基于模型的位姿估计中优化方法研究

介绍了基于模型的位姿估计中所使用的一些优化方法。为了提高位姿估计的精度,摄像机的标定参数必须足够精确,这就对标定过程的非线性优化算法提出了很高的要求,采用了一种新的优化目标函数,用来最小化控制点间的三维重建误差,从而使标定参数是全局最优;在双像机位姿估计中,引入了实时遗传算法进行全局搜索,加快了算法的收敛速度。最后的实验证明了这些方法的正确性并显示出这些方法在精度上比传统方法有了较大程度的提高。

一种基于遗传线性搜索算法的关节手模型匹配方法

提出了一种可替代传统搜索算法的改进型模型匹配算法 这种算法将遗传算法 (GeneticAlgorithm ,GA)和经典的线性搜索算法 (LineSearch ingAlgorithm ,LSA)相结合 它能保证匹配解是全局最优的并且运算是近乎实时的

一种欠驱动AUV的三维实时避碰方法

针对以测距声纳为避碰传感器的一类欠驱动型AUV，提出了一种水平面和垂直面相结合的三维实时避碰方法。根据测距声纳和欠驱动AUV 的特殊性，首先从运动规划和路径规划2 个层次提出了AUV 混合型实时避碰结构，并分别设计了基于事件反馈监控的避碰自动机和基于免疫遗传的局部路径规划算法。多种典型障碍场景的半物理仿真实验表明，论文所提方法能够实现AUV 安全、稳定的三维避碰过程。

一种改进的遗传算法

针对传统遗传算法在编码方案及交叉操作中存在的局限性，提出了一种新的遗传算法的改进 方法．该方法（１）以实数编码代替二进制编码，有效地解决了传统遗传算法中二进制编码串的长度与 计算精度、运算量之间的矛盾，（２）根据适应度值对父染色体进行重组操作，克服了传统遗传算法中 交叉操作所存在的盲目性．最后，以求解自然对数和神经网络的训练为例验证了所提出方法的有效 性．

How Good are Genetic Algorithms at Finding Large Cliques: An Experimental Study

This paper investigates the power of genetic algorithms at solving the MAX-CLIQUE problem. We measure the performance of a standard genetic algorithm on an elementary set of problem instances consisting of embedded cliques in random graphs. We indicate the need for improvement, and introduce a new genetic algorithm, the multi-phase annealed GA, which exhibits superior performance on the same problem set. As we scale up the problem size and test on \hard" benchmark instances, we notice a degraded performance in the algorithm caused by premature convergence to local minima. To alleviate this problem, a sequence of modi cations are implemented ranging from changes in input representation to systematic local search. The most recent version, called union GA, incorporates the features of union cross-over, greedy replacement, and diversity enhancement. It shows a marked speed-up in the number of iterations required to find a given solution, as well as some improvement in the clique size found. We discuss issues related to the SIMD implementation of the genetic algorithms on a Thinking Machines CM-5, which was necessitated by the intrinsically high time complexity (O(n3)) of the serial algorithm for computing one iteration. Our preliminary conclusions are: (1) a genetic algorithm needs to be heavily customized to work "well" for the clique problem; (2) a GA is computationally very expensive, and its use is only recommended if it is known to find larger cliques than other algorithms; (3) although our customization e ort is bringing forth continued improvements, there is no clear evidence, at this time, that a GA will have better success in circumventing local minima.

Modeling the Effects of Habitat Fragmentation on the Movements of the Mongolian Gazelle in the Eastern Mongolian Steppe

The Mongolian gazelle, Procapra gutturosa, resides in the immense and dynamic ecosystem of the Eastern Mongolian Steppe. The Mongolian Steppe ecosystem dynamics, including vegetation availability, change rapidly and dramatically due to unpredictable precipitation patterns. The Mongolian gazelle has adapted to this unpredictable vegetation availability by making long range nomadic movements. However, predicting these movements is challenging and requires a complex model. An accurate model of gazelle movements is needed, as rampant habitat fragmentation due to human development projects - which inhibit gazelles from obtaining essential resources - increasingly threaten this nomadic species. We created a novel model using an Individual-based Neural Network Genetic Algorithm (ING) to predict how habitat fragmentation affects animal movement, using the Mongolian Steppe as a model ecosystem. We used Global Positioning System (GPS) collar data from real gazelles to “train” our model to emulate characteristic patterns of Mongolian gazelle movement behavior. These patterns are: preferred vegetation resources (NDVI), displacement over certain time lags, and proximity to human areas. With this trained model, we then explored how potential scenarios of habitat fragmentation may affect gazelle movement. This model can be used to predict how fragmentation of the Mongolian Steppe may affect the Mongolian gazelle. In addition, this model is novel in that it can be applied to other ecological scenarios, since we designed it in modules that are easily interchanged.

Exploring genetic alternative concepts for FUELGEN

This paper describes new crossover operators and mutation strategies for the FUELGEN system, a genetic algorithm which designs fuel loading patterns for nuclear power reactors. The new components are applications of new ideas from recent research in genetic algorithms. They are designed to improve the performance of FUELGEN by using information in the problem as yet not made explicit in the genetic algorithm's representation. The paper introduces new developments in genetic algorithm design and explains how they motivate the proposed new components.

Parallel genetic algorithms for the solution of inverse heat conduction problems

A parallel genetic algorithm (PGA) is proposed for the solution of two-dimensional inverse heat conduction problems involving unknown thermophysical material properties. Experimental results show that the proposed PGA is a feasible and effective optimization tool for inverse heat conduction problems

Design optimization of cold-formed steel portal frames taking into account the effect of building topology

Cold-formed steel portal frames are a popular form of construction for low-rise commercial, light industrial and agricultural buildings with spans of up to 20 m. In this article, a real-coded genetic algorithm is described that is used to minimize the cost of the main frame of such buildings. The key decision variables considered in this proposed algorithm consist of both the spacing and pitch of the frame as continuous variables, as well as the discrete section sizes.A routine taking the structural analysis and frame design for cold-formed steel sections is embedded into a genetic algorithm. The results show that the real-coded genetic algorithm handles effectively the mixture of design variables, with high robustness and consistency in achieving the optimum solution. All wind load combinations according to Australian code are considered in this research. Results for frames with knee braces are also included, for which the optimization achieved even larger savings in cost.

On the application of genetic algorithms for optimising composites against impact loading

A genetic algorithm (GA) was adopted to optimise the response of a composite laminate subject to impact. Two different impact scenarios are presented: low-velocity impact of a slender laminated strip and high-velocity impact of a rectangular plate by a spherical impactor. In these cases, the GA's objective was to, respectively, minimise the peak deflection and minimise penetration by varying the ply angles.

The GA was coupled to a commercial finite-element (FE) package LS DYNA to perform the impact analyses. A comparison with a commercial optimisation package, LS OPT, was also made. The results showed that the GA was a robust, capable optimisation tool that produced near optimal designs, and performed well with respect to LS OPT for the more complex high-velocity impact scenario tested.

Effect of serviceability limits on optimal design of steel portal frames

The design of hot-rolled steel portal frames can be sensitive to serviceability deflection limits. In such cases, in order to reduce frame deflections, practitioners increase the size of the eaves haunch and / or the sizes of the steel sections used for the column and rafter members of the frame. This paper investigates the effect of such deflection limits using a real-coded niching genetic algorithm (RC-NGA) that optimizes frame weight, taking into account both ultimate as well as serviceability limit states. The results show that the proposed GA is efficient and reliable. Two different sets of serviceability deflection limits are then considered: deflection limits recommended by the Steel Construction Institute (SCI), which is based on control of differential deflections, and other deflection limits based on suggestions by industry. Parametric studies are carried out on frames with spans ranging between 15 m to 50 m and column heights between 5 m to 10 m. It is demonstrated that for a 50 m span frame, use of the SCI recommended deflection limits can lead to frame weights that are around twice as heavy as compared to designs without these limits.

Advanced design optimization of cold-formed steel portal frame buildings

The design optimization of cold-formed steel portal frame buildings is considered in this paper. The objective function is based on the cost of the members for the main frame and secondary members (i.e., purlins, girts, and cladding for walls and roofs) per unit area on the plan of the building. A real-coded niching genetic algorithm is used to minimize the cost of the frame and secondary members that are designed on the basis of ultimate limit state. It iis shown that the proposed algorithm shows effective and robust capacity in generating the optimal solution, owing to the population's diversity being maintained by applying the niching method. In the optimal design, the cost of purlins and side rails are shown to account for 25% of the total cost; the main frame members account for 27% of the total cost, claddings for the walls and roofs accounted for 27% of the total cost.

Dimensioning of multilayer optical networks

Este trabalho apresenta um estudo sobre o dimensionamento de redes ópticas, com vistas a obter um modelo de dimensionamento para redes de transporte sobreviventes. No estudo utilizou-se uma abordagem estatística em detrimento à determinística. Inicialmente, apresentam-se as principais tecnologias e diferentes arquitecturas utilizadas nas redes ópticas de transporte. Bem como os principais esquemas de sobrevivência e modos de transporte. São identificadas variáveis necessárias e apresenta-se um modelo dimensionamento para redes de transporte, tendo-se dado ênfase às redes com topologia em malha e considerando os modos de transporte opaco, transparente e translúcido. É feita uma análise rigorosa das características das topologias de redes de transporte reais, e desenvolve-se um gerador de topologias de redes de transporte, para testar a validade dos modelos desenvolvidos. Também é implementado um algoritmo genético para a obtenção de uma topologia optimizada para um dado tráfego. São propostas expressões para o cálculo de variáveis não determinísticas, nomeadamente, para o número médio de saltos de um pedido, coeficiente de protecção e coeficiente de restauro. Para as duas últimas, também é analisado o impacto do modelo de tráfego. Verifica-se que os resultados obtidos pelas expressões propostas são similares às obtidas por cálculo numérico, e que o modelo de tráfego não influencia significativamente os valores obtidos para os coeficientes. Finalmente, é demonstrado que o modelo proposto é útil para o dimensionamento e cálculo dos custos de capital de redes com informação incompleta.

Optimisation of viscoelastic treatments using genetic algorithms

Viscoelastic treatments are one of the most efficient treatments, as far as passive damping is concerned, particularly in the case of thin and light structures. In this type of treatment, part of the strain energy generated in the viscoelastic material is dissipated to the surroundings, in the form of heat. A layer of viscoelastic material is applied to a structure in an unconstrained or constrained configuration, the latter proving to be the most efficient arrangement. This is due to the fact that the relative movement of both the host and constraining layers cause the viscoelastic material to be subjected to a relatively high strain energy. There are studies, however, that claim that the partial application of the viscoelastic material is just as efficient, in terms of economic costs or any other form of treatment application costs. The application of patches of material in specific and selected areas of the structure, thus minimising the extension of damping material, results in an equally efficient treatment. Since the damping mechanism of a viscoelastic material is based on the dissipation of part of the strain energy, the efficiency of the partial treatment can be correlated to the modal strain energy of the structure. Even though the results obtained with this approach in various studies are considered very satisfactory, an optimisation procedure is deemed necessary. In order to obtain optimum solutions, however, time consuming numerical simulations are required. The optimisation process to use the minimum amount of viscoelastic material is based on an evolutionary geometry re-design and calculation of the modal damping, making this procedure computationally costly. To avert this disadvantage, this study uses adaptive layerwise finite elements and applies Genetic Algorithms in the optimisation process.

Automatic tuning of PID controllers using a neuro-genetic system

Neural networks and genetic algorithms have been in the past successfully applied, separately, to controller turning problems. In this paper we propose to combine its joint use, by exploiting the nonlinear mapping capabilites of neural networks to model objective functions, and to use them to supply their values to a genetic algorithm which performs on-line minimization.