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

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.