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.

A method is presented for the prediction of optimal configurations for two-materiai composite continuum structures. In the model for this method, both local properties and topology for the stiffer of the two materials are to be predicted. The proper- ties of the second, less stiff material are specified and remain fixed. At the start of the procedure for computational solution, material composition of the structure is represented as a pure mixture of the two materials. This design becomes modified in subsequent steps into a form comprised of a skeleton of concentrated stiffer ma- terial, together with a nonoverlapping distribution of the second material to fill the original domain. Computational solutions are presented for two example design problems. A comparison among solutions for different ratios of stiffness between the two materials gives an indication of how the distribution of concentrated stiffer material varies with this factor. An example is presented as well to show how the method can be used to predict an efficient layout for rib-reinforcement of a stamped sheet metal panel.

An evolutional system design methodology for generating conceptual moving structural designs by computer is proposed in which multiobjective optimization strategies are used. In this methodology, operators for evolution are defined as factors for causing evolution, so that actions by these operators on prior design solutions will generate new design solutions. Here, a number of environments are prepared, each of which has different evaluative functions expressed as multiobjective optimization problems. The evolution of design solutions thus occurs in a variety of environments. A given environment can be considered as imposing a set of criteria in which specific abilities are crucial or advantageous. Using a variety of environments to generate solutions has advantages: (1) a broader range of solutions is obtained, and (2) by moving a solution generated in one environment to a different one, additional “species” of solutions can be generated to good advantage. The proposed methodology is applied to computer-generated moving structural systems consisting of arms and joints, as well as the data for moving these structures.

Advances in computer technology and performance allow researchers to pose useful optimization problems that were previously too large for consideration. For example, NASA Langley Research Center is investigating the large structural optimization problems that arise in aircraft design. The total number of design variables and constraints for these nonlinear optimization problems is now an order of magnitude larger than anything previously reported. To find solutions in a reasonable amount of time, a coarse-grained parallel-processing algorithm is recommended. This paper studies the effects of problem size on sequential and parallel versions of this algorithm.