A Novel Constrained Genetic Algorithm for the Optimization of Active Bar Placement and Feedback Gains in Intelligent Truss Structures

2006 ◽  
pp. 920-927 ◽  
Author(s):  
Wenying Chen ◽  
Shaoze Yan ◽  
Keyun Wang ◽  
Fulei Chu
Keyword(s):  
Genetic Algorithm ◽  
Truss Structures

Damage Identification of Truss Structures Based on Force Method

2015 ◽  
Vol 7 (2) ◽  
pp. 229-244 ◽  
Author(s):  
Nam-Il Kim ◽  
Seunghye Lee ◽  
Namshik Ahn ◽  
Jaehong Lee
Keyword(s):  
Genetic Algorithm ◽  
Numerical Solutions ◽  
Damage Identification ◽  
Truss Structures ◽  
Computationally Efficient ◽  
Equilibrium Equations ◽  
Force Method ◽  
Space Truss ◽  
Reaction Forces ◽  
Micro Genetic Algorithm

AbstractAn computationally efficient damage identification technique for the planar and space truss structures is presented based on the force method and the micro genetic algorithm. For this purpose, the general equilibrium equations and the kinematic relations in which the reaction forces and the displacements at nodes are take into account, respectively, are formulated. The compatibility equations in terms of forces are explicitly presented using the singular value decomposition (SVD) technique. Then governing equations with unknown reaction forces and initial elongations are derived. Next, the micro genetic algorithm (MGA) is used to properly identify the site and extent of multiple damage cases in truss structures. In order to verify the accuracy and the superiority of the proposed damage detection technique, the numerical solutions are presented for the planar and space truss models. The numerical results indicate that the combination of the force method and the MGA can provide a reliable tool to accurately and efficiently identify the multiple damages of the truss structures.

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.

scholarly journals A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

2009 ◽  
Vol 2009 ◽  
pp. 1-28 ◽  
Author(s):  
Tugrul Talaslioglu
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Truss Structures ◽  
Single Population ◽  
Multiple Populations ◽  
Promising Solution ◽  
Feasible Solutions

A new genetic algorithm (GA) methodology, Bipopulation-Based Genetic Algorithm with Enhanced Interval Search (BGAwEIS), is introduced and used to optimize the design of truss structures with various complexities. The results of BGAwEIS are compared with those obtained by the sequential genetic algorithm (SGA) utilizing a single population, a multipopulation-based genetic algorithm (MPGA) proposed for this study and other existing approaches presented in literature. This study has two goals: outlining BGAwEIS's fundamentals and evaluating the performances of BGAwEIS and MPGA. Consequently, it is demonstrated that MPGA shows a better performance than SGA taking advantage of multiple populations, but BGAwEIS explores promising solution regions more efficiently than MPGA by exploiting the feasible solutions. The performance of BGAwEIS is confirmed by better quality degree of its optimal designations compared to algorithms proposed here and described in literature.

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