Optimum topological design of geometrically nonlinear single layer latticed domes using coupled genetic algorithm

Single layer latticed domes are lightweight and elegant structures that provide cost-effective solutions to cover the large areas without intermediate supports. The topological design of these structures present difficulty due to the fact that the number of joints and members as well as the height of the dome keeps on changing during the design process. This makes it necessary to automate the numbering of joints and members and the computation of the coordinates of joints in the dome. On the other hand the total number of joints and members in a dome is function of the total number of rings exist in the dome. Currently no study is available that covers the topological design of dome structures that give the optimum number of rings, the optimum height of crown and the tubular cross-sectional designations for the dome members under the given general external loading. The algorithm presented in this study carries out the optimum topological design of single layer lattice domes. The serviceability and strength requirements are considered in the design problem as specified in BS5950. The algorithm takes into account the nonlinear response of the dome due to effect of axial forces on the flexural stiffness of members. The optimum solution of the design problem is obtained using coupled genetic algorithm. Having the total number of rings and the height of crown as design variables provides the possibility of having a dome with different topology for each individual in the population. It is shown in the design example considered that the optimum number of joints, members and the optimum height of a geodesic dome under a given external loading can be determined without designer’s interference.     

Optimum topology design of various geometrically nonlinear latticed domes using improved harmony search method

Domes are elegant and economical structures used in covering large areas. They are built in various forms. According to their form, they are given special names such as lamella, network, and geodesic domes. In this paper, optimum topological design algorithm is presented that determines the optimum number of rings, the optimum height of crown and tubular section designations for the member groups of these domes. The design algorithm developed has a routine that generates the data required for the geometry of these domes automatically. The minimum weight of each dome is taken as the objective function. The design constraints are implemented according to the provision of LRFD-AISC (Load and Resistance Factor Design–American Institute of Steel Constitution). The optimum topological design problem that considers these constraints turns out to be discrete programming problem. Improved harmony search algorithm is suggested to determine its optimum solution. The design algorithm also considers the geometric nonlinearity of these dome structures. Design examples are presented to demonstrate the effectiveness and robustness of the design optimization algorithm developed.     

Geometry and topology optimization of geodesic domes using charged system search

Dome structures provide cost-effective solutions for covering large areas without intermediate supports. In this article, simple procedures are developed to reach the configuration of the geodesic domes. A new definition of dome optimization problems is given which consists of finding optimal sections for elements (size optimization), optimal height for the crown (geometry optimization) and the optimum number of elements (topology optimization) under determined loading conditions. In order to find the optimum design, the recently developed meta-heuristic algorithm, known as the Charged System Search (CSS), is applied to the optimum design of geodesic domes. The CSS takes into account the nonlinear response of the domes. Using CSS, the optimum design of the geodesic domes is efficiently performed.     

A mixed GA-PSO-based approach for performance-based design optimization of 2D reinforced concrete special moment-resisting frames

A mixed genetic algorithm and particle swarm optimization in conjunction with nonlinear static and dynamic analyses as a smart and simple approach is introduced for performance-based design optimization of two-dimensional (2D) reinforced concrete special moment-resisting frames. The objective function of the problem is considered to be total cost of required steel and concrete in design of the frame. Dimensions and longitudinal reinforcement of the structural elements are considered to be design variables and serviceability, special moment-resisting and performance conditions of the frame are constraints of the problem. First, lower feasible bond of the design variables are obtained via analyzing the frame under service gravity loads. Then, the joint shear constraint has been considered to modify the obtained minimum design variables from the previous step. Based on these constraints, the initial population of the genetic algorithm (GA) is generated and by using the nonlinear static analysis, values of each population are calculated. Then, the particle swarm optimization (PSO) technique is employed to improve keeping percent of the badly fitted populations. This procedure is repeated until the optimum result that satisfies all constraints is obtained. Then, the nonlinear static analysis is replaced with the nonlinear dynamic analysis and optimization problem is solved again between obtained lower and upper bounds, which is considered to be optimum result of optimization solution with nonlinear static analysis. It has been found that by mixing the analyses and considering the hybrid GA-PSO method, the optimum result can be achieved with less computational efforts and lower usage of materials.     

Optimum design of geometrically nonlinear space trusses

A structural optimization algorithm is developed for geometrically nonlinear three-dimensional trusses subject to displacement, stress and cross-sectional area constraints. The method is obtained by coupling the nonlinear analysis technique with the optimality criteria approach. The nonlinear behaviour of the space truss which was required for the steps of optimality criteria method was obtained by using iterative linear analysis. In each iteration the geometric stiffness matrix is constructed for the deformed structure and compensating load vector is applied to the system in order to adjust the joint displacements. During nonlinear analysis, tension members are loaded up to yield stress and compression members are stressed until their critical limits. The overall loss of elastic stability is checked throughout the steps of algorithm. The member forces resulted at the end of nonlinear analysis are used to obtain the new values of design variables for the next cycle. Number of design examples are presented to demonstrate the application of the algorithm. It is shown that the consideration of nonlinear behaviour of the space trusses in their optimum design makes it possible to achieve further reduction in the overall weight. The other advantage of the algorithm is that it takes into account the realistic behaviour of the structure, without which an optimum design might lead to erroneous result. This is noticed in one of the design example where a tension member changed into a compression one at the end of nonlinear analysis.     

一种求解车间调度问题的混沌遗传规划方法

作业车间调度问题是制造业的一个经典NP-hard组合优化难题。提出一种基于混沌遗传规划的调度算法,利用遗传规划进行染色体的结构设计,采用混沌序列改善初始种群质量,利用混沌扰动来维持进化群体的多样性,并自适应调整个体权重,使算法具有优良的综合求解性能。实验表明,算法对典型的标准调度测试问题具有较强的全局搜索能力,甘特图表明其获得的最优解优于当前已知的最优解历史记录,对比结果表明了该方法的有效性。     

遗传正交试验算法及其应用

遗传算法是一种能够在较大的参数空间中搜索到问题最优解的方法,在解决非线性问题时具有全局收敛性,但收敛性能差。论文提出一种结合遗传与正交试验两种算法优点的新混合遗传算法,应用表明该算法收敛能力强、寻优能力强及能产生大量次优解,是一种值得信赖的算法。     

Optimum structural design of spatial steel frames via biogeography-based optimization

Metaheuristic algorithms have provided an efficient tool for designers by which discrete optimum design of real-size steel space frames under design code requirements can be obtained. In this study, the optimum sizing design of steel space frames is formulated according to provisions of Load and Resistance Factor Design—American Institute of Steel Construction. The weight of the steel frame is taken as objective function. The design algorithm selects the appropriate W sections for members of the steel frame such that the frame weight is the minimum and design code limitations are satisfied. The biogeography-based optimization algorithm is utilized to find out the optimum solution of the discrete programming problem. This algorithm is one of the recent additions to metaheuristic techniques which are based on theory of island biogeography where each habitat is assumed to be potential solution for the design problem. The performance of the biogeography-based optimization algorithm is compared with other recent metaheuristic algorithms such as adaptive firefly algorithm, teaching and learning-based optimization, artificial bee colony optimization, dynamic harmony search algorithm, and ant colony algorithm. It is shown that biogeography-based optimization algorithm outperforms other metaheuristic techniques in the design examples considered.

     

Intelligent Fixture Design through a Hybrid System of Artificial Neural Network and Genetic Algorithm

In designing fixtures for machining operations, clamping scheme is a complex and highly nonlinear problem that entails the frictional contact between the workpiece and the clamps. Such parameters as contact area, state of contact, clamping force, wear and damage in the contact area and deformation of the component are of special interest. A viable fixture plan must include the optimum values of clamping forces. Along research efforts carried out in this area, this comprehensive problem in fixture design needs further investigation. In this study, a hybrid learning system that uses nonlinear finite element analysis (FEA) with a supportive combination of artificial neural network (ANN) and genetic algorithm (GA) is discussed. A frictional model of workpart–fixture system under cutting and clamping forces is solved through FEA. Training and querying an ANN takes advantage of the results of FEA. The ANN is required to recognize a pattern between the clamping forces and state of contact in the workpiece–fixture system and the workpiece maximum elastic deformation. Using the identified pattern, a GA-based program determines the optimum values for clamping forces that do not cause excessive deformation/stress in the component. The advantage of this work against similar studies is manifestation of exact state of contact between clamp elements and workpart. The results contribute to automation of fixture design task and computer aided process planning (CAPP).     

The effects of rehabilitation objectives on near optimal trade-off relation between minimum weight and maximum drift of 2D steel X-braced frames considering soil-structure interaction using a cluster-based NSGA II

A cluster-based non-dominated sorting genetic algorithm (NSGA) II has been considered to investigate the effects of rehabilitation objectives on multi-objective design optimization of two-dimensional (2D) steel X-braced frames in the presence of soil-structure interaction. The substructure elasto-perfect plastic model has been adopted for modeling of the soil-structure interaction and the nonlinear pushover analysis is used to evaluate the performance level of the frames for a specified hazard level. Cross-sections of grouped elements of the frames are considered to be discontinuous design variables of the problem. Via implementing some of the constraints, which are independent of doing the time-consuming nonlinear analysis, input population of the optimization technique has been clustered. By using the nonlinear analysis technique in conjunction with the cluster-based NSGA II, near optimal trade-off relation between minimum weight and maximum story drifts of the frames are obtained. The allowable rotations, geometry, and resistance constraints of the structural elements are considered in the optimization design of the frames. The effects of the enhanced basic safety and limited selective rehabilitation objectives on optimum design of the frame are studied. The results show differences between the optimum results of the three mentioned rehabilitation objectives and effects of soil types.

     

基于黄金分割双种群遗传算法的区域交通信号控制

针对非均匀交通流的城市区域信号配时优化问题,以区域总通行能力和总延误为优化目标,构建基于目标相对占优策略的城市区域交通信号优化模型;在采用遗传算法求解优化模型时,由于遗传算法易早熟收敛会导致寻优效果不佳,因此引入黄金分割法对双种群遗传算法进行改进,两个种群同时且独立地进行寻优操作,并进行个体交换,避免算法陷入局部最优的陷阱,利用4个常用的测试函数验证算法有效性,实验结果表明改进的算法能够快速搜索到全局最优解;最后对所提的模型和算法进行有效性评价,结果表明,所建模型符合实际交通控制目标并且计算简单,验证了模型的有效性;所改进的算法在城市区域路网中能够有效地获得良好的信号配时方案。     

基于蜂群遗传算法的0-1背包问题

针对0-1背包问题,本文提出了基于蜂群遗传算法的优化求解方案。该算法包括两个种群,一个主要用于全局搜索,另一个主要用于局部搜索;每个个体采用二进制编码;采用最优个体交叉策略;对当前解的处理措施是将还未装入背包且性价比最好的物品装进背包,直至不能装为止;不符合约束条件的解采用诱变因子指导变异处理;遗传算子包括单点交叉算子、简单变异算子、主动进化算子和抑制算子。本算法充分发挥了遗传算法的群体搜索和全局收敛的特性,快速地并行搜索,有效地克服了经典遗传算法容易陷入局部最优问题。数值实验表明,该算法在求解0-1背包问题中取得了较好的效果,同样可以应用于其它的组合优化问题。     

遗传算法的早熟问题及其防止

针对遗传算法的早熟收敛问题,本文对其进行了分析和研究,提出几种防止早熟问题的措施,分析了编码、适应度、参数的动态自适应调整、多种群、精英种群和混合遗传算法等策略对早熟问题的影响。     

Benchmark case studies in optimization of geometrically nonlinear structures

A structural optimization algorithm is developed for truss and beam structures undergoing large deflections against instability. The method combines the nonlinear buckling analysis using the displacement control technique, with the optimality criteria approaches. Several benchmark case studies illustrate the procedure and the results are compared with examples reported in the literature. It is shown that a design based on the generalized eigenvalue problem (linear buckling) highly underestimates the optimum mass or overestimates the buckling load for these types of structures, so a design based on the linear buckling analysis may result in catastrophic failure. The effect of geometrical nonlinearities and element imperfections has also been studied.     

分数阶策略和带有Lévy飞行的螺旋蝙蝠算法

针对蝙蝠算法在求解多峰、复杂非线性问题时,搜索效率降低、易陷入局部最优等不足,提出了一种改进的蝙蝠算法。引入具有短期记忆特性的分数阶策略来更新蝙蝠位置,增加种群多样性,提高了算法收敛速度;用带有Lévy飞行的阿基米德螺旋策略产生局部新解,增强局部开发能力,同时有助于算法跳出局部最优;采用新的非线性动态机制调节响度和脉冲发射率,以平衡算法的探索和开发。选取CEC2014测试集,包括单峰、多峰、混合以及复合函数,对提出的算法和其他群智能算法进行仿真实验,测试结果表明提出的算法搜索效率和求解精度相较于对比算法得到提升,用Friedman统计分析验证了算法的优越性。将提出的算法用于求解机械工程减速器设计问题,与PSO-DE、WCA、APSO进行实验对比,验证该算法的有效性。     

An interactive genetic algorithm with the interval arithmetic based on hesitation and its application to achieve customer collaborative product configuration design

Complex product configuration design requires rapid and accurate response to customers’ demand. The participation of customers in product design will be a very effective solution to achieve this. The traditional interactive genetic algorithm (IGA) can solve the above problem to some extent by a computer-aided user interface. However, it is difficult to adopt an accurate number to express an individual's fitness because the customers’ cognition of evolutionary population is uncertain, and to solve the users’ fatigue problem in IGA. Thus, an interactive genetic algorithm with interval individual fitness based on hesitancy (IGA-HIIF) is proposed in this paper. In IGA-HIIF, the interval number derived from users’ evaluation time is adopted to express an individual's fitness, and the evolutionary individuals are compared according to the interval probability dominant strategy proposed in this paper. Then, the genetic operations are applied to generate offspring population and the evolutionary process doesn’t stop until it meets the termination conditions of the evolution or user manually terminates the evolution process. The IGA-HIIF is applied into the design system of the car console configuration, and compared to the other two kinds of IGA. The extensive experiment results are provided to demonstrate that our proposed algorithm is correct and efficient.     

An adaptive annealing genetic algorithm for the job-shop planning and scheduling problem

The genetic algorithm, the simulated annealing algorithm and the optimum individual protecting algorithm are based on the order of nature, and there exist some application limitations on global astringency, population precocity and convergence rapidity. An adaptive annealing genetic algorithm is proposed to deal with the job-shop planning and scheduling problem for the single-piece, small-batch, custom production mode. In the AAGA, the adaptive mutation probability is included to improve upon the convergence rapidity of the genetic algorithm, and to avoid local optimization, the Boltzmann probability selection mechanism from the simulated annealing algorithm, which solves the population precocity and the local convergence problems, is applied to select the crossover parents. Finally, the AAGA-based job-shop planning and scheduling problem is discussed, and the computing results of AAGA and GA are depicted and compared.     

望目特性连续型参数稳健设计遗传优化方法

针对一类可通过理论计算得到输出特性值的望目特性连续型参数稳健设计问题,提出了一种遗传进化方法。描述了研究的问题;提出了望目特性连续型参数稳健设计遗传进化方法的技术思路：以密集抽样取代离散化处理,以个体取代试验方案,以变化的种群取代固定的内表,通过遗传进化得到最优设计方案。提出并设计了一种望目特性连续型参数稳健设计遗传算法,阐述了算法的计算流程、个体编码、适应度、种群初始化、解码操作及遗传操作。通过案例分析验证了所提方法的有效性。     

Computationally efficient analysis of cable-stayed bridge for GA-based optimization

Optimum design of a cable-stayed bridge structure is very complicated because of large number of design variables. Use of genetic algorithms (GAs) in optimizing such structure consumes significant computational time. Due to nonlinearity, structural analysis itself takes considerable computational time and the genetic algorithm has to perform a large number of iterations in order to obtain global minima. A new approach combining GA and support vector machine (SVM) has been adopted. This drastically reduces the computation time of optimization. The genetic algorithm is employed to obtain the minimum cost of the cable-stayed bridge. Constraint evaluation is done using SVM which is trained by a data base generated through FEM analysis. System level optimization is carried out considering configuration and cross-sectional parameters as design variables. In the present study, optimization was carried out for bridge lengths ranging from 100 to 500 m. Final optimum designs were reanalyzed to check the adequacy of the developed approach.     

用基于多目标决策的遗传算法解网络拓扑结构设计问题

多点网络拓扑结构设计问题是NP-完全问题。该文提出了一个基于多目标决策的遗传算法(MCGA)来解决多点网络拓扑结构问题。和其它多目标遗传算法不同的是:首先,对网络节点进行预划分,使得Pareto优的节点归于候选分枝节点集合;其次,修改了Prüfer编码,使得编码中的码元代表候选分枝节点,以利于对分枝节点的搜索;最后,构造了分枝变异算子与非分枝变异算子作为主要的进化算子。该算法以概率1收敛于全局最优解集。数值实验表明该算法优于其它多目标遗传算法。