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

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

scholarly journals CONSTRAINT ROBUST PORTFOLIO SELECTION BY MULTIOBJECTIVE EVOLUTIONARY GENETIC ALGORITHM

2012 ◽  
pp. 57-62
Author(s):  
S.K. MISHRA ◽  
G. PANDA ◽  
S. MEHER ◽  
R. MAJHI
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Selection ◽  
Critical Issue ◽  
Total Risk ◽  
Cardinality Constraints ◽  
Real World Data ◽  
Robust Portfolio ◽  
Total Profit ◽  
Non Gaussian ◽  
Nondominated Sorting

The problem of portfolio selection is a very challenging problem in computational finance and has received a lot of attention in last few decades. Selecting an asset and optimal weighting of it from a set of available assets is a critical issue for which the decision maker takes several aspects into consideration. Different constraints like cardinality constraints, minimum buy in thresholds and maximum limit constraint are associated with assets selection. Financial returns associated are often strongly non-Gaussian in character, and exhibit multivariate outliers. Taking these constraints into consideration and with the presence of these outliers we consider a multi-objective problem where the percentage of each available asset is so selected that the total profit of the portfolio is maximized while total risk is minimized. Nondominated Sorting Genetic Algorithm-II is used for solving this multiobjective portfolio selection problem. Performance of the proposed algorithm is carried out by performing different numerical experiments using real-world data.

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