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


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Virginia Tech


This thesis 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 an 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.

The migration algorithm is found to diminish the normalized cost and improve the reliability of a GA optimization run. An effective linear speedup for constant work is achieved, and the dynamic load balancing algorithm with distributed control and token ring termination detection yield improved run time performance.



genetic algorithms, parallel computation, distributed control, composite laminate design