This paper presents an approach to optimal design
of elastic flywheels using an Injection Island Genetic
Algorithm (iiGA), summarizing a sequence of results reported
in earlier publications. An iiGA in combination with a
structural finite element code is used to search for shape
variations and material placement to optimize the Specific
Energy Density (SED, rotational energy per unit weight)
of elastic flywheels while controlling the failure angular
velocity. iiGAs seek solutions simultaneously at different
levels of refinement of the problem representation (and
correspondingly different definitions of the fitness function)
in separate subpopulations (islands). Solutions are sought
first at low levels of refinement with an axi-symmetric
plane stress finite element code for high-speed exploration
of the coarse design space. Next, individuals are injected
into populations with a higher level of resolution that
use an axi-symmetric three-dimensional finite element code
to “fine-tune” the structures. A greatly simplified
design space (containing two million possible solutions)
was enumerated for comparison with various approaches that
include: simple GAs, threshold accepting (TA), iiGAs and
hybrid iiGAs. For all approaches compared for this simplified
problem, all variations of the iiGA were found to be the
most efficient. This paper will summarize results obtained
studying a constrained optimization problem with a huge
design space approached with parallel GAs that had various
topological structures and several different types of iiGA,
to compare efficiency. For this problem, all variations
of the iiGA were found to be extremely efficient in terms
of computational time required to final solution of similar
fitness when compared to the parallel GAs.
