An adaptive elitist differential evolution for optimization of truss structures with discrete design variables

2016 ◽  
Vol 165 ◽  
pp. 59-75 ◽  
Author(s):  
V. Ho-Huu ◽  
T. Nguyen-Thoi ◽  
T. Vo-Duy ◽  
T. Nguyen-Trang
Keyword(s):  
Differential Evolution ◽  
Truss Structures ◽  
Design Variables ◽  
Discrete Design Variables

Performance evaluation of local surrogate models in differential evolution-based optimum design of truss structures

2017 ◽  
Vol 34 (2) ◽  
pp. 499-547 ◽  
Author(s):  
Eduardo Krempser ◽  
Heder S. Bernardino ◽  
Helio J.C. Barbosa ◽  
Afonso C.C. Lemonge
Keyword(s):  
Surrogate Model ◽  
Structural Engineering ◽  
Optimization Problems ◽  
Surrogate Models ◽  
Truss Structures ◽  
Test Problems ◽  
Local Similarity ◽  
Content Type ◽  
Design Variables ◽  
Discrete Design Variables

Purpose The purpose of this paper is to propose and analyze the use of local surrogate models to improve differential evolution’s (DE) overall performance in computationally expensive problems. Design/methodology/approach DE is a popular metaheuristic to solve optimization problems with several variants available in the literature. Here, the offspring are generated by means of different variants, and only the best one, according to the surrogate model, is evaluated by the simulator. The problem of weight minimization of truss structures is used to assess DE’s performance when different metamodels are used. The surrogate-assisted DE techniques proposed here are also compared to common DE variants. Six different structural optimization problems are studied involving continuous as well as discrete sizing design variables. Findings The use of a local, similarity-based, surrogate model improves the relative performance of DE for most test-problems, specially when using r-nearest neighbors with r = 0.001 and a DE parameter F = 0.7. Research limitations/implications The proposed methods have no limitations and can be applied to solve constrained optimization problems in general, and structural ones in particular. Practical/implications The proposed techniques can be used to solve real-world problems in engineering. Also, the performance of the proposals is examined using structural engineering problems. Originality/value The main contributions of this work are to introduce and to evaluate additional local surrogate models; to evaluate the effect of the value of DE’s parameter F (which scales the differences between components of candidate solutions) upon each surrogate model; and to perform a more complete set of experiments covering continuous as well as discrete design variables.

scholarly journals Reliability-Based Design Optimization of Trusses with Linked-Discrete Design Variables using the Improved Firefly Algorithm

2016 ◽  
Vol 6 (2) ◽  
pp. 964-971
Author(s):  
N. M. Okasha
Keyword(s):  
Design Optimization ◽  
Firefly Algorithm ◽  
Structural Reliability ◽  
Solution Process ◽  
Truss Structures ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Design Variables ◽  
Improved Firefly Algorithm ◽  
Discrete Design Variables

In this paper, an approach for conducting a Reliability-Based Design Optimization (RBDO) of truss structures with linked-discrete design variables is proposed. The sections of the truss members are selected from the AISC standard tables and thus the design variables that represent the properties of each section are linked. Latin hypercube sampling is used in the evaluation of the structural reliability. The improved firefly algorithm is used for the optimization solution process. It was found that in order to use the improved firefly algorithm for efficiently solving problems of reliability-based design optimization with linked-discrete design variables; it needs to be modified as proposed in this paper to accelerate its convergence.

scholarly journals Optimization of steel roof trusses using machine learning-assisted differential evolution

2021 ◽  
Vol 15 (4) ◽  
pp. 99-110
Author(s):  
Nguyen Tran Hieu ◽  
Nguyen Quoc Cuong ◽  
Vu Anh Tuan
Keyword(s):  
Differential Evolution ◽  
Computational Cost ◽  
Differential Evolution Algorithm ◽  
Truss Structures ◽  
De Algorithm ◽  
Design Variables ◽  
Evolution Algorithm ◽  
Structural Engineers ◽  
Steel Trusses ◽  
Steel Roof

A steel truss is a preferred solution in large-span roof structures due to its good attributes such as lightweight, durability. However, designing steel trusses is a challenging task for engineers due to a large number of design variables. Recently, optimization-based design approaches have demonstrated the great potential to effectively support structural engineers in finding the optimal designs of truss structures. This paper aims to use the AdaBoost-DE algorithm for optimizing steel roof trusses. The AdaBoost-DE employed in this study is a hybrid algorithm in which the AdaBoost classification technique is used to enhance the performance of the Differential Evolution algorithm by skipping unnecessary fitness evaluations during the optimization process. An example of a duo-pitch steel roof truss with a span of 24 meters is carried out. The result shows that the AdaBoost-DE achieves the same optimal design as the original DE algorithm, but reduces the computational cost by approximately 36%.

Combined size and shape optimization of truss structures using subset simulation optimization

2018 ◽  
Vol 233 (7) ◽  
pp. 2455-2477 ◽  
Author(s):  
Yuan-Zhuo Ma ◽  
Hong-Shuang Li ◽  
Kong-Fah Tee ◽  
Wei-Xing Yao
Keyword(s):  
Simulation Optimization ◽  
Truss Structures ◽  
Shape Design ◽  
Conditional Sampling ◽  
Subset Simulation ◽  
Design Constraints ◽  
Size And Shape ◽  
Design Variables ◽  
Discrete Design Variables ◽  
Metropolis Hasting Algorithm

This paper presents an approach to solve the combined size and shape design optimization problems using recently developed subset simulation optimization for both continuous and discrete design variables. Except for the componentwise Metropolis–Hasting algorithm, a recently developed adaptive conditional sampling algorithm is also employed as an alternative approach for generating new conditional samples (candidate designs) for each simulation level, which enhances the accuracy and stability of the optimization process. Besides, a double-criterion sorting algorithm is used to handle the design constraints and integrate them in the generation of conditional samples during the Markov Chain Monte Carlo simulation, and the inverse transform method is employed to deal with the discrete design variables. Totally, four numerical examples are considered, including a 15-bar 2D truss, an 18-bar 2D truss, a 39-bar 3D truss and a truss-type landing gear of an unmanned aerial vehicle. The optimal designs obtained from subset simulation optimization using either the componentwise Metropolis–Hasting algorithm or the adaptive conditional sampling algorithm succeed in substantially reducing the weights of the truss-type structures under design constraints in terms of the member stress, the Euler buckling and the nodal displacement. The computational results indicate the proposed method can be taken as an alternative tool for structural optimization design on truss structures when involving the combined size and shape design.

Differential Evolution Applied to the Design of a Three-Dimensional Vehicular Structure

2007 ◽  
Author(s):  
Felipe Antonio Chegury Viana ◽  
Fernando Ce´sar Gama de Oliveira ◽  
Jose Antonio Ferreria Borges ◽  
Valder Steffen
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Three Dimensional ◽  
Differential Evolution Algorithm ◽  
Basic Algorithm ◽  
Present Contribution ◽  
Finite Element Software ◽  
Design Variables ◽  
Numerical Noise ◽  
Discrete Design Variables

The purpose of this paper is to demonstrate the application of Differential Evolution to a realistic design optimization test problem. The present contribution regards the improvements implemented to the original basic algorithm as well as the application of a new algorithm for dealing with the unique challenges associated with real world optimization problems. The selected example is a three-dimensional vehicular structure optimization problem modeled using the commercial Finite Element software ANSYS® that has a combination of continuous and discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the Differential Evolution algorithm is able to find the optimum design for the proposed problem. The algorithm is robust in the sense that it is capable of dealing with the numerical noise involved in the modeling of the system and to manipulate discrete design variables, accordingly.

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