Optimizing the Design of Composite Panels using an Improved Genetic Algorithm

1. Abstract Composite materials are combinations of, at least, two organic or inorganic materials, working together to give the composite some desired properties. Composites are highly-used on industrial design [3]. Their light weight make them key elements to reduce weight and direct operating costs in some domains like aeronautics. Genetic algorithms are heuristic stochastic methods that explore a reduced set of tentative solutions, performing a guided search procedure that evaluates few solutions, in several orders of magnitude smaller than the whole search space. The dynamics in genetic algorithms provide optimal (or near-optimal) solutions to complex optimization problems when analytical techniques are not able to calculate them. This paper describes the results of applying several improvements to the standard genetic algorithm to the optimization of a stiffened composite panel subject to a set of shear and axial loads. The performance and characteristics of the proposed configurations are evaluated via nonlinear finite element simulation. The goal is to find the lightest configuration that keeps the principal strains under a given threshold. The proposed improvements significantly reduce both the weight and the number of analyses required for the optimization. 2. Keywords: composite laminate, genetic algorithms, stacking optimization

[1]  C. A. Conceição António,et al.  A hierarchical genetic algorithm with age structure for multimodal optimal design of hybrid composites , 2006 .

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  C. Liang,et al.  Optimum design of fiber-reinforced composite cylindrical skirts for solid rocket cases subjected to buckling and overstressing constraints , 2003 .

[4]  L. Watson,et al.  Genetic Algorithm for Mixed Integer Nonlinear Programming Problems Using Separate Constraint Approximations , 2003 .

[5]  Raphael T. Haftka,et al.  Stacking sequence optimization of simply supported laminates with stability and strain constraints , 1992 .

[6]  Rodolphe Le Riche,et al.  Optimization of composite structures by genetic algorithms , 1995 .

[7]  R. Haftka,et al.  Genetic optimization of two-material composite laminates , 2001 .

[8]  Layne T. Watson,et al.  A DISTRIBUTED GENETIC ALGORITHM WITH MIGRATION FOR THE DESIGN OF COMPOSITE LAMINATE STRUCTURES , 2000, Parallel Algorithms Appl..

[9]  Layne T. Watson,et al.  Improved Genetic Algorithm for the Design of Stiffened Composite Panels , 1994 .

[10]  R. Haftka,et al.  Optimization of laminate stacking sequence for buckling load maximization by genetic algorithm , 1993 .

[11]  Lawrence Davis,et al.  Genetic Algorithms and Simulated Annealing , 1987 .

[12]  Araceli Sanchis,et al.  Hierarchical genetic algorithms for composite laminate panels stress optimisation , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[13]  Marc Schoenauer,et al.  Alternative Random Initialization in Genetic Algorithms , 1997, ICGA.

[14]  Hugo de Garis,et al.  Genetic Programming: Artificial Nervous Systems, Artificial Embryos and Embryological Electronics , 1990, PPSN.

[15]  G. Soremekun Genetic Algorithms for Composite Laminate Design and Optimization , 1997 .