Sizing, geometry and topology optimization of trusses via force method and genetic algorithm

Rahami, H.; Kaveh, A.; Gholipour, Yaghob

doi:10.1016/j.engstruct.2008.01.012

Cited by 167 publications

(70 citation statements)

References 22 publications

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“…For just the size problem the resulting weight of 2491kg is comparable to the 2540kg of Sivakumar et al (2004) and the 2474kg of Meesomklin (2001). For the simultaneous optimization approach, the GA's result of 1230kg compares well to those of 1282kg and 1235kg obtained by Tang et al (2005) and Rahami et al (2008) These comparisons indicate that the algorithm selected for this study provides reasonable results. Therefore, the algorithm can be regarded as an average performing optimization routine which makes it eligible for being used in a quantitative comparison study.…”

Section: -Bar Trusssupporting

confidence: 63%

A Quantitative Comparison Between Size, Shape, Topology and Simultaneous Optimization for Truss Structures

Muller

Klashorst

2017

Lat. Am. j. solids struct.

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Section: -Bar Trusssupporting

confidence: 63%

A Quantitative Comparison Between Size, Shape, Topology and Simultaneous Optimization for Truss Structures

Muller

Klashorst

2017

Lat. Am. j. solids struct.

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“…The problem with some of the most common and robust optimization algorithms such as Genetic Algorithm, Ant Colony, and Harmony Search, is that they entail a large number of iterations for reaching the optimum solution [1][2][3][4]. This is a significant barrier when applying exact simulations to real life engineering optimization problems.…”

Section: Introductionmentioning

confidence: 99%

“…To investigate the computational cost and convergence rate, three examples (10, 25, and 72-bar truss) are selected from refs [1][2][3][4]. The algorithm was developed using Python programming language and ran on a Centrino, 1.4 GHZ computer.…”

Section: Computational Experimentsmentioning

confidence: 99%

An improved version of Inverse Distance Weighting metamodel assisted Harmony Search algorithm for truss design optimization

Shahbazi

Gholipour

Behnia

2013

Lat. Am. j. solids struct.

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This paper focuses on a metamodel-based design optimization algorithm. The intention is to improve its computational cost and convergence rate. Metamodel-based optimization method introduced here, provides the necessary means to reduce the computational cost and convergence rate of the optimization through a surrogate. This algorithm is a combination of a high quality approximation technique called Inverse Distance Weighting and a metaheuristic algorithm called Harmony Search. The outcome is then polished by a semi-tabu search algorithm. This algorithm adopts a filtering system and determines solution vectors where exact simulation should be applied. The performance of the algorithm is evaluated by standard truss design problems and there has been a significant decrease in the computational effort and improvement of convergence rate.

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“…The weight w and the bias b can be obtained by solving the following optimization problem: is the kernel function [4,5].…”

Section: Genetic Support Vector Regression Algorithmmentioning

confidence: 99%

Sports Economic Activities Income Forecasting based on Genetic Support Vector Regression Algorithm

Guoai¹

2015

TOCSJ

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Abstract:In order to solve the shortcoming of support vector regression algorithm, sports economic activities income forecasting based on genetic support vector regression algorithm is presented in this paper. As the kernel parameter, insensitive loss parameter and penalty parameter have a great influence on the forecasting performance of support vector regression algorithm, genetic algorithm is used to perform the parameters optimization of support vector regression algorithm simultaneously in this study. The experimental results show that the sports economic activities income forecasting results of genetic support vector regression model trained by the four-dimension training samples are more excellent than those of genetic support vector regression model trained by the three-dimension training samples, and the sports economic activities income forecasting ability of genetic support vector regression model trained by the fourdimension training samples is better than that of support vector regression model trained by the four-dimension training samples. Thus, genetic support vector regression algorithm has a good application prospect in sports economic activities income forecasting.

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Sizing, geometry and topology optimization of trusses via force method and genetic algorithm

Cited by 167 publications

References 22 publications

A Quantitative Comparison Between Size, Shape, Topology and Simultaneous Optimization for Truss Structures

A Quantitative Comparison Between Size, Shape, Topology and Simultaneous Optimization for Truss Structures

An improved version of Inverse Distance Weighting metamodel assisted Harmony Search algorithm for truss design optimization

Sports Economic Activities Income Forecasting based on Genetic Support Vector Regression Algorithm

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