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.     

Dynamic selective pressure using hybrid evolutionary and ant system strategies for structural optimization

Genetic algorithms have already been applied to various fields of engineering problems as a general optimization tool in charge of expensive sampling of the coded design space. In order to reduce such a computational cost in practice, application of evolutionary strategies is growing rapidly in the adaptive use of problem‐specific information. This paper proposes a hybrid strategy to utilize a cooperative dynamic memory of more competitive solutions combining indirect information share in ant systems with direct constructive genetic search. Some proper coding techniques are employed to enable testing the method with various sets of control parameters. As a challenging field of interest, its application to structural layout optimization is considered while an example of a traveling salesman problem is also treated as a combinatorial benchmark. Copyright © 2007 John Wiley & Sons, Ltd.     

遗传算法用于电子枪发射系统的全局最优化设计

常用的优化设计方法 ,如单纯形法、Powell法等 ,易陷入局部最优解。而遗传算法是一种新兴的直接搜索最优化算法 ,它模拟达尔文遗传选择与自然进化的理论 ,根据“适者生存”和“优胜劣汰”的原则 ,借助“复制”、“交换”、“突变”等操作可以得到全局最优解。本文将遗传算法运用于电子枪发射系统的最优化设计 ,得到了使交叠点半径尽可能小的发射系统的最佳结构和相应电参量     

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.     

An adaptive penalty scheme to solve constrained structural optimization problems by a Craziness based Particle Swarm Optimization

The use of Evolutionary Algorithms (EAs) to solve optimization problems has been increasing. One of the most used techniques is Particle Swarm Optimization (PSO), which is considered robust, efficient and competitive in comparison with other bio-inspired algorithms. EAs were originally designed to solve unconstrained optimization problems. However, the most significant problems, particularly those from real world optimization, present constraints. It is not trivial to define a strategy to handle constraints and, in general, penalty functions containing parameters to be set by the user and it may affect the search considerably. This paper consists of a combination of the Craziness based Particle Swarm Optimization (CRPSO) with an adaptive penalty technique, called Adaptive Penalty Method (APM), to solve constrained optimization problems. A CRPSO is adopted here in order to avoid premature convergence using a new velocity expression and an operator called “craziness velocity”. APM and its variants were applied in other EAs, originally in a Genetic Algorithm, which demonstrated its robustness. APM deals with inequality and equality constraints, and it is free of parameters to be defined by the user. In order to assess the applicability and performance of the algorithm, several structural engineering optimization problems traditionally found in the literature are used in the computational experiments.     

Automated generation and analysis of dynamic system designs

This research uses Genetic Algorithms (GA) to suggest new dynamic systems based on topological remapping of system constituents. The bondgraph representation of the dynamic system behavior is evolved by the operators encapsulated in the genetic algorithms to meet the specified design criteria. The resultant evolved graph is assembled by designers with schemes to produce design variants. Behavioral transformation and structural transformation are adopted as strategies to generate design variants that extend beyond the scope of parametric design into innovative design. Behavioral transformation involves changes in the structure of the representation graphs, while maintaining the functions. Structural transformation involves changes in the components and the subsystems represented by the graph fragments. GAs are used to implement the operators of the transformation to search the problem-solution space because GAs are very robust search routines. Further, since the goal is to generate many solutions, genetic speciation is used to diverge the search so as to uncover other desirable solutions. The dynamic systems are modeled using bond graphs. Bond graphs provide a unified approach to the analysis, synthesis and evaluation of dynamic engineering systems. Though the scope of this investigation is limited to systems represented by bond graphs, the domain is wide enough to include many interesting applications like pump systems and vibration isolation systems.     

On the need for the use of error‐controlled finite element analyses in structural shape optimization processes

This work analyzes the influence of the discretization error associated with the finite element (FE) analyses of each design configuration proposed by the structural shape optimization algorithms over the behavior of the algorithm. The paper clearly shows that if FE analyses are not accurate enough, the final solution provided by the optimization algorithm will neither be optimal nor satisfy the constraints. The need for the use of adaptive FE analysis techniques in shape optimum design will be shown. The paper proposes the combination of two strategies to reduce the computational cost related to the use of mesh adaptivity in evolutionary optimization algorithms: (a) the use of an algorithm for the mesh generation by projection of the discretization error, which reduces the computational cost associated with the adaptive FE analysis of each geometrical configuration and (b) the successive increase of the required accuracy of the FE analyses in order to obtain a considerable reduction of the computational cost in the early stages of the optimization process. Copyright © 2011 John Wiley & Sons, Ltd.     

Bouc-Wen模型参数识别的非线性自适应遗传算法和试验验证

Bouc-Wen模型是一种可表征结构及构件在往复荷载作用下的刚度退化、强度退化等的一种多功能非线性光滑滞回模型,可广泛应用于各类结构滞回行为的描述。Bouc-Wen模型参数是决定结构构件滞回模型力学特征的关键,由于该模型参数众多且物理意义不明确,往往只能从滞回数据得到近似解。为适应该类模型参数高效识别的需求,该研究提出了一种非线性自适应遗传算法,并通过4片不同配筋和加载条件的RC剪力墙的低周反复加载试验对Bouc-Wen模型参数识别的效果进行了验证。模型参数识别得到的滞回曲线和算法效率与标准遗传算法识别的结果以及实验数据进行了对比,结果表明:所提出的方法显著提升了Bouc-Wen模型的识别精度与效率。该文所提出的方法可用来进行结构滞回模型的识别并用所识别的模型进行结构的非线性行为模拟。     

Self-adaptive multi-objective harmony search for optimal design of water distribution networks

In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.     

Using genetic algorithms to solve the multi-product JIT sequencing problem with set-ups

This paper presents a methodology to solve the Just-in-Time (JIT) sequencing problem for multiple product scenarios when set-ups between products are required. Problems of this type are combinatorial, and complete enumeration of all possible solutions is computationally prohibitive. Therefore, Genetic Algorithms are often employed to find desirable, although not necessarily optimal, solutions. This research, through experimentation, shows that Genetic Algorithms provide formidable solutions to the multi-product JIT sequencing problem with set-ups. The results also compare favourably to those found using the search techniques of Tabu Search and Simulated Annealing.     

A comparative study of genetic algorithms for the multi-objective optimization of composite stringers under compression loads

Optimization methods are close to become a common task in the design process of many mechanical engineering fields, specially those related with the use of composite materials which offer the flexibility in the design of both the shape and the material properties and so, are very suitable to any optimization process. While nowadays there exist a large number of solution methods for optimization problems there is not much information about which method may be most reliable for a specific problem. Genetic algorithms have been presented as a family of methods which can handle most of engineering problems. However, starting from a common basic set of rules many algorithms which differ slightly from each other have been implemented even in commercial software packages. This work presents a comparative study of three common Genetic Algorithms: Archive-based Micro Genetic Algorithm (AMGA), Neighborhood Cultivation Genetic Algorithm (NCGA) and Non-dominate Sorting Genetic Algorithm II (NSGA-II) considering three different strategies for the initial population. Their performance in terms of solution, computational time and number of generations was compared. The benchmark problem was the optimization of a T-shaped stringer commonly used in CFRP stiffened panels. The objectives of the optimization were to minimize the mass and to maximize the critical buckling load. The comparative study reveals that NSGA-II and AMGA seem the most suitable algorithms for this kind of problem.     

Re-entrant flow shop scheduling problem with time windows using hybrid genetic algorithm based on auto-tuning strategy

The re-entrant flow shop scheduling problem considering time windows constraint is one of the most important problems in hard-disc drive (HDD) manufacturing systems. In order to maximise the system throughput, the problem of minimising the makespan with zero loss is considered. In this paper, evolutionary techniques are proposed to solve the complex re-entrant scheduling problem with time windows constraint in manufacturing HDD devices with lot size. This problem can be formulated as a deterministic Fm?|?fmls, rcrc, temp?|?C_max problem. A hybrid genetic algorithm was used for constructing chromosomes by checking and repairing time window constraints, and improving chromosomes by a left-shift heuristic as a local search algorithm. An adaptive hybrid genetic algorithm was eventually developed to solve this problem by using fuzzy logic control in order to enhance the search ability of the genetic algorithm. Finally, numerical experiments were carried out to demonstrate the efficiency of the developed approaches.     

An adaptive penalty scheme for genetic algorithms in structural optimization

A parameter‐less adaptive penalty scheme for genetic algorithms applied to constrained optimization problems is proposed. Using feedback from the evolutionary process the procedure automatically defines a penalty parameter for each constraint. The user is thus relieved from the burden of having to determine sensitive parameter(s) when dealing with every new constrained optimization problem. The procedure is shown to be effective and robust when applied to test problems from the evolutionary computation literature as well as several optimization problems from the structural engineering literature. Copyright © 2003 John Wiley & Sons, Ltd.     

On a cellular division method for topology optimization

This paper concerns a comparative analysis of a novel biologically inspired method for topology optimization. The proposed methodology develops each individual topology according to a set of rules that regulate a ‘cellular division’ process. These rules are then evolved using a genetic algorithm to minimize objective functions while satisfying a set of constraints. The results reported in this work show that the methodology suits engineering design and represents an improvement over existing topology optimization methods based on evolutionary algorithms. Copyright © 2011 John Wiley & Sons, Ltd.     

Photovoltaic Models Parameters Estimation Based on Weighted Mean of Vectors

Renewable energy sources are gaining popularity, particularly photovoltaic energy as a clean energy source. This is evident in the advancement of scientific research aimed at improving solar cell performance. Due to the non-linear nature of the photovoltaic cell, modeling solar cells and extracting their parameters is one of the most important challenges in this discipline. As a result, the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate. In this paper, a weIghted meaN oF vectOrs algorithm (INFO) that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way. In each generation, the INFO utilizes three operations to update the vectors’ locations: updating rules, vector merging, and local search. The INFO is applied to estimate the parameters of static models such as single and double diodes, as well as dynamic models such as integral and fractional models. The outcomes of all applications are examined and compared to several recent algorithms. As well as the results are evaluated through statistical analysis. The results analyzed supported the proposed algorithm’s efficiency, accuracy, and durability when compared to recent optimization algorithms.     

Solving structural optimization problems with genetic algorithms and simulated annealing

In this paper we study the performance of two stochastic search methods: Genetic Algorithms and Simulated Annealing, applied to the optimization of pin‐jointed steel bar structures. We show that it is possible to embed these two schemes into a single parametric family of algorithms, and that optimal performance (in a parallel machine) is obtained by a hybrid scheme. Examples of applications to the optimization of several real steel bar structures are presented. Copyright © 1999 John Wiley & Sons, Ltd.     

An improved hybrid self-inertia weight adaptive particle swarm optimization algorithm with local search

As an evolutionary computing technique, particle swarm optimization (PSO) has good global search ability, but the swarm can easily lose its diversity, leading to premature convergence. To solve this problem, an improved self-inertia weight adaptive particle swarm optimization algorithm with a gradient-based local search strategy (SIW-APSO-LS) is proposed. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation of the gradient-based local search strategy. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The gradient-based local search focuses on the exploitation ability because it performs an accurate search following SIW-APSO. Experimental results verified that the proposed algorithm performed well compared with other PSO variants on a suite of benchmark optimization functions.     

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.     

Adaptive Poly-FEM for the analysis of plane elasticity problems

In this work, we present an adaptive polygonal finite element method (Poly-FEM) for the analysis of two-dimensional plane elasticity problems. The generation of meshes consisting of n ? sided polygonal finite elements is based on the generation of a centroidal Voronoi tessellation (CVT). An unstructured tessellation of a scattered point set, that minimally covers the proximal space around each point in the point set, is generated whereby the method also includes tessellation of nonconvex domains. In this work, we propose a region by region adaptive polygonal element mesh generation. A patch recovery type of stress smoothing technique that utilizes polygonal element patches for obtaining smooth stresses is proposed for obtaining the smoothed finite element stresses. A recovery type a ? posteriori error estimator that estimates the energy norm of the error from the recovered solution is then adopted for the Poly-FEM. The refinement of the polygonal elements is then made on an region by region basis through a refinement index. For the numerical integration of the Galerkin weak form over polygonal finite element domains, we resort to classical Gaussian quadrature applied to triangular subdomains of each polygonal element. Numerical examples of two-dimensional plane elasticity problems are presented to demonstrate the efficiency of the proposed adaptive Poly-FEM.     

Binocular self-calibration performed via adaptive genetic algorithm based on laser line imaging

Abstract

An accurate technique to perform binocular self-calibration by means of an adaptive genetic algorithm based on a laser line is presented. In this calibration, the genetic algorithm computes the vision parameters through simulated binary crossover (SBX). To carry it out, the genetic algorithm constructs an objective function from the binocular geometry of the laser line projection. Then, the SBX minimizes the objective function via chromosomes recombination. In this algorithm, the adaptive procedure determines the search space via line position to obtain the minimum convergence. Thus, the chromosomes of vision parameters provide the minimization. The approach of the proposed adaptive genetic algorithm is to calibrate and recalibrate the binocular setup without references and physical measurements. This procedure leads to improve the traditional genetic algorithms, which calibrate the vision parameters by means of references and an unknown search space. It is because the proposed adaptive algorithm avoids errors produced by the missing of references. Additionally, the three-dimensional vision is carried out based on the laser line position and vision parameters. The contribution of the proposed algorithm is corroborated by an evaluation of accuracy of binocular calibration, which is performed via traditional genetic algorithms.