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.     

Optimum spacing design of grillage systems using a genetic algorithm

In this study a genetic algorithm based method is developed for the optimum design of grillage systems. The algorithm not only selects the optimum sections for the grillage elements from a set of standard universal beam sections, but also finds the optimum spacing required for the grillage system. Deflection limitations and allowable stress constraints are considered in the formulation of the design problem. Due to the fact that grillage elements are thin walled sections, warping plays an important role in their design, particularly, when they are subjected to significant torsional loading. The algorithm developed has the flexibility of including or excluding the effect of warping in the design process. Grillage structures are designed for uniformly distributed loading. The optimum spacings are determined both considering and not taking into account the effect of warping in the design. The comparison of the results shows that inclusion of warping in the design process has a significant effect on the optimum spacing.     

Optimum design of structures by an improved genetic algorithm using neural networks

Optimum design of large-scale structures by standard genetic algorithm (GA) makes the computational burden of the process very high. To reduce the computational cost of standard GA, two different strategies are used. The first strategy is by modifying the standard GA, called virtual sub-population method (VSP). The second strategy is by using artificial neural networks for approximating the structural analysis. In this study, radial basis function (RBF), counter propagation (CP) and generalized regression (GR) neural networks are used. Using neural networks within the framework of VSP creates a robust tool for optimum design of structures.     

Optimum design of structures against earthquake by adaptive genetic algorithm using wavelet networks

Optimum design of structures against earthquake is achieved by a modified genetic algorithm. Some features of the simulated annealing are used to control various parameters of the genetic algorithm. To reduce the computational work, a fast wavelet transform is used by which the number of points in the earthquake record is decreased. For this purpose, the record is decomposed into two parts. One part contains the low frequency and the other possesses the high frequency of the record. The low-frequency part is used for dynamic analysis. Then, by using a wavelet network, the dynamic responses of the structures are approximated. By such approximation, the dynamic analysis of the structure is not necessary during the optimisation process. Thus, wavelet neural networks have been employed as a general approximation tool for the time-history dynamic analysis and estimation of the dynamic responses in the process of optimisation. A number of structures are designed for optimal weight against El Centro earthquake and the results are compared with those of the exact approach.     

Optimum load and resistance factor design of steel space frames using genetic algorithm

In this paper, an algorithm is presented for the minimum weight design of steel moment-resisting space frames subjected to American Institute of Steel Construction (AISC) Load and Resistance Factor Design (LRFD) specification. A genetic algorithm (GA) is utilized herein as the optimization method. Design variables which are cross-sectional areas are discrete and are selected from the standard set of AISC wide-flange (W) shapes. The structure is subjected to wind loading in accordance with the Uniform Building Code (UBC) in conjunction with vertical loads (dead and live loads). Displacement and AISC LRFD stress constraints are imposed on the structure. The algorithm is applied to the design of three space frame structures. The designs obtained using AISC LRFD code are compared to those where AISC Allowable Stress Design (ASD) is considered. The comparisons show that the former code results in lighter structures for the examples presented. Received November 15, 1999     

Optimum topology and shape design of prestressed concrete bridge girders using a genetic algorithm

The purpose of this study is to optimize the topology and shape of prestressed concrete bridge girders. An optimum design approach that uses a genetic algorithm (GA) for this purpose is presented. The cost of girders is the optimum design criterion. The design variables are the cross-sectional dimensions of the prefabricated prestressed beams, the cross-sectional area of the prestressing steel and the number of beams in the bridge cross-section. Stress, displacement and geometrical constraints are considered in the optimum design. AASHTO Standard Specifications for Highway Bridges are taken into account when calculating the loads and designing the prestressed beams. A computer program is coded in Visual Basic for this optimization. Many design examples from various applications have been optimized using this program. Several of these examples are presented to demonstrate the efficiency of the algorithm coded in the study.     

Optimum part orientation in Rapid Prototyping using genetic algorithm

Part orientation is an important parameter in the planning of a Rapid Prototyping (RP) process as it directly governs productivity, part quality and cost of manufacturing. This paper reports the design and implementation of a system for obtaining optimum orientation of a part for RP. Developed in a modular fashion, the system comprises of functional modules for CAD model preprocessing, shelling (hollowing), part orientation and optimization. CAD part model in STL format is an input to the system. The oriented CAD model is sliced and hollowed with desired shell thickness. Genetic algorithm based strategy is used to obtain optimum orientation of the parts for RP process. The objective criteria for optimization is considered to be a weighted average of the performance measures such as build time, part quality and the material used in the hollowed model. The developed system has been tested with several case studies considering SLS process.     

Optimum design of steel braced frames considering dynamic soil-structure interaction

Structural and Multidisciplinary Optimization - Recent studies on design optimization of steel frames considering soil-structure interaction have focused on static loading scenarios, and limited...     

Optimum design of steel frames using harmony search algorithm

In this article, harmony search algorithm was developed for optimum design of steel frames. Harmony search is a meta-heuristic search method that has been developed recently. It bases on the analogy between the performance process of natural music and searching for solutions to optimization problems. The objective of the design algorithm is to obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. Strength constraints of AISC load and resistance factor design specification and displacement constraints were imposed on frames. The effectiveness and robustness of harmony search algorithm, in comparison with genetic algorithm and ant colony optimization-based methods, were verified using three steel frames. The comparisons showed that the harmony search algorithm yielded lighter designs.     

Optimum positioning of actuators in tall buildings using genetic algorithm

An effective method for determining the optimal position of actuators in tall buildings using genetic algorithm is introduced through the formulation of a discrete and non-linear optimization problem. The simulation study is carried out for a 16-story building under 18 different earthquake excitations. The effects of different earthquake excitations on the optimal placement of actuators and on the proposed optimization algorithm are thus examined.     

Optimum coordination of overcurrent relay timing using continuous genetic algorithm

Overcurrent relays (OCR) are the major protection devices in a distribution system. To reduce the power outages, mal-operation of the backup relays should be avoided, and therefore, OCR time coordination in power distribution network is a major concern of protection engineer. The OCR time coordination in ring fed distribution networks is a highly constrained optimization problem. The purpose is to find an optimum relay setting to minimize the time of operation of relays and at the same time, to avoid the mal-operation of relays.This paper presents continuous genetic algorithm (CGA) technique for optimum coordination of OCR in a ring fed distribution system. Constraints are incorporated in the fitness function making use of the penalty method. The CGA is inherently faster than binary genetic algorithm (GA) because the chromosomes do not have to be decoded. Also the CGA gives an advantage of requiring less storage than binary GA.     

用于模糊控制器设计的遗传算法研究

将遗传操作用于模糊规则和控制器参数编码,实现输入变量的合理组合、模糊规则的获取和控制器参数的优化,设计者仅需给出一个运行遗传算法(GA)的个体适应度函数。同时将模拟退火算法(SA)用于优化控制器参数,这种GASA混合优化策略在模糊控制器设计中取得了良好的效果。实例表明了算法的有效性。     

Floorplan design problem using improved genetic algorithm

Genetic algorithms (GA) are applicable to many kinds of difficult problems. When a population keeps enough diversity and similarity, GA can obtain good solutions quickly. However, because these often compete with each other, it is difficult to fulfill both of these conditions simultaneously. In this article, taking these into consideration, we propose a new GA for the floorplan design problem, and aimed at improving the efficiency of calculation, the maintenance of the solution’s population diversity, and reduction of the number of parameters. We applied it to two MCNC (originally established as the Microelectronics Center of North Carolina) benchmark problems. The experimental results showed that the proposed method performed better than the existing methods. This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24#x2013;26, 2003     

Optimization of fuzzy rules design using genetic algorithm

Fuzzy rules optimization is a crucial step in the development of a fuzzy model. A simple two inputs fuzzy model will have more than ten thousand possible combinations of fuzzy rules. A fuzzy designer normally uses intuition and trial and error method for the rules assignment. This paper is devoted to the development and implementation of genetic optimization library (GOL) to obtain the optimum set of fuzzy rules. In this context, a fitness calculation to handle maximization and minimization problem is employed. A new fitness-scaling mechanism named as Fitness Mapping is also developed. The developed GOL is applied to a case study involving fuzzy expert system for machinability data selection (Wong SV, Hamouda AMS, Baradie M. Int J Flexi Automat Integr Manuf 1997;5(1/2):79–104). The main characteristics of genetic optimization in fuzzy rule design are presented and discussed. The effect of constraint (rules violation) application is also presented and discussed. Finally, the developed GOL replaces the tedious process of trial and error for better combination of fuzzy rules.     

Optimum energy-based design of BRB frames using nonlinear response history analysis

In this paper, an optimum design method for buckling restrained brace frames subjected to seismic loading is presented. The multi-objective charged system search is developed to optimize costs and damages caused by the earthquake for steel frames. Minimum structural weight and minimum seismic energy which including seismic input energy divided by maximum hysteretic energy of fuse members are selected as two objective functions to find a Pareto solutions that copes with considered preferences. Also, main design constraints containing allowable amount of the inter-story drift and plastic rotation of beam, column members and plastic displacement of buckling restrained braces are controlled. The results of optimum design for three different frames are obtained and investigated by the developed method.     

Optimum design of pitched roof steel frames with haunched rafters by genetic algorithm

The pitched roof steel frames appear to be the simplest structural form used in single storey buildings. However, its design necessitates consideration of many different structural criteria that are required in the design of complex structures. In this paper, a genetic algorithm is used to develop an optimum design method for pitched roof steel frames with haunches for the rafters in the eaves. The algorithm selects the optimum universal beam sections for columns and rafters from the available steel sections tables. Furthermore, it determines the optimum depth of the haunch at the eaves and the length of the haunch required for reaching the most cost-effective form. Formulation of the design problem is based on the elastic design method. The serviceability and the strength constraints are included in the design problem as defined in BS 5950. Furthermore, the overall buckling of columns and rafters in the torsional mode between effective torsional restraints to both flanges is also checked. A pitched roof frame is designed by the algorithm developed to demonstrate its practical application.     

Optimum design of prestressed concrete beams using constrained differential evolution algorithm

This paper is concerned with the cost minimization of prestressed concrete beams using a special differential evolution-based technique. The optimum design is posed as single-objective optimization problem in presence of constraints formulated in accordance with the current European building code. The design variables include geometrical dimensions that define the shape of the cross section and the amount of prestressing steel. A special (μ?+?λ)-constrained differential evolution method is performed in order to solve the optimization problem. Its search mechanism depends on several mutation strategies whereas an archiving-based adaptive tradeoff model is in charge of selecting a specific constraint-handling technique. Finally, numerical examples are included to illustrate the application of the presented approach.     

A control design of robotics using the genetic algorithm

This paper presents a new and practical method for a control design of a robotic system. In general, actuators in robotic systems are set with gears whose characteristics are elastic. Since a state feedback-type digital controller is usually used for such a robotic system, the design of the feedback gain of the controller is important, because undesirable vibrations or an overshoot in responses occur for high gains. Therefore the desired response, the output of a reference model, is designed first, and the feedback gains are determined so that the response will coincide with the desired response, which is an optimization problem. The gradient method works to some extent, but it takes a long time to get a satisfactory result. Thus we applied the genetic algorithm (GA) to this nonlinear optimization problem, which gave the very first convergence. The gains obtained have many useful applications. The results of a simulation are also given. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

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.