A hybrid method for fast and robust topology optimization

To accelerate the convergence rate and obtain robust optimal results with clear profiles of structural topologies, this paper proposes a hybrid multi-population genetic algorithm (MPGA) and bi-directional evolutionary structural optimization (BESO) method for structural topology optimization. Each element in the design domain is treated as an individual and the elemental sensitivity is taken as the fitness function of one individual. Based on these treatments, MPGA operators, including crossover, mutation, migration and selection, are modified to adapt to compliance minimization problems. Additionally, some key parameters are controlled to guarantee a convergent solution and to solve the structural unconnectivity problem. A case is used to verify the effectiveness and efficiency of the proposed method. The numerical results show that the proposed method is efficient, and compared with the BESO method and combined simple genetic algorithm and BESO method, the proposed method provides a powerful ability in searching for better robust solutions and improving convergence speed.