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A comparative study of genetic algorithms for the multi-objective optimization of composite stringers under compression loads

Optimization methods are close to become a common task in the design process of many mechanical engineering fields, specially those related with the use of composite materials which offer the flexibility in the design of both the shape and the material properties and so, are very suitable to any optimization process. While nowadays there exist a large number of solution methods for optimization problems there is not much information about which method may be most reliable for a specific problem. Genetic algorithms have been presented as a family of methods which can handle most of engineering problems. However, starting from a common basic set of rules many algorithms which differ slightly from each other have been implemented even in commercial software packages. This work presents a comparative study of three common Genetic Algorithms: Archive-based Micro Genetic Algorithm (AMGA), Neighborhood Cultivation Genetic Algorithm (NCGA) and Non-dominate Sorting Genetic Algorithm II (NSGA-II) considering three different strategies for the initial population. Their performance in terms of solution, computational time and number of generations was compared. The benchmark problem was the optimization of a T-shaped stringer commonly used in CFRP stiffened panels. The objectives of the optimization were to minimize the mass and to maximize the critical buckling load. The comparative study reveals that NSGA-II and AMGA seem the most suitable algorithms for this kind of problem

The authors wish to acknowledge the Ministerio de Ciencia e Innovación for the funding of the Project DPI2009-08048 and particularly to Universitat de Girona for the research grant coded as BR2011/02

Elsevier

Manager: Ministerio de Ciencia e Innovación (Espanya)
Author: Badalló i Cañellas, Pere
Trias Mansilla, Daniel
Marín Hernández, Lorena
Mayugo Majó, Joan Andreu
Date: 2018 June 5
Abstract: Optimization methods are close to become a common task in the design process of many mechanical engineering fields, specially those related with the use of composite materials which offer the flexibility in the design of both the shape and the material properties and so, are very suitable to any optimization process. While nowadays there exist a large number of solution methods for optimization problems there is not much information about which method may be most reliable for a specific problem. Genetic algorithms have been presented as a family of methods which can handle most of engineering problems. However, starting from a common basic set of rules many algorithms which differ slightly from each other have been implemented even in commercial software packages. This work presents a comparative study of three common Genetic Algorithms: Archive-based Micro Genetic Algorithm (AMGA), Neighborhood Cultivation Genetic Algorithm (NCGA) and Non-dominate Sorting Genetic Algorithm II (NSGA-II) considering three different strategies for the initial population. Their performance in terms of solution, computational time and number of generations was compared. The benchmark problem was the optimization of a T-shaped stringer commonly used in CFRP stiffened panels. The objectives of the optimization were to minimize the mass and to maximize the critical buckling load. The comparative study reveals that NSGA-II and AMGA seem the most suitable algorithms for this kind of problem
The authors wish to acknowledge the Ministerio de Ciencia e Innovación for the funding of the Project DPI2009-08048 and particularly to Universitat de Girona for the research grant coded as BR2011/02
Document access: http://hdl.handle.net/2072/320832
Language: eng
Publisher: Elsevier
Rights: Tots els drets reservats
Subject: Carbon fibers
Finite element method
Genetic algorithms
Numerical analysis
Fibres de carboni
Elements finits, Mètode dels
Algoritmes genètics
Anàlisi numèrica
Title: A comparative study of genetic algorithms for the multi-objective optimization of composite stringers under compression loads
Type: info:eu-repo/semantics/article
Repository: Recercat

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