This paper first describes optimal design of elastic flywheels using an Injection Island Genetic Algorithm (iiGA). An iiGA in combination with a finite element code is used to search for shape variations to optimize the Specific Energy Density offlywheels (SED is the rotational energy stored per unit mass). iiGA's seek solutions simultaneously at different levels of refinement of the problem representation (and correspondingly different definitions of the fitness function) in separatesubpopulations (islands). Solutions are sought first at low levels of refinement with an axisymmetric plane stress finite element code for high-speed exploration of the coarse design space. Next, individuals are injected into populations with a higherlevel of resolution that uses an axisymmetric threedimensional finite element model to "fine-tune" the flywheel designs. Solutions found for these various coarse' fitness functions on various nodes are injected into nodes that evaluate the ultimatefitness to be optimized. Allowing subpopulations to explore different regions of the fitness space simultaneously allows relatively robust and efficient exploration in problems for which fitness evaluations are costly. First the paper treats a greatlysimplified case - one for which all two million possible solutions were enumerated, yielding a known global optimum. Then the success and speed of many methods, including several variations of an iiGA, in finding this known global optimum are compared.The iiGA methods always found the global optimum, and the other methods never did. Hybridizing the iiGA with a local search operator and a Threshold Accepting (TA) search at the end of each generation provided the fastest solutions, without sacrificingrobustness. Finally, a problem with a large design space is presented and results are compared for a hybrid iiGA to a parallel GA that uses a topological ring structure. The hybrid iiGA greatly outperforms the topological "ring" GA in terms of fitness and search efficiency for this given problem.
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