scholarly journals Critical Evaluation of Metaheuristic Algorithms for Weight Minimization of Truss Structures

2019 ◽  
Vol 5 ◽  
Author(s):  
Aristotelis E. Charalampakis ◽  
George C. Tsiatas
Keyword(s):  
Critical Evaluation ◽  
Metaheuristic Algorithms ◽  
Truss Structures ◽  
Weight Minimization

scholarly journals Large-Scale Truss-Sizing Optimization with Enhanced Hybrid HS Algorithm

2021 ◽  
Vol 11 (7) ◽  
pp. 3270
Author(s):  
Sadik Ozgur Degertekin ◽  
Mohammad Minooei ◽  
Lorenzo Santoro ◽  
Bartolomeo Trentadue ◽  
Luciano Lamberti
Keyword(s):  
Structural Optimization ◽  
Large Scale ◽  
Harmony Search ◽  
Metaheuristic Algorithms ◽  
Truss Structures ◽  
Nonlinear Constraints ◽  
Weight Minimization ◽  
Design Variables ◽  
Gradient Based ◽  
Nodal Displacements

Metaheuristic algorithms currently represent the standard approach to engineering optimization. A very challenging field is large-scale structural optimization, entailing hundreds of design variables and thousands of nonlinear constraints on element stresses and nodal displacements. However, very few studies documented the use of metaheuristic algorithms in large-scale structural optimization. In order to fill this gap, an enhanced hybrid harmony search (HS) algorithm for weight minimization of large-scale truss structures is presented in this study. The new algorithm, Large-Scale Structural Optimization–Hybrid Harmony Search JAYA (LSSO-HHSJA), developed here, combines a well-established method like HS with a very recent method like JAYA, which has the simplest and inherently most powerful search engine amongst metaheuristic optimizers. All stages of LSSO-HHSJA are aimed at reducing the number of structural analyses required in large-scale structural optimization. The basic idea is to move along descent directions to generate new trial designs, directly through the use of gradient information in the HS phase, indirectly by correcting trial designs with JA-based operators that push search towards the best design currently stored in the population or the best design included in a local neighborhood of the currently analyzed trial design. The proposed algorithm is tested in three large-scale weight minimization problems of truss structures. Optimization results obtained for the three benchmark examples, with up to 280 sizing variables and 37,374 nonlinear constraints, prove the efficiency of the proposed LSSO-HHSJA algorithm, which is very competitive with other HS and JAYA variants as well as with commercial gradient-based optimizers.

Total potential energy minimization method in structural analysis considering material nonlinearity

2017 ◽  
Vol 3 (3) ◽  
pp. 129 ◽  
Author(s):  
Rasim Temür ◽  
Gebrail Bekdaş ◽  
Yusuf Cengiz Toklu
Keyword(s):  
Structural Analysis ◽  
Potential Energy ◽  
Total Potential Energy ◽  
Metaheuristic Algorithms ◽  
Truss Structures ◽  
Minimum Potential Energy ◽  
Minimization Method ◽  
Total Potential ◽  
Tlbo Algorithm ◽  
Minimum Potential

Minimum potential energy principle is the basis of the most of the well-known traditional techniques used in the structural analysis. This principle determines the equilibrium conditions of systems with reference to minimization of the sum of the total potential energy of the structure. In traditional applications, this methodology is formulized by using matrix operations. A methodology has been proposed in the last decades for structural analyses based on the idea of using metaheuristic algorithms to obtain minimum potential energy of the structural system instead of following this classical approach. This new method, called “Total Potential Optimization using Metaheuristic Algorithms (TPO/MA)”, has been applied in this paper to truss structures considering linear and nonlinear behavior of the structural material. The metaheuristic method used in this process is teaching-learning based optimization (TLBO) algorithm. The proposed technique is applied on numerical examples and results are compared with other techniques in order to test the efficiency of the proposed method. According to results obtained, TPO/MA method with TLBO algorithm is a feasible technique for the investigated problem.

scholarly journals WEIGHT MINIMIZATION OF TRUSS STRUCTURES WITH SIZING AND LAYOUT VARIABLES USING INTEGRATED PARTICLE SWARM OPTIMIZER

2017 ◽  
Vol 23 (8) ◽  
pp. 985-1001 ◽  
Author(s):  
Ali MORTAZAVI ◽  
Vedat TOĞAN ◽  
Ayhan NUHOĞLU
Keyword(s):  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Truss Structures ◽  
Continuous Variables ◽  
Particle Swarm Optimizer ◽  
Search Spaces ◽  
Optimization Constraints ◽  
Weight Minimization ◽  
Ipso Algorithm

This study investigates the performances of the integrated particle swarm optimizer (iPSO) algorithm in the layout and sizing optimization of truss structures. The iPSO enhances the standard PSO algorithm employing both the concept of weighted particle and the improved fly-back method to handle optimization constraints. The performance of the recent algorithm is tested on a series of well-known truss structures weight minimization problems including mixed design search spaces (i.e. with both discrete and continuous variables) over various types of constraints (i.e. nodal dis­placements, element stresses and buckling criterion). The results demonstrate the validity of the proposed approach in dealing with combined layout and size optimization problems.

scholarly journals Weight Minimization of Spatial Trusses with Genetic Algorithm

2019 ◽  
Vol 1 (1) ◽  
pp. 238-243
Author(s):  
Maksym Grzywiński ◽  
Jacek Selejdak
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Minimization Problem ◽  
Truss Structures ◽  
Shape Design ◽  
Jaya Algorithm ◽  
Finite Element Code ◽  
Simple Formulation ◽  
Weight Minimization ◽  
Design Variables

Abstract A genetic algorithm is proposed to solve the weight minimization problem of spatial truss structures considering size and shape design variables. A very recently developed metaheuristic method called JAYA algorithm (JA) is implemented in this study for optimization of truss structures. The main feature of JA is that it does not require setting algorithm specific parameters. The algorithm has a very simple formulation where the basic idea is to approach the best solution and escape from the worst solution. Analyses of structures are performed by a finite element code in MATLAB. The effectiveness of JA algorithm is demonstrated through benchmark spatial truss 39-bar, and compare with results in references.

Sign in / Sign up

Export Citation Format

Share Document