Design optimization of truss structures with continuous and discrete variables by hybrid of biogeography‐based optimization and differential evolution methods

This paper presents a hybrid BBO‐DE algorithm by hybridizing biogeography‐based optimization (BBO) and differential evolution (DE) methods for optimum design of truss structures with continuous and discrete variables. In BBO‐DE, the migration operator of BBO method serves as a local exploiter mechanism during the search process. Besides, DE has a role of the global exploration by performing multiple search directions in the search space to preserve more diversity in the population. By embedding of DE algorithm in BBO method as a mutation mechanism, the balance between the exploration and exploitation abilities is further improved. The comparative results with some of the most recently developed methods demonstrate the fast convergence properties of the proposed algorithm and confirm its effectiveness to solve optimum design problems of truss structures with continuous and discrete variables.     

基于小生境遗传算法的单层网壳结构优化设计研究

本文提出了一种改进的遗传算法,将小生境技术与模糊控制思想引入其中,从而达到保护种群的多样性的目的,同时使每代最优解得以保存。简单遗传算法本身存在着过早成熟收敛,后期收敛速度放慢等缺陷,其在加入小生境技术后虽可保持种群群体的多样性,使其优化过程不致过早收敛,但是由于小生境技术本身的特点,又不可避免地会产生部分个体的早熟以及陷入局部最优,本文于是在小生境遗传算法基础上加入模糊控制思想,对种群的杂交概率Pc和变异概率Pm进行模糊控制,从而形成了一种新型的模糊控制小生境遗传算法。最后通过单层网壳结构的数值实例证明了该方法的有效性和可行性,进一步为空间网格结构的优化设计奠定了基础。     

Optimal control of nonlinear frames by Newmark and distributed genetic algorithms

Active control of multi‐storey frame structures with nonlinear hysteretic response has been studied in this paper. A new nonlinear optimal control algorithm based on nonlinear Newmark integration method and distributed genetic algorithm (DGA) has been developed. The objective has been to reduce the response to below any desired level. In this study, DGA has been used to determine the weights in the control law corresponding to displacement, velocity and acceleration. The capabilities of the method has been assessed through simulation where an eight‐storey frame with bilinear hysteretic behaviour has been subjected to a white noise ground acceleration and controlled by the controllers designed by the new algorithm. An active tendon control system comprised of prestressed tendons and either a single actuator, which could apply a force to one of the floors, or eight actuators, which could fully control the frame, has been used. Also, the optimum actuator placement in the single actuator case has been studied. The designed controllers have been tested under both near and far‐field earthquakes and have performed successfully. Copyright © 2009 John Wiley & Sons, Ltd.     

基于自适应遗传算法的钢筋混凝土桥墩抗震设计方法

为满足钢筋混凝土桥墩抗震设计中对构件承载力和延性的要求,常需依靠设计者经验,对配筋方案进行反复调整,这就在一定程度上增加了结构设计难度,降低了工作效率。为解决这一实际问题,对传统自适应遗传算法中交叉率和变异率的计算方法进行改进,通过当前适应度值动态调整交叉率和变异率,在此基础上提出基于自适应遗传算法的钢筋混凝土桥墩结构抗震设计方法,实现对符合条件设计解的自动随机定向搜索,提高求解的收敛速度,并增强算法的鲁棒性。在对每一配筋方案的抗弯承载力和位移延性系数的计算中,又提出基于自适应遗传算法的钢筋混凝土桥墩弯矩 曲率关系的计算方法,提高求解效率。通过设计算例,可见该方法快速有效,计算过程无需人工干预,实现钢筋混凝土桥墩结构抗震设计的人工智能化。     

整数编码遗传算法离散变量优化设计

提出了一种具有较强局部搜索能力的整数编码遗传算法,给出了离散变量结构优化的数学模型和适应度函数,它采用动态边界约束、有限变异算子、( - 1,0,1)规划算子等策略来改进算法。算例表明本文算法收敛速度快、收敛平稳     

地下洞室地表非线性沉降模型参数确定新方法

根据随机介质理论构建地下洞室地表非线性沉降模型,把 DFP 变尺度算法作为一个与遗传算法的选择、交叉和变异平行的算子,嵌入到改进浮点编码遗传算法中,从而获得基于 DFP 算法和改进浮点编码遗传算法的加速混合遗传算法(AHGA),该方法既保留了 DFP 算法和改进浮点编码遗传算法的优点,又使收敛速度较快,还有可能搜索到最优化问题的全局优化解。通过某地下洞室地表沉降的工程实例分析表明,加速混合遗传算法不仅能解决地下洞室地表非线性沉降模型参数确定问题,而且优化结果优于其它方法。     

基于v-SVR和MVPSO算法的边坡位移反分析方法及其应用

 针对传统粒子群算法存在搜索空间有限、容易陷入局部最优点的缺陷,通过引入迁徙算子和自适应变异算子,提出基于粒子迁徙和变异的粒子群优化(MVPSO)算法。基准测试函数结果表明,改进的MVPSO算法较传统的粒子群优化算法在收敛效率上有大幅度提高,在处理非线性、多峰值的复杂优化问题中能快速地搜索,得到全局最优解。应用改进的MVPSO算法搜索最佳的支持向量机(v-SVR)模型参数,建立岩体力学参数与岩体位移之间的非线性支持向量机模型,提高v-SVR的预测精度和推广泛化性。然后,利用v-SVR模型的外推预测替代耗时的FLAC正向计算,利用改进的MVPSO算法搜索岩体力学参数的最优组合,提出v-SVR和MVPSO相结合的边坡位移反分析方法(v-SVR-MVPSO算法),与传统的BP-GA算法和v-SVR-GA算法相比,该算法在反演精度和反演效率上均有较大幅度提高。最后,将本文发展的v-SVR-MVPSO算法应用到大岗山水电站右岸边坡岩体参数反演分析,并对边坡后续开挖位移和稳定性进行预测,取得较好的效果。     

自适应小生境遗传算法在土钉支护结构整体稳定性分析中的应用

根据极限平衡理论和遗传算法原理,建立了基于自适应小生境遗传算法的土钉支护结构内部整体稳定性分析模型,该模型利用自适应小生境遗传算法搜索最危险滑动面及其对应的最小安全系数,同时能定量分析各设计参数对土钉支护结构整体稳定最小安全系数的影响。针对遗传算法在寻优过程中存在的问题,引入小生境淘汰技术,设计自适应交叉函数和自适应变异函数,自适应调整交叉率和变异率策略,克服了简单遗传算法易陷入局部极小和早熟收敛的缺陷,提高了算法的搜索效率、精度和稳定性。通过对珠江三角洲地区某深基坑实例分析,得到了一些有益结论,可作为软土深基坑支护结构优化设计和施工提供参考。     

基于分级遗传算法的离散变量结构优化设计

提出一种分级处理的遗传算法，它采用微种群和附加变异算子来减少计算量、增加算法的局部搜索能力．算例表明，这种算法收敛平稳、并取得了较好的效果。     

基于改进和声搜索算法的项目工期—成本优化

工期—成本优化（time-cost trade-off problem,TCTP）是组合优化中 NP—hard 问题,其实现的科学性对提高企业经济效益有着重要意义。为解决这一问题引入了和声搜索算法,并针对该算法收敛稳定性差、不易获得全局最优的缺陷,对其进行了算法参数的自适应改进。利用 MATLAB R2012a 软件对改进的和声算法进行编程,实现了工期—成本最优解的搜索。最后通过实例分析,验证了改进的和声搜索算法对工期—成本进行优化是行之有效的。     

弦支穹顶结构的预应力优化设计

采用遗传算法对弦支穹顶结构进行了预应力优化设计.从随机产生的初始种群出发,通过选择、杂交及变异等遗传算子,使种群一代一代地进化到最优解.本文采用二级优化的步骤对水平径向约束反力和上部杆件轴力两个优化目标同时进行了预应力优化,并编制了相应的程序对联方型弦支穹顶结构进行了预应力优化设计.结果表明,采用遗传算法收敛速度快,而且所需迭代次数少;最外圈索杆的预应力对水平径向约束反力的影响最大,而内部几圈索杆的预应力对上部单层网壳杆件的轴力影响较大.     

结构优化设计中自适应遗传算法的研究

主要介绍了一种基于非连续设计变量的结构优化设计方法一遗传算法(Genetic Al-gorithms，GA)。首先对遗传算法的来源、基本过程进行了论述；为了提高遗传算法的收敛性能，同时考虑到交叉率和变异率的选取问题，引入一种基于个体适应度值的自适应调整交叉率和变异率的自适应遗传算法，并通过算例表明自适应遗传算法是有效的。     

Modified Genetic Algorithm for the Dynamic Identification of Structural Systems Using Incomplete Measurements

Abstract: A modified real‐coded genetic algorithm to identify the parameters of large structural systems subject to the dynamic loads is presented in this article. The proposed algorithm utilizes several subpopulations and a migration operator with a ring topology is periodically performed to allow the interaction between them. For each subpopulation, a specialized medley of recent genetic operators (crossover and mutation) has been adopted and is briefly discussed. The final algorithm includes a novel operator based on the auto‐adaptive asexual reproduction of the best individual in the current subpopulation. This latter is introduced to avoid a long stagnation at the start of the evolutionary process due to insufficient exploration as well as to attempt an improved local exploration around the current best solution at the end of the search. Moreover, a search space reduction technique is performed to improve, both convergence speed and final accuracy, allowing a genetic‐based search within a reduced region of the initial feasible domain. This numerical technique has been used to identify two shear‐type mechanical systems with 10 and 30 degrees‐of‐freedom, assuming as unknown parameters the mass, the stiffness, and the damping coefficients. The identification will be conducted starting from some noisy acceleration signals to verify, both the computational effectiveness and the accuracy of the proposed optimizer in presence of high noise‐to‐signal ratio. A critical and detailed analysis of the results is presented to investigate the inner work of the optimizer. Finally, its performances are examined and compared to the most recent results documented in the current literature to demonstrate the numerical competitiveness of the proposed strategy.     

扩散问题的一种非重叠型区域分解算法

研究外区域Helmholtz问题的一种区域分解算法。将无界区域分解成为一些不重叠的子区域,自然积分算子被用作计算区域外边界上的人工边界条件。在能量范数意义下给出了算法的收敛性。最后讨论了数值离散化问题,并给出了相应的数值例子。     

基于加速混合遗传算法搜索复杂边坡的最危险滑动面

基于简化Bishop条分法分析复杂边界边坡的稳定性,以圆弧滑裂面与边坡面的左右交点的两个X坐标--XL和XR以及滑裂面圆心坐标的Y坐标--Y0作为设计变量,提出一种加速混合遗传算法(AHGA)对设计变量进行优化。该方法是通过把无约束优化的Powell算法作为一个与遗传算法的选择、交叉和变异平行的算子,嵌入到改进的实数编码遗传算法中而得到的,它同时具有两个方法的优点,有较强的自适应能力,使得收敛速度加快,还有可能搜索到最优化问题的全局最优解。通过一高速公路边坡工程实例验证,加速混合遗传算法(AHGA)搜索到的圆弧滑动面与现场勘察结果很吻合,优于简单遗传算法(SGA)。     

A Hybrid Particle Swarm—Gradient Algorithm for Global Structural Optimization

Abstract: The particle swarm optimization (PSO) method is an instance of a successful application of the philosophy of bounded rationality and decentralized decision making for solving global optimization problems. A number of advantages with respect to other evolutionary algorithms are attributed to PSO making it a prospective candidate for optimum structural design. The PSO‐based algorithm is robust and well suited to handle nonlinear, nonconvex design spaces with discontinuities, exhibiting fast convergence characteristics. Furthermore, hybrid algorithms can exploit the advantages of the PSO and gradient methods. This article presents in detail the basic concepts and implementation of an enhanced PSO algorithm combined with a gradient‐based quasi‐Newton sequential quadratic programming (SQP) method for handling structural optimization problems. The proposed PSO is shown to explore the design space thoroughly and to detect the neighborhood of the global optimum. Then the mathematical optimizer, starting from the best estimate of the PSO and using gradient information, accelerates convergence toward the global optimum. A nonlinear weight update rule for PSO and a simple, yet effective, constraint handling technique for structural optimization are also proposed. The performance, the functionality, and the effect of different setting parameters are studied. The effectiveness of the approach is illustrated in some benchmark structural optimization problems. The numerical results confirm the ability of the proposed methodology to find better optimal solutions for structural optimization problems than other optimization algorithms.     

加速混合遗传算法在搜索边坡最危险滑动面中的应用

基于简化Janbu条分法,提出一种加速混合遗传算法,在无需对边坡最危险滑动面的几何形状进行假设的前提下,自由搜索最危险任意形状滑动面,并计算其对应的最小安全系数。该方法是通过把无约束优化的Powell算法作为一个与遗传算法的选择、交叉和变异平行的算子,嵌入到改进的实数编码遗传算法中而得到的,它同时具有两种方法的优点:(1)有较强的自适应能力,使得收敛速度加快;(2)还有可能搜索到最优化问题的全局最优解。首先通过一算例验证加速混合遗传算法的准确性,然后应用于一工程实例。与现场勘察结果对照表明,加速混合遗传算法搜索到的最危险滑动面与实际情况吻合得很好,优于简单遗传算法。     

Optimum Design of Grillage Systems Using Genetic Algorithms

Genetic algorithms have attracted great attention due to their ability to provide a solution to discrete optimum design problems. In this study, a genetic algorithm is presented for the optimum design of grillage systems to decide the cross-sectional properties of members from a standard set of universal beam sections. The deflection limitations and the allowable stress constraints are considered in the formulation of the design problem. Furthermore, in obtaining the response of grillage systems, the effect of warping and shear is also taken into account. The algorithm starts with an initial population of designs and carries out basic genetic operations of selection, mating, crossover, and mutation that yield to a new generation. It continues the generation of populations until the same individual dominates the population. An improvement is also suggested to the general steps of the genetic algorithm to prevent the destruction of good individuals during the generation of new populations. The algorithm is applied to the optimum design of a 40-member grillage system to investigate the effect of warping.     

Optimum design of steel frames using a multiple-deme GA with improved reproduction operators

In this paper, the performance of the genetic algorithm is improved by introducing some new crossover and mutation operators. The new operators are incorporated into a multiple-deme genetic algorithm in which population is divided into subpopulations and communication between different demes is established through migration of individuals, enhancing diversity and resulting in better solutions. This algorithm is applied to the minimum weight design of steel frames subjected to actual strength and ductility constraints of AISC-ASD specifications as well as other serviceability and constructability constraints. The efficiency of the proposed method is demonstrated through optimising two benchmark problems including a three-bay, three-storey steel frame and a five-bay, 22-storey special steel frame. Significant improvements in the optimum solutions are obtained with reduced number of finite element analyses, resulting in less computational effort.     

Border-search and jump reduction method for size optimization of spatial truss structures

This paper proposes a sensitivity-based border-search and jump reduction method for optimum design of spatial trusses. It is considered as a two-phase optimization approach, where at the first phase, the first local optimum is found by few analyses, after the whole searching space is limited employing an efficient random strategy, and the second phase involves finding a sequence of local optimum points using the variables sensitivity with respect to corresponding values of constraints violation. To reach the global solution at phase two, a sequence of two sensitivity-based operators of border-search operator and jump operator are introduced until convergence is occurred. Sensitivity analysis is performed using numerical finite difference method. To do structural analysis, a link between open source software of OpenSees and MATLAB was developed. Spatial truss problems were attempted for optimization in order to show the fastness and efficiency of proposed technique. Results were compared with those reported in the literature. It shows that the proposed method is competitive with the other optimization methods with a significant reduction in number of analyses carried.