analysis of the cracked beam and only the changes in the first few natural frequencies are utilized to establish the objective function of the optimization problem for crack identification. A numerical simulation and an experimental work are studied to illustrate the efficiency of the proposed method. Studies show that the present techniques can produce more accurate damage identification results when compared with original ABC, DE algorithm, particle swarm optimization and genetic algorithm.     

Improved multilayer OLED architecture using evolutionary genetic algorithm

Organic light-emitting diodes (OLEDs) constitute a new class of emissive devices, which present high efficiency and low voltage operation, among other advantages over current technology. Multilayer architecture (M-OLED) is generally used to optimize these devices, specially overcoming the suppression of light emission due to the exciton recombination near the metal layers. However, improvement in recombination, transport and charge injection can also be achieved by blending electron and hole transporting layers into the same one. Graded emissive region devices can provide promising results regarding quantum and power efficiency and brightness, as well. The massive number of possible model configurations, however, suggests that a search algorithm would be more suitable for this matter. In this work, multilayer OLEDs were simulated and fabricated using Genetic Algorithms (GAs) as evolutionary strategy to improve their efficiency. Genetic Algorithms are stochastic algorithms based on genetic inheritance and Darwinian strife to survival. In our simulations, it was assumed a 50 nm width graded region, divided into five equally sized layers. The relative concentrations of the materials within each layer were optimized to obtain the lower V/J^0.5 ratio, where V is the applied voltage and J the current density. The best M-OLED architecture obtained by genetic algorithm presented a V/J^0.5 ratio nearly 7% lower than the value reported in the literature. In order to check the experimental validity of the improved results obtained in the simulations, two M-OLEDs with different architectures were fabricated by thermal deposition in high vacuum environment. The results of the comparison between simulation and some experiments are presented and discussed.     

Efficient optimization of process parameters in shadow mask manufacturing using NNPLS and genetic algorithm

A shadow mask, the primary component of a cathode ray tube (CRT), is used to prevent the outer edges of electron beams from hitting incorrect phosphor dots. It is fabricated by means of a photo-etching process consisting of a few hundred/thousand process parameters. A primary concern in the management of the process is to determine the optimal process parameter settings necessary to sustain the desired levels of product quality. The characteristics of the process, including a large number of process parameters and collinear observed data, make it difficult to accomplish the primary concern. To cope with the difficulties, a two-phase approach is employed that entails the identification of a few critical process parameters, followed by determination of the optimal parameter settings. The former is obtained through the operator's domain knowledge and the NNPLS-based prediction model built between process parameters and quality defects. The latter is obtained by solving an optimization problem using a genetic algorithm (GA). A comparative study shows that the proposed approach improves product quality greatly in the shadow-mask manufacturing process.     

Multi-objective optimal design of sandwich panels using a genetic algorithm

In this study, an optimization problem concerning sandwich panels is investigated by simultaneously considering the two objectives of minimizing the panel mass and maximizing the sound insulation performance. First of all, the acoustic model of sandwich panels is discussed, which provides a foundation to model the acoustic objective function. Then the optimization problem is formulated as a bi-objective programming model, and a solution algorithm based on the non-dominated sorting genetic algorithm II (NSGA-II) is provided to solve the proposed model. Finally, taking an example of a sandwich panel that is expected to be used as an automotive roof panel, numerical experiments are carried out to verify the effectiveness of the proposed model and solution algorithm. Numerical results demonstrate in detail how the core material, geometric constraints and mechanical constraints impact the optimal designs of sandwich panels.     

基于模型参考自适应算法的非线性结构损伤识别

许多工程结构在遭受飓风、地震之类的动荷载时,存在迟滞非线性现象,因此,对其结构参数进行实时辨识显得尤为必要.针对迟滞非线性结构的力学性质,采用一种基于模型参考自适应算法的参数辨识方法.该方法能够追踪时变的参数,检测损伤,包括损伤的大小和位置,以及损伤发生的时刻.数值仿真的结果表明,该方法对参数变化具有敏感性,非线性结构的仿真结果证实了该方法能够有效检测结构的损伤.     

Simultaneous variable selection and outlier detection using a robust genetic algorithm

Given a dataset in which it is known that all spectra are representative, without error, and have matching accurate reference values, there are many tools which exist to determine the best set of variables to use for constructing an inverse model, such as partial least squares (PLS). Likewise, given that the best variables are known a priori, there are many tools that can be used to determine if any samples are outliers, either due to inaccurate reference values, or due to invalid spectra. However, in many real-world situations, the reference values contain error and the spectra are imperfect. In this situation, it is not always possible to determine either the best subset of samples or the best subset of variables. This paper presents a new technique for combining a robust outlier determination method with a genetic algorithm optimized for spectral variable selection. No assumptions are made as to the optimum set of variables or as to the amount and structure of the errors present in either the predictor (X) or predictand (Y) variables. The technique is best suited for datasets which contain redundant information, i.e., datasets from designed experiments with no replicates may not produce optimum results, as the experimental design implicitly assumes there are no outlier data.     

Decomposition-based design optimization method using genetic co-evolution

The paper examines new strategies for the implementation of genetic algorithms in a decomposition-based approach for design. In this approach, the design problem is decomposed into smaller-sized sub-problems, the solutions for which are obtained through co-evolution. The emphasis resides in evaluating methods for exchanging design information relevant to coordinating solutions of temporarily decoupled sub-problems. Methods based on modification of genetic makeup through experiential inheritance (exposure to another species), and through inter-species migration are investigated in this work. Different forms of design problem coupling are investigated, ranging from coupling through constraints only, to coupled objective and constraint functions. The proposed strategies are validated through implementation in representative algebraic and structural design problems.     

Determination of the optimal part orientation in layered manufacturing using a genetic algorithm

Several important factors must be taken into consideration to maximise the efficiency of rapid prototyping (RP) processes. The ability to select the optimal orientation of a build direction is one of the most critical factors in using RP processes, since it affects the quality of the prototyped part, the support structure and the build time. This study aims to determine the optimal part orientation that improves the average weighted surface roughness (AWSR) generated from the stair stepping effect. It also minimises the build time including the structure of the support in fabricating a completely freeform part. To avoid pre-selection operation, which is often troublesome and time-consuming, the genetic algorithm, that considers the fuzzy weight for surface roughness and build time, is used to determine the optimal orientation. The validity of the proposed algorithm is demonstrated by several examples using different RP systems and compared with previous works. The algorithm can help RP users select the best orientation of the part and carry out efficient process planning.     

Truss optimization on shape and sizing with frequency constraints based on genetic algorithm

Truss optimization on shape and sizing with frequency constraints are highly nonlinear dynamic optimization problems. Coupling of two different types of design variables, nodal coordinates and cross-sectional areas, often lead to divergence while multiple frequency constraints often cause difficult dynamic sensitivity analysis. So optimal criteria method and mathematical programming, which need complex dynamic sensitivity and are easily trapped into the local optima, are difficult to solve the problems. To solve the truss shape and sizing optimization simply and effectively, a Niche Hybrid Genetic Algorithm (NHGA) is proposed. The objective of NHGA is to enhance the exploitation capacities while preventing the premature convergence simultaneously based on the new hybrid architecture. Niche techniques and adaptive parameter adjustment are used to maintain population diversity for preventing the premature convergence while simplex search is used to enhance the local search capacities of GAs. The proposed algorithm effectively alleviates premature convergence and improves weak exploitation capacities of GAs. Several typical truss optimization examples are employed to demonstrate the validity, availability and reliability of NHGA for solving shape and sizing optimization of trusses with multiple frequency constraints.     

Solving a fixture configuration design problem using genetic algorithm with learning automata approach

Proper fixture design is crucial to workpiece quality assurance in manufacturing. Incorrect fixture design may lead to workpiece deformation during machining. The fixture configuration design is one of the important aspects of fixture design. This paper deals with fixture layout optimization problem. The objective is to minimize the norm of all the passive contact forces satisfying Coulomb friction constraint, work-piece static equilibrium constraint and contact constraint, for the entire cutting operation. To solve this problem, the paper proposes Genetic Algorithm with Learning Automata (GALA) algorithm, which is a population based interconnected learning automata algorithm incorporating genetic operators. The algorithm enjoys the good characteristics of both GA and LA. It is validated with an example of face milling operation. The optimal layout is found to be in tune with empirical facts. Also, for the further investigation of the algorithm, it has been tested on a different problem sets and a comparative study is carried out.     

Optimization of composite sandwich panel against impact using genetic algorithm

The present paper deals with the optimization of composite sandwich panels subjected to low-velocity impact using advanced genetic algorithm (GA). Equivalent multi degree-of-freedom system is used to predict the contact force history and deformation response of the sandwich panels. Also, analytical solutions are used to determine the impact force and velocity at damage initiation. The GA's objective is to maximize the strength of panel by varying the ply angles of the facesheets. The results show that the stacking sequence of facesheets plays an important role in the strength of the composite sandwich panels subjected to impact loading.     

Crack identification of beam structures using homotopy continuation algorithm

An approach based on homotopy continuation algorithm is presented to identify the parameters of a cracked beam. Euler–Bernoulli finite beam element with a fully opened crack model is adopted to establish the dynamic equation of the structural system. In the inverse problem, the homotopy equation is derived from minimizing the error between the calculated and the simulated measured acceleration responses. The range of homotopy parameter is divided into a number of divisions. Newton iterative method is employed to estimate the solution at each of these division points. The solution at the last division point corresponds to the homotopy equation matching the objective function. Numerical simulations with a simply supported beam and two-span beam show that the proposed method is very accurate compared to an existing method for both single and multiple cracks identification. The effects of type of excitation, division of the homotopy parameter and measurement noise on the identified results are discussed. It is noted that there is no need for an accurate set of initial values with the proposed approach.