Improved genetic algorithm for multidisciplinary optimization of composite laminates

We suggest new approaches to reduce the number of fitness function evaluations in genetic algorithms (GAs) applied to multidisciplinary optimization of composite laminates. In the stacking sequence design of laminated structures, the design criteria are classified into two groups, which are layer combination dependent criteria and layer sequence dependent criteria. The memory approach is employed to lessen the number of fitness function evaluations for the identical design individuals that appear during the search. The permutation operator with local learning or random shuffling is applied to the same design individual to improve the fitness for layer sequence dependent criterion, while maintaining the same performance for layer combination dependent criterion. The numerical efficiency of the present method is validated by the sample problem of weight minimization of composite laminated plate under multiple design constraints.

[1]  Nozomu Kogiso,et al.  Genetic algorithms with local improvement for composite laminate design , 1993 .

[2]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[3]  Alain Vautrin,et al.  Weight minimization of composite laminated plates with multiple constraints , 2003 .

[4]  Alain Vautrin,et al.  Simultaneous optimization of composite structures considering mechanical performance and manufacturing cost , 2004 .

[5]  Richard J. Povinelli Improving Computational Performance of Genetic Algorithms: A Comparison of Techniques , 2000 .

[6]  Akira Todoroki,et al.  Permutation genetic algorithm for stacking sequence design of composite laminates , 2000 .

[7]  Ching-Fang Liaw,et al.  A hybrid genetic algorithm for the open shop scheduling problem , 2000, Eur. J. Oper. Res..

[8]  R. Haftka,et al.  Improved genetic algorithm for minimum thickness composite laminate design , 1995 .

[9]  Dimitris A. Saravanos,et al.  Multiobjective shape and material optimization of composite structures including damping , 1990 .

[10]  Abdelghani Saouab,et al.  Coupled compression RTM and composite layup optimization , 2003 .

[11]  Layne T. Watson,et al.  COMPOSITE LAMINATE DESIGN OPTIMIZATION BY GENETIC ALGORITHM WITH GENERALIZED ELITIST SELECTION , 2001 .

[12]  R. Michalski Understanding the Nature of Learning: Issues and Research Directions , 1985 .

[13]  Nielen Stander,et al.  The genetic algorithm applied to stiffness maximization of laminated plates: review and comparison , 1998 .

[14]  S. Vel,et al.  MULTI-OBJECTIVE OPTIMIZATION OF FIBER REINFORCED COMPOSITE LAMINATES FOR STRENGTH, STIFFNESS AND MINIMAL MASS , 2006 .

[15]  Daijin Kim,et al.  An accurate COG defuzzifier design using Lamarckian co-adaptation of learning and evolution , 2002, Fuzzy Sets Syst..

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

[17]  Roy L. Johnston,et al.  Implementation of Lamarckian concepts in a Genetic Algorithm for structure solution from powder diffraction data , 2000 .

[18]  Akira Todoroki,et al.  Stacking sequence optimization by a genetic algorithm with a new recessive gene like repair strategy , 1998 .

[19]  A. J. Morris,et al.  A multicriteria objective function optimization scheme for laminated composites for use in multilevel structural optimization schemes , 1987 .

[20]  H. A. Eschenauer,et al.  Multidisciplinary design of composite aircraft structures by lagrange , 1992 .

[21]  Christine M. Anderson-Cook,et al.  A genetic algorithm with memory for mixed discrete–continuous design optimization , 2003 .

[22]  Alain Vautrin,et al.  Multiconstraint Optimization of Composite Structures Manufactured by Resin Transfer Molding Process , 2005 .

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

[24]  Nozomu Kogiso,et al.  Design of Composite Laminates by a Genetic Algorithm with Memory , 1994 .

[25]  S. Chatterjee,et al.  Genetic algorithms and traveling salesman problems , 1996 .