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.

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.

Design of Lightweight Composite Structures: A Parameter Free Structural Optimization Approach

2012 ◽  
Vol 504-506 ◽  
pp. 1391-1396
Author(s):  
Michael Fischer ◽  
Helmut Masching ◽  
Matthias Firl ◽  
Kai Uwe Bletzinger
Keyword(s):  
Finite Element ◽  
Structural Optimization ◽  
Industrial Application ◽  
Composite Structures ◽  
Large Scale ◽  
Finite Element Models ◽  
Optimization Approach ◽  
Geometrically Nonlinear ◽  
Gradient Based ◽  
Algorithmic Techniques

This contribution presents computational concepts and algorithmic techniques for simulation and gradient-based optimization of geometrically nonlinear and large-scale finite element models of composite structures. Several industrial application examples illustrate the methods, show the applicability to large problems, and prove the high parallel efficiency.

scholarly journals A Novel Hybrid Harmony Search Approach for the Analysis of Plane Stress Systems via Total Potential Optimization

2020 ◽  
Vol 10 (7) ◽  
pp. 2301 ◽  
Author(s):  
Aylin Ece Kayabekir ◽  
Yusuf Cengiz Toklu ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli ◽  
Melda Yücel ◽  
...  
Keyword(s):  
Potential Energy ◽  
Plane Stress ◽  
Harmony Search ◽  
Total Potential Energy ◽  
Metaheuristic Algorithms ◽  
Flower Pollination Algorithm ◽  
Complex Nature ◽  
Total Potential ◽  
Design Variables ◽  
Search Approach

By finding the minimum total potential energy of a structural system with a defined degree of freedoms assigned as design variables, it is possible to find the equilibrium condition of the deformed system. This method, called total potential optimization using metaheuristic algorithms (TPO/MA), has been verified on truss and truss-like structures, such as cable systems and tensegric structures. Harmony Search (HS) algorithm methods perfectly found the analysis results of the previous structure types. In this study, TPO/MA is presented for analysis of plates for plane stress members to solve general types of problems. Due to the complex nature of the system, a novel hybrid Harmony Search (HHS) approach was proposed. HHS is the hybridization of local search phases of HS and the global search phase of the Flower Pollination Algorithm (FPA). The results found via HHS were verified with the finite element method (FEM). When compared with classical HS, HHS provides smaller total potential energy values, and needs less iterations than other new generation metaheuristic algorithms.

Structural Optimization of Composite Panel with Lamination Parameters and Equivalent Stiffness Laminate Method

2014 ◽  
Vol 496-500 ◽  
pp. 429-435
Author(s):  
Xiao Ping Zhong ◽  
Peng Jin
Keyword(s):  
Genetic Algorithm ◽  
Structural Optimization ◽  
Composite Structure ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Composite Panel ◽  
Analysis Tool ◽  
Equivalent Stiffness ◽  
Lamination Parameters ◽  
Design Variables

Firstly, a two-level optimization procedure for composite structure is investigated with lamination parameters as design variables and MSC.Nastran as analysis tool. The details using lamination parameters as MSC.Nastran input parameters are presented. Secondly, with a proper equivalent stiffness laminate built to substitute for the lamination parameters, a two-level optimization method based on the equivalent stiffness laminate is proposed. Compared with the lamination parameters-based method, the layer thicknesses of the equivalent stiffness laminate are adopted as continuous design variables at the first level. The corresponding lamination parameters are calculated from the optimal layer thicknesses. At the second level, genetic algorithm (GA) is applied to identify an optimal laminate configuration to target the lamination parameters obtained. The numerical example shows that the proposed method without considering constraints of lamination parameters can obtain better optimal results.

Trajectory Optimization Applied to Space Rendezvous

2013 ◽  
Vol 756-759 ◽  
pp. 3466-3470
Author(s):  
Xu Min Song ◽  
Qi Lin
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Optimization Method ◽  
Nonlinear Constraints ◽  
Design Variables ◽  
Adaptive Simulated Annealing ◽  
Simulation Results

The trajcetory plan problem of spece reandezvous mission was studied in this paper using nolinear optimization method. The optimization model was built based on the Hills equations. And by analysis property of the design variables, a transform was put forward , which eliminated the equation and nonlinear constraints as well as decreaseing the problem dimensions. The optimization problem was solved using Adaptive Simulated Annealing (ASA) method, and the rendezvous trajectory was designed.The method was validated by simulation results.

Sign in / Sign up

Export Citation Format

Share Document