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

Abstract

Please cite this article as: Badalló, P., Trias, D., Marín, L., Mayugo, J.A., A comparative study of genetic algorithms for the multi-objective optimization of composite stringers under compression loads, Composites: Part B (2012), doi: http://dx.doi.org/10. 1016/j.compositesb.2012.10.037 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.A comparative study of genetic algorithms for the multi-objective optimization of composite stringers under compression loads AbstractOptimization 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 Tshaped 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.

A optimization technique for the composite strut using genetic algorithms

“…Many different factors, such as mass, thermal expansion, buckling load, and natural frequency, were found to must be taken into account [1][2][3][4][5][6]. Badalló et al [7] presented 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) by optimizing a T-shaped stringer. The objectives were to minimize the mass and to maximize the critical buckling load.…”

Damage tolerance optimization of composite stringer run-out under tensile load

“…A Non-dominate Sorting Genetic Algorithm II (NSGA-II) [29] is the variant used to achieve the optimization (implemented in the Optimization Toolbox TM of MATLAB R ⃝ ). In a previous work [30] NSGA-II was determined as one of the most effective algorithms.…”

Multi-objective optimisation of vehicle bodies made of FRP sandwich structures

“…The objective function representing the material cost is modelled according to Equation (5) and considering the assumptions made in Table 6. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 9 (5) where:…”

“…Therefore, there are cases where several objectives need to be defined and considered within the optimisation procedure in which case a conflict situation appears between objectives, meaning that an increased performance in one objective leads to a decreased performance for the others [3]. Several complex optimisation techniques and algorithms have been proposed for solving such multi-objective problems [1,[4][5][6][7][8][9]. The weighted sum approach has been used as an attempt to simplify the problem complexity of finding solutions within multi-objective optimisation problems, where all the objectives functions are summed into a single objective function, giving weight penalties for each of them [1].…”