Shape optimization using reproducing kernel particle method and an enriched genetic algorithm

Combining Reproducing Kernel Particle Method (RKPM) with the proposed Multi-Family Genetic Algorithm (MFGA), a novel approach to continuum-based shape optimization problems is brought forward in this paper. Taking full advantage of the features of meshfree method and the merits of MFGA, the new method solves shape optimization problems in such a unique way that remeshing is avoided and particularly the computation burden and errors caused by sensitivity analysis are eliminated completely. The effectiveness, versatility and performance of the proposed approach are demonstrated via three 2-D numerical examples.