Shape optimization of axisymmetric shells using a higher-order shear deformation theory

In this paper structural and sensitivity analysis for the optimization of laminated axisymmetric shells subjected to static constraints and arbitrary loading is presented. The shell thickness, radial coordinate of a nodal point, lamina thickness and the angle of orientation of the fibers are the design variables. The objective of the design optimization is the minimization of the volume of the shell or the strain energy. The model is based on a three-node axisymmetric finite element with 24 degrees of freedom. A higher-order theory is developed for the nonlinear distribution of the meridional displacement component through the thickness of the shell. The sensitivities of the discrete model developed are evaluated analytically using a symbolic manipulator. The efficiency and accuracy of the proposed model is discussed with reference to the applications.     

Shape optimization of dynamically loaded viscoelastic cylindrical shells

Axisymmetric deflections of cylindrical shells of variable thickness are examined. The shell material is linear viscoelastic. The loading is of the impulsive type—it induces inside the shell a radial velocity field. The amount of kinetic energy is prescribed. The thickness function includes some design parameters, which must be calculated so that deflections of the beam are minimal. Only designs with a given volume are considered.For solving this optimization problem the space variable and the time will be separated. For evaluating the minimum of the objective function the Nelder-Mead technique has been used. Computations show that the viscosity effect is essential only for very short shells. Some numerical examples are presented.     

Shape optimization of axisymmetric structures with adaptive finite element procedures

A robust and versatile algorithm for shape optimization with adaptive finite element procedures is developed for the design of axisymmetric structures. The algorithm is based on the use of boundary parameterization with cubic splines for describing shape changes and takes advantage of the utilities available in an advancing front type mesh generator. Six-noded triangular elements are adopted. Shape optimization examples involving solid axisymmetric structures are presented to illustrate the various features of the integrated approach.     

Shape optimization using reproducing kernel particle method and an enriched genetic algorithm

Combining Reproducing Kernel Particle Method (RKPM) with the proposed Multi-Family Genetic Algorithm (MFGA), a novel approach to continuum-based shape optimization problems is brought forward in this paper. Taking full advantage of the features of meshfree method and the merits of MFGA, the new method solves shape optimization problems in such a unique way that remeshing is avoided and particularly the computation burden and errors caused by sensitivity analysis are eliminated completely. The effectiveness, versatility and performance of the proposed approach are demonstrated via three 2-D numerical examples.     

基于改进型遗传算法的能源模糊优化的研究

将模糊优化的概念引入能源模型中,将多目标能源模型中的能源、经济和环境3个目标函数的约束条件模糊化,并定义了由惩罚函数和目标函数组成的新目标函数.针对该模糊多目标优化模型,采用改进型遗传算法进行求解,在适应度函数以及其它遗传操作的设计上做了改进.实验表明,这种遗传算法是一种性能优良的解决能源优化问题的启发式搜索算法,可以快速有效的求得能源优化问题的最优解,进而得到能源的最佳配置.     

基于模糊遗传算法的埋入式电阻热布局优化

针对标准遗传算法优化埋入式电阻热布局存在的过早收敛等问题,通过设计适应度函数、采用模糊逻辑控制器自适应调整交叉概率和变异概率,以及对长时间未进化的种群实施局部灾变等措施维持种群多样性,使算法最终收敛于全局最优解.仿真结果表明,该算法能够更好地抑制早熟收敛,算法优化布局结果的温度分布更平均,并通过热成像仪对实验样件进行温度分布测试验证了算法的有效性.     

基于遗传算法分阶段快速寻优

该算法充分利用算法前期搜索的有用信息训练出淘汰模式和优秀模式。交叉产生的新群体与上一代群体竞争后进入下一代。有淘汰模式指导变异提高变异能力。根据优秀模式缩短解空间,提高搜索精度和放大适应度值比例进行快速收敛寻优。仿真试验表明,收敛速度和最优解精度都有大幅度提高。     

Micro-scale truss optimization using genetic algorithm

This paper describes the development of a genetic algorithm that is capable of optimizing the mass of micro-scale trusses. Belonging to the group of periodic cellular materials, micro-scale trusses are characterized by the creation of a base cell with a pattern that is repeated in space until a global structure is obtained. Investigation in this field has generally been focused on the design of base cells and their resistance once the final structure is obtained. In this project we have attempted to optimize each individual cell and in particular its elements according to the loads and boundary conditions applied to the global structure. With this objective, we defined a dichotomic search algorithm that establishes a set of cross-sectional areas suitable for the micro-scale truss, formulated the penalty coefficient for the over-sized elements, and studied the clones and rebirth process in order to avoid stagnation of the genetic algorithm. The cell elements used in this project were equal to or less than to 1 mm long, with a cross-sectional area in the order of 10^− 9 m².     

Crew pairing optimization by a genetic algorithm with unexpressed genes

We propose a genetic algorithm to solve the pairing optimization problem for subway crew scheduling. Our genetic algorithm employs new crossover and mutation operators specially designed to work with the chromosomes of set-oriented representation. To enhance the efficiency of the search with the newly designed genetic operators, we let a chromosome consist of an expressed part and an unexpressed part. While the genes in both parts evolve, only the genes in the expressed part are used when an individual is evaluated. The purpose of the unexpressed part is to preserve information susceptible to be lost by the application of genetic operators, and thus to maintain the diversity of the search. Experiments with real-world data have shown that our genetic algorithm outperforms other local search methods such as simulated annealing and tabu search. Received: June 2005/Accepted: December 2005     

基于遗传算法的人工鱼群优化算法

人工鱼群算法(AFSA)是一种高效的群智能全局优化技术.通过对人工鱼群算法(AFSA)不足的研究,在遗传算法的基础上,提出了基于遗传算法的人工鱼群优化算法.该算法保留了人工鱼群算法(AFSA)简单、易实现的特点,同时克服了人工鱼漫无目的的随机游动或在非全局极值点的大量聚集,显著提高了算法的运行效率和求解质量.最后通过大量的函数和实例测试结果表明,与其它算法相比,该算法是可行和有效的,具有运行速度快和求解精度高等特点.     

Network model and optimization of reverse logistics by hybrid genetic algorithm

The interest about recovery of used products and materials have been increased. Therefore, reverse logistics network problem (rLNP) will be powerful and get a great potential for winning consumers in a more competitive context in the future.We formulate a mathematical model of remanufacturing system as three-stage logistics network model for minimizing the total of costs to reverse logistics shipping cost and fixed opening cost of the disassembly centers and processing centers. And we consider a multi-stage, multi-product and some attach condition for disassembly centers and processing centers, respectively.For solving this problem, we propose a genetic algorithm (GA) with priority-based encoding method consisting of 1st and 2nd stages combined a new crossover operator called as weight mapping crossover (WMX). A heuristic approach is applied in the 3rd stage to transportation of parts from processing center to manufacturer. Numerical experiments with various scales of rLNP models show the effectiveness and efficiency of our approach by comparing the recent researches.     

一种用于求解多目标组合优化的混合遗传算法

为高效求解多目标组合优化问题 ,提出一种进化计算与局部搜索结合的多目标算法。此算法基于个体排序数和密度值进行适应度赋值 ,采用非劣解并行局部搜索策略 ,在解的适应度赋值和局部搜索过程中使用 Pa-reto支配的概念。实验结果表明 ,新算法不仅提高了优化搜索的效率 ,且能够找到更多的近似 Pareto最优解。     

利用改进遗传算法优化PID参数

为了改善单纯遗传算法早熟收敛与寻优能力不足的问题,将粒子群算法引入遗传算法变异操作中,提出了一种基于遗传算法与粒子群算法的组合算法。将改进的遗传算法应用于PID控制器参数优化中,通过仿真实验表明,新算法效果明显优于单纯遗传算法,能有效克服早熟收敛现象、降低随机性初始种群的影响、提高算法收敛精度,具有良好的收敛性和寻优能力。     

Form finding of shells by structural optimization

Shell structures are known to be extremely parameter sensitive; even small changes of the initial design, e.g., to the shape of the shell, may drastically change the internal stress state. The ideal case for concrete shells is a pure membrane stress state in compression for all loading conditions. Since in many realistic situations the solution for an optimal shape is not obvious, the need for form finding methods is evident. This paper presents computational methods of structural optimization as a general tool for the form finding of shells. The procedure as a synthesis of design modelling, structural analysis and mathematical optimization is discussed with special emphasis on the modelling stage. Several examples show the power of the approach and the similarities to experimental solutions.     

免疫遗传算法及在优化问题中的应用综述*

指出遗传算法的不足,将免疫学原理引入遗传算法,进而形成免疫遗传算法。针对免疫遗传算法在优化问题中的研究现状,从编码技术、先验知识、操作算子、混沌理论引入、多种群方式、与小生境理论结合等方面进行了总结,指出了不足之处,最后探讨了免疫遗传算法需要进一步研究的问题和发展方向。     

基于多智能体遗传算法的战术航段优化

针对飞机战术飞行要求和威胁规避目标的问题,采用优势函数和战术规避相结合的原则,将战术航段优化问题转化为路径搜索问题,提出了基于多智能体遗传算法来解决此问题.采用自适应交叉和变异算子,改进自学习算子获取子代的算法,实现了全局最优的结果.通过和传统遗传算法进行仿真比较,相比之下,基于多智能体的遗传算法可以有效利用地形,实现战术飞行.     

Entropy-based multi-objective genetic algorithm for design optimization

Obtaining a fullest possible representation of solutions to a multiobjective optimization problem has been a major concern in Multi-Objective Genetic Algorithms (MOGAs). This is because a MOGA, due to its very nature, can only produce a discrete representation of Pareto solutions to a multiobjective optimization problem that usually tend to group into clusters. This paper presents a new MOGA, one that aims at obtaining the Pareto solutions with maximum possible coverage and uniformity along the Pareto frontier. The new method, called an Entropy-based MOGA (or E-MOGA), is based on an application of concepts from the statistical theory of gases to a baseline MOGA. Two demonstration examples, the design of a two-bar truss and a speed reducer, are used to demonstrate the effectiveness of E-MOGA in comparison to the baseline MOGA.     

Solution searching for multivariable optimization problems by a momentum genetic algorithm

Genetic algorithms (GAs) have emerged as powerful solution searching mechanisms, especially for nonlinear and multivariable optimization problems. Generally, it is time-consuming for GAs to find the solutions, and sometimes they cannot find the global optima. In order to improve their search performance, we propose a fast GA algorithm called momentum GA, which employs momentum offspring (MOS) and constant range mutation (CRM). MOS, which generates offspring based on the best individuals of current and past generations, is considered to have the effect of fast searching for the optimum solutions. CRM is considered to have the ability to avoid the production of ineffective individuals and maintain the diversity of the population. In order to verify the performance of our proposed method, a comparison between momentum GA and the conventional mean will be implemented by utilizing optimization problems of two multivariable functions and neural network training problems with different activation functions. Simulations show that the proposed method has good performance regardless of the small values of the population size and generation number in the GA. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

用遗传算法优化Boltzmann机

Boltzmann机是一种应用广泛的随机神经网络。它通过模拟退火算法进行网络学习,能取得一个全局或接近全局最优的最优值;通过期望网络模式和实际学习得到的网络模式比较来调节网络的权值,使网络能尽可能地达到或逼近期望的网络模式。将遗传算法运用到Boltzmann机的网络学习中,在对BM机编码后,通过选择、交叉和变异等遗传操作算子对网络进行训练,调整网络的权值,使适应度函数值大的网络保留下来,最终使网络达到期望的模式。通过实例验证,这是一种简单可行的调节网络权值的方法。