Genetic Algorithm Trained Counter-Propagation Neural Net in Structural Optimization

2009 ◽  
Author(s):  
A. Iranmanesh ◽  
M. Fahimi
Keyword(s):  
Genetic Algorithm ◽  
Structural Optimization ◽  
Neural Net

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.

scholarly journals Performance of the Modified Dolphin Monitoring Operator for Weight Optimization of Skeletal Structures

2018 ◽  
Author(s):  
Ali Kaveh ◽  
S.R. Hoseini Vaez ◽  
Pedram Hosseini
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Structural Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Harmony Search ◽  
Average Weight ◽  
Weight Optimization ◽  
Swarm Optimization ◽  
Population Dispersion

In this study, the Modified Dolphin Monitoring (MDM) operator is used to enhance the performance of some metaheuristic algorithms. The MDM is a recently presented operator that controls the population dispersion in each iteration. Algorithms are selected from some well-established algorithms. Here, this operator is applied on Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Vibrating Particles System (VPS), Enhanced Vibrating Particles System (EVPS), Colliding Bodied Optimization (CBO) and Harmony Search (HS) and the performance of these algorithms are evaluated with and without this operator on three well-known structural optimization problems. The results show the performance of this operator on these algorithms for the best, the worst, average and average weight of the first quarter of answers.

Structural optimization by genetic algorithm with degeneration (GAd)

2004 ◽  
Vol 35 (5) ◽  
pp. 32-43 ◽  
Author(s):  
Tetsuyuki Takahama ◽  
Setsuko Sakai ◽  
Takumi Ichimura ◽  
Yoshinori Isomichi
Keyword(s):  
Genetic Algorithm ◽  
Structural Optimization

Data-Driven Bi-Objective Genetic Algorithms EvoNN and BioGP and Their Applications in Metallurgical and Materials Domain

2016 ◽  
pp. 346-368 ◽  
Author(s):  
Nirupam Chakraborti
Keyword(s):  
Genetic Algorithm ◽  
Noisy Data ◽  
Prey Type ◽  
Data Driven ◽  
Neural Net ◽  
Predator Prey ◽  
Modeling And Optimization ◽  
Materials Research ◽  
Computational Materials ◽  
Data Driven Modeling

Data-driven modeling and optimization are now of utmost importance in computational materials research. This chapter presents the operational details of two recent algorithms EvoNN (Evolutionary Neural net) and BioGP (Bi-objective Genetic Programming) which are particularly suitable for modeling and optimization tasks pertinent to noisy data. In both the approaches a tradeoff between the accuracy and complexity of the candidate models are sought, ultimately leading to some optimum tradeoffs. These novel strategies are tailor-made for constructing models of right complexity, excluding the non-essential inputs. They are constructed to implement the notion of Pareto-optimality using a predator-prey type genetic algorithm, providing the user with a set of optimum models, out of which an appropriate one can be easily picked up by applying some external criteria, if necessary. Several materials related problems have been solved using these algorithms in recent times and a couple of typical examples are briefly presented in this chapter.

A genetic algorithm encoding for cardinality constraints and automatic variable linking in structural optimization

2008 ◽  
Vol 30 (12) ◽  
pp. 3708-3723 ◽  
Author(s):  
Helio J.C. Barbosa ◽  
Afonso C.C. Lemonge ◽  
Carlos C.H. Borges
Keyword(s):  
Genetic Algorithm ◽  
Structural Optimization ◽  
Cardinality Constraints
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