Enhanced differential evolution-Rao optimization with distance comparison method and its application in optimal sizing of truss structures

A new decision-making approach based on distance measures is established in this study to effectively reduce unnecessary structural analyses in performing truss optimization by metaheuristic algorithms. This approach termed distance comparison (DiC) judges a new design candidate as worth evaluating by using its distance from the best solution. The new candidate solution will be omitted without evaluating it if it is not closer to the best solution than the one being compared. The DiC method is integrated with a novel hybrid metaheuristic based on differential evolution (DE) and the Rao algorithm. In the proposed hybrid strategy, a modified Rao algorithm and an enhanced DE are applied adaptively based on the population diversity to utilize the advantage of each one for a specific stage of the optimization process. Six truss sizing examples with continuous variables, including the 10-bar and 200-bar planar trusses and the 25-bar, 72-bar, 120-bar, and 942-bar spatial trusses, are examined to evaluate the effectiveness of the proposed method. Numerical results demonstrate that DiC significantly reduces the number of structural analyses. Moreover, the performance of the proposed hybrid metaheuristic algorithm conducted on the examples is better than that of some state-of-the-art metaheuristic algorithms.