A Distributed Genetic Algorithm with Migration for the Design of Composite Laminate Structures

This paper describes the development of a general Fortran 90 framework for the solution of composite laminate design problems using a genetic algorithm (GA). The initial Fortran 90 module and package of operators result in a standard genetic algorithm (sGA). The sGA is extended to operate on a parallel processor, and a migration algorithm is introduced. These extensions result in the distributed genetic algorithm with migration (dGA). The performance of the dGA in terms of cost and reliability is studied and compared to a sGA baseline, using two types of composite laminate design problems. The nondeterminism of GAs and the migration and dynamic load balancing algorithm used in this work result in a changed (diminished) workload, so conventional measures of parallelizability are not meaningful. Thus, a set of experiments is devised to characterize the run time performance of the dGA.

[1]  L. Darrell Whitley,et al.  Serial and Parallel Genetic Algorithms as Function Optimizers , 1993, ICGA.

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

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

[4]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[5]  Dana S. Richards,et al.  Punctuated Equilibria: A Parallel Genetic Algorithm , 1987, ICGA.

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

[7]  Frank Hoffmeister,et al.  Scalable Parallelism by Evolutionary Algorithms , 1991 .

[8]  Richard M. Friedberg,et al.  A Learning Machine: Part I , 1958, IBM J. Res. Dev..

[9]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

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

[11]  Raphael T. Haftka,et al.  Optimization of composite laminates , 1993 .

[12]  Thomas Bäck,et al.  Selective Pressure in Evolutionary Algorithms: A Characterization of Selection Mechanisms , 1994, International Conference on Evolutionary Computation.

[13]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  John J. Grefenstette,et al.  How Genetic Algorithms Work: A Critical Look at Implicit Parallelism , 1989, ICGA.

[16]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[17]  Thomas Bäck,et al.  Extended Selection Mechanisms in Genetic Algorithms , 1991, ICGA.

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

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

[20]  R. Haftka,et al.  Elements of Structural Optimization , 1984 .

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

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

[23]  Layne T. Watson,et al.  Derivative based approximation for predicting the effect of changes in laminate stacking sequence , 1996 .

[24]  Martina Gorges-Schleuter,et al.  ASPARAGOS An Asynchronous Parallel Genetic Optimization Strategy , 1989, ICGA.