Design of frames using genetic algorithm,force method and graph theory

In the process of discrete‐sizing optimal design of frame structures by genetic algorithm, analysis should be performed several times. In this article, the force method is employed for the analysis using subminimal cycle basis of their graph models. The advantage of employing this method lies in the fact that the matrices corresponding to particular and complementary solutions are formed once and independently of the mechanical properties of members. These matrices are then used several times in the process of the sequential analyses, increasing the speed of optimization. Copyright © 2004 John Wiley & Sons, Ltd.     

Genetic algorithm for discrete‐sizing optimal design of trusses using the force method

In the process of discrete‐sizing optimal design of truss structures by Genetic Algorithm (GA), analysis should be performed several times. In this article, the force method is employed for the analysis. The advantage of using this method lies in the fact that the matrices corresponding to particular and complementary solutions are formed independently of the mechanical properties of members. These matrices are used several times in the process of the sequential analyses, increasing the speed of optimization. The second feature of the present method is the automatic nature of the prediction of the useful range of sections for a member from a list of profiles with a large number of cross‐sections. The third feature consists of a contraction process developed to increase the efficiency of the GA by which an optimal design for the first sub‐string associated with member cross‐sections is obtained. Improved designs are achieved in subsequent cycles by reducing the length of sub‐strings. Copyright © 2002 John Wiley & Sons, Ltd.     

Analysis,design and optimization of structures using force method and genetic algorithm

In the first part of this paper, the energy formulation of the force method is presented and analysis is performed using genetic algorithm. Two simple examples are provided to show the accuracy of the approach. In the second part, an efficient method is developed for designing structures with prescribed stress ratios for its members. The genetic algorithm performed very well and designs with specified stress ratios were achieved with a good convergence rate. A unit value of c_i for all the members of a structure corresponds to the well known fully stressed design. In the third part, minimum weight design is formulated by the additional conditions being imposed on the design process. Again, genetic algorithm showed to be a powerful means for optimization. Copyright © 2005 John Wiley & Sons, Ltd.     

Topology and sizing optimization of discrete structures using a cooperative coevolutionary genetic algorithm with independent ground structures

This article proposes a method called the cooperative coevolutionary genetic algorithm with independent ground structures (CCGA-IGS) for the simultaneous topology and sizing optimization of discrete structures. An IGS strategy is proposed to enhance the flexibility of the optimization by offering two separate design spaces and to improve the efficiency of the algorithm by reducing the search space. The CCGA is introduced to divide a complex problem into two smaller subspaces: the topological and sizing variables are assigned into two subpopulations which evolve in isolation but collaborate in fitness evaluations. Five different methods were implemented on 2D and 3D numeric examples to test the performance of the algorithms. The results demonstrate that the performance of the algorithms is improved in terms of accuracy and convergence speed with the IGS strategy, and the CCGA converges faster than the traditional GA without loss of accuracy.     

Topology optimization of fluid problems using genetic algorithm assisted by the Kriging model

A non‐gradient‐based approach for topology optimization using a genetic algorithm is proposed in this paper. The genetic algorithm used in this paper is assisted by the Kriging surrogate model to reduce computational cost required for function evaluation. To validate the non‐gradient‐based topology optimization method in flow problems, this research focuses on two single‐objective optimization problems, where the objective functions are to minimize pressure loss and to maximize heat transfer of flow channels, and one multi‐objective optimization problem, which combines earlier two single‐objective optimization problems. The shape of flow channels is represented by the level set function. The pressure loss and the heat transfer performance of the channels are evaluated by the Building‐Cube Method code, which is a Cartesian‐mesh CFD solver. The proposed method resulted in an agreement with previous study in the single‐objective problems in its topology and achieved global exploration of non‐dominated solutions in the multi‐objective problems. © 2016 The Authors International Journal for Numerical Methods in Engineering Published by John Wiley & Sons Ltd     

Topology optimization of compliant mechanisms using pairs of curves

The structural topology optimization approach can be used to generate compliant mechanisms for some desired input–output requirements. The success of the optimization depends on the structural geometry representation scheme used. In this paper, a novel representation scheme is proposed. The representation scheme is characterized by pairs of curves that are used to connect Input/Ouput (I/O) regions of the structure. Each pair of curves includes a normal curve and a fat curve. The areas bounded by the pair of curves define the material distribution between them. All I/O regions are connected to one another (either directly or indirectly) by pairs of curves in order to form one single connected load-bearing structure. A genetic algorithm for constrained and multiobjective optimization is then applied with the representation scheme of the structure in the form of a graph. Simulation results from a displacement inverter and a displacement redirector indicate that the presented representation scheme is appropriate.     

桁架结构布局优化的并行子空间方法

由于结构布局优化存在设计变量类型众多和变量耦合等问题,采取合适的优化方法获得满足结构设计要求的最小质量的结构具有重要的工程意义.基于多学科设计优化方法中的并行子空间优化法,提出一种桁架结构布局优化的并行子空间优化方法.将结构布局设计问题按设计变量类型分为布局、形状和尺寸三个并行的子空间,设计变量在各自的子空间内单独优化,各子空间优化结束后,在系统级中协调3类设计变量,保持最小质量的子空间的优化设计变量不变,采用近似一维搜索的方法协调其他子空间的设计变量,然后进行下一次迭代直至收敛.2个算例表明该方法能够取得较好的优化结果,具有实际工程应用价值.     

Topology optimization of compliant mechanisms using the homogenization method

A procedure to obtain a topology of an optimal structure considering flexibility is presented. The methodology is based on a mutual energy concept for formulation of flexibility and the homogenization method. A multi-objective optimization problem is formulated as an application of compliant mechanism design. Some examples of the design of compliant mechanisms for plane structures are presented. © 1998 John Wiley & Sons, Ltd.     

Topology optimization of resonating structures using SIMP method

The present paper is concerned with the layout optimization of resonating actuators using topology optimization techniques. The goal of the optimization is a maximization of the magnitude of steady‐state vibrations for a given excitation frequency. The problem formulation includes an external viscous damper at the output port which models a working load on the structure. Copyright © 2002 John Wiley & Sons, Ltd.     

Topology optimization using bi-directional evolutionary structural optimization based on the element-free Galerkin method

In this article, the bi-directional evolutionary structural optimization (BESO) method based on the element-free Galerkin (EFG) method is presented for topology optimization of continuum structures. The mathematical formulation of the topology optimization is developed considering the nodal strain energy as the design variable and the minimization of compliance as the objective function. The EFG method is used to derive the shape functions using the moving least squares approximation. The essential boundary conditions are enforced by the method of Lagrange multipliers. Several topology optimization problems are presented to show the effectiveness of the proposed method. Many issues related to topology optimization of continuum structures, such as chequerboard patterns and mesh dependency, are studied in the examples.     

Topology optimization of plane structures using smoothed particle hydrodynamics method

This paper presents an alternative topology optimization method based on an efficient meshless smoothed particle hydrodynamics (SPH) algorithm. To currently calculate the objective compliance, the deficiencies in standard SPH method are eliminated by introducing corrective smoothed particle method and total Lagrangian formulation. The compliance is established relative to a designed density variable at each SPH particle which is updated by optimality criteria method. Topology optimization is realized by minimizing the compliance using a modified solid isotropic material with penalization approach. Some numerical examples of plane elastic structure are carried out and the results demonstrate the suitability and effectiveness of the proposed SPH method in the topology optimization problem. Copyright © 2016 John Wiley & Sons, Ltd.     

Topology optimization of exterior acoustic-structure interaction systems using the coupled FEM-BEM method

This paper presents an efficient topology optimization procedure for exterior acoustic-structure interaction problems, in which the coupled systems are formulated by the boundary element method (BEM) and the finite element method (FEM). So far, the topology optimization based on the coupled FEM-BEM still faces several issues needed to be addressed, especially the efficient design sensitivity analysis for the coupled systems. In this work, we contribute to these issues in two main aspects. Firstly, the adjoint variable method (AVM) formulations are derived for sensitivity analysis of arbitrary objective function, and the feedback coupling between the structural and acoustic domains are taken into consideration in the sensitivity analysis. Secondly, in addition to the application of fast multipole method (FMM) in the acoustic BEM response analysis, the FMM is now updated to adapt to the arising different multiplications in the AVM equations. These accelerations save considerable computing time and memory. Numerical tests show that the developed approach permits its application to large-scale problems. Finally, some basic observations for the optimized designs are drawn from the numerical investigations.     

Improved genetic algorithm for mixed-discrete-continuous design optimization problems

An improved genetic algorithm (IGA) is presented to solve the mixed-discrete-continuous design optimization problems. The IGA approach combines the traditional genetic algorithm with the experimental design method. The experimental design method is incorporated in the crossover operations to systematically select better genes to tailor the crossover operations in order to find the representative chromosomes to be the new potential offspring, so that the IGA approach possesses the merit of global exploration and obtains better solutions. The presented IGA approach is effectively applied to solve one structural and five mechanical engineering problems. The computational results show that the presented IGA approach can obtain better solutions than both the GA-based and the particle-swarm-optimizer-based methods reported recently.     

Dynamic optimization of chemical engineering problems using a control vector parameterization method with an iterative genetic algorithm

An approach that combines genetic algorithm (GA) and control vector parameterization (CVP) is proposed to solve the dynamic optimization problems of chemical processes using numerical methods. In the new CVP method, control variables are approximated with polynomials based on state variables and time in the entire time interval. The iterative method, which reduces redundant expense and improves computing efficiency, is used with GA to reduce the width of the search region. Constrained dynamic optimization problems are even more difficult. A new method that embeds the information of infeasible chromosomes into the evaluation function is introduced in this study to solve dynamic optimization problems with or without constraint. The results demonstrated the feasibility and robustness of the proposed methods. The proposed algorithm can be regarded as a useful optimization tool, especially when gradient information is not available.     

Topology optimization in magnetic fields using the homogenization design method

To improve the performance of electric machinery, it is necessary to obtain the optimal topology of a structure in magnetic fields. The homogenization design method is applied to obtain the optimal topology. In the method, the change of inner hole size and rotational angle of unit cell determines the optimal material distribution in a design domain and this distribution defines an optimal topology. The objective function is defined as maximizing magnetic mean compliance (MMC). This is the same as maximizing magnetic vector potential and effective to improve the performance of electomagnet. The analysis and optimization is performed based on three‐dimensional hexahedral elements. This design method is applied to the H‐shaped electromagnet (H‐magnet). Copyright © 2000 John Wiley & Sons, Ltd.     

Seeding the initial population with feasible solutions in metaheuristic optimization of steel trusses

In spite of considerable research work on the development of efficient algorithms for discrete sizing optimization of steel truss structures, only a few studies have addressed non-algorithmic issues affecting the general performance of algorithms. For instance, an important question is whether starting the design optimization from a feasible solution is fruitful or not. This study is an attempt to investigate the effect of seeding the initial population with feasible solutions on the general performance of metaheuristic techniques. To this end, the sensitivity of recently proposed metaheuristic algorithms to the feasibility of initial candidate designs is evaluated through practical discrete sizing of real-size steel truss structures. The numerical experiments indicate that seeding the initial population with feasible solutions can improve the computational efficiency of metaheuristic structural optimization algorithms, especially in the early stages of the optimization. This paves the way for efficient metaheuristic optimization of large-scale structural systems.     

Groundwater optimization and parameter estimation by genetic algorithm and dual reciprocity boundary element method

Genetic algorithms have been applied with great success in a number of fields as a tool for optimization. This paper explores their application in the groundwater field. The dual reciprocity boundary element method with global interpolation functions is employed as the computational tool. Two optimization problems are investigated: a pumping management problem in a homogeneous aquifer, and a parameter estimation problem in a heterogeneous aquifer. The initial findings about the effectiveness of the algorithms are encouraging.     

Decomposition-based design optimization method using genetic co-evolution

The paper examines new strategies for the implementation of genetic algorithms in a decomposition-based approach for design. In this approach, the design problem is decomposed into smaller-sized sub-problems, the solutions for which are obtained through co-evolution. The emphasis resides in evaluating methods for exchanging design information relevant to coordinating solutions of temporarily decoupled sub-problems. Methods based on modification of genetic makeup through experiential inheritance (exposure to another species), and through inter-species migration are investigated in this work. Different forms of design problem coupling are investigated, ranging from coupling through constraints only, to coupled objective and constraint functions. The proposed strategies are validated through implementation in representative algebraic and structural design problems.     

Topology optimization of structures using meshless density variable approximants

This paper proposes a new structural topology optimization method using a dual‐level point‐wise density approximant and the meshless Galerkin weak‐forms, totally based on a set of arbitrarily scattered field nodes to discretize the design domain. The moving least squares (MLS) method is used to construct shape functions with compactly supported weight functions, to achieve meshless approximations of system state equations. The MLS shape function with the zero‐order consistency will degenerate to the well‐known ‘Shepard function’, while the MLS shape function with the first‐order consistency refers to the widely studied ‘MLS shape function’. The Shepard function is then applied to construct a physically meaningful dual‐level density approximant, because of its non‐negative and range‐restricted properties. First, in terms of the original set of nodal density variables, this study develops a nonlocal nodal density approximant with enhanced smoothness by incorporating the Shepard function into the problem formulation. The density at any node can be evaluated according to the density variables located inside the influence domain of the current node. Second, in the numerical implementation, we present a point‐wise density interpolant via the Shepard function method. The density of any computational point is determined by the surrounding nodal densities within the influence domain of the concerned point. According to a set of generic design variables scattered at field nodes, an alternative solid isotropic material with penalization model is thus established through the proposed dual‐level density approximant. The Lagrangian multiplier method is included to enforce the essential boundary conditions because of the lack of the Kronecker delta function property of MLS meshless shape functions. Two benchmark numerical examples are employed to demonstrate the effectiveness of the proposed method, in particular its applicability in eliminating numerical instabilities. Copyright © 2012 John Wiley & Sons, Ltd.     

基于遗传算法的谐波齿轮可靠性优化设计

谐波齿轮具有优异的特性,在传动系统中的应用越来越广泛.针对谐波齿轮,考虑其设计过程中的各种不确定因素,根据其失效形式建立谐波齿轮的可靠性分析模型.在可靠性理论和优化设计方法的基础上,将可靠性设计与遗传算法结合起来;根据应力-强度干涉理论使用一次二阶矩法对谐波齿轮进行了优化设计;使用矩阵微分技术,分析了设计变量对结构可靠性的影响程度,即灵敏度.并使用蒙特卡洛模拟的方法对可靠性进行了验证.在基本随机参数的前二阶矩已知的情况下,通过计算机程序可以迅速准确地得到谐波齿轮可靠性优化设计信息.数值算例表明,所提出的方法是一种实用、有效的方法.