A hybrid deterministic/stochastic optimization approach for the shape configuration design of structures

Abstract

This paper presents a computer-based shape configuration design methodology to generate optimum design of specified structures satisfying the structural performance requirements and the geometric connectivity of the model. Mathematically, this problem can be categorized as a large-scale, nonconvex and nonlinear problem. The solution methods, grouped into two main categories, deterministic and stochastic approaches, require enormous computational efforts to find global optimum designs, a matter of major importance since many local suboptimal solutions can exist. In this study, two popular methods belonging to each class are examined and compared. The methods understudied are selected as the enumeration technique for deterministic approach and the simulated annealing for the stochastic method. The advantages and disadvantages of each technique are investigated. Using the best properties of each method and an algorithm for phase change between the two, a hybrid global shape optimization approach is formulated. The hybrid method is structured to combine the enumeration method for local minimization process and the simulated annealing for global minimum search phase. The hybrid method can find global optimum designs in a robust and efficient way, contrary to the signle phase solutions examined in this study. To demonstrate the hybrid method and its effectiveness, several design examples are presented.