A two phase approach based on skeleton convergence and geometric variables for topology optimization using genetic algorithm

This paper introduces a set of skeleton operators for characterizing topologies evolving in a bit-array represented structural topology optimization problem. It is shown that the design generally converges to a stable skeleton fairly early in the optimization process. It is observed that further optimization is more about finding optimal gross shape for the various branches of the converged skeleton and the bit-array representation is not appropriate. A two-phase approach to topology optimization is proposed in which the first phase, where bit-array is used to represent the topology, ends with the detection of stabilization of skeleton, and the second phase proceeds further with the geometry based representation that directly addresses gross variation in shape of the branches of the converged skeleton. Genetic Algorithm has been used for optimization in both the phases. The efficiency and effectiveness of the use of skeleton operators and geometric variables for identification of convergence in the first phase and optimization in the second phase respectively is demonstrated.