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.
