Large-scale structural optimization using metaheuristic algorithms with elitism and a filter strategy

Large-scale structural optimization often requires numerous finite element analyses to assess the feasibility of the derived solutions during the optimization process, which consume most of the computational cost. To enhance the computational efficiency, this study introduces a filter strategy aiming to eliminate the redundant constraint violation evaluations in large-scale structural optimization using metaheuristic algorithms. Based on the solution selection rule, this study separates the metaheuristic algorithms into two categories: replacement and elitism. The filter mechanism founds on elitism of the metaheuristic algorithms and reduces substantially the number of structural analyses without compromising the effectiveness of the optimization algorithms and the constraint handling techniques. This study also defines a parameter, R, to assess the enhancement performance of the computational efficiency improved by the proposed method. Results from both mathematical simulations and two large-scale structural optimization examples using various metaheuristic algorithms demonstrate that the harmony search (HS) leads always to the lowest R value. The R value is less than 0.4 and is even as small as 0.09 for the 942-bar example, which means over 90% of time savings compared with the penalty method and the Deb rule and the quality of the final optimum also does not depend on the value of R.