An optimization method for dynamics of structures with repetitive component patterns

The occurrence of dynamic problems during the operation of machinery may have devastating effects on a product. Therefore, design optimization of these products becomes essential in order to meet safety criteria. In this research, a hybrid design optimization method is proposed where attention is focused on structures having repeating patterns in their geometries. In the proposed method, the analysis is decomposed but the optimization problem itself is treated as a whole. The model of an entire structure is obtained without modeling all the repetitive components using the merits of the Component Mode Synthesis method. Backpropagation Neural Networks are used for surrogate modeling. The optimization is performed using two techniques: Genetic Algorithms (GAs) and Sequential Quadratic Programming (SQP). GAs are utilized to increase the chance of finding the location of the global optimum and since this optimum may not be exact, SQP is employed afterwards to improve the solution. A theoretical test problem is used to demonstrate the method

An ant colony algorithm for the sequential testing problem under precedence constraints.

We consider the problem of minimum cost sequential testing of a series (parallel) system under precedence constraints that can be modeled as a nonlinear integer program. We develop and implement an ant colony algorithm for the problem. We demonstrate the performance of this algorithm for special type of instances for which the optimal solutions can be found in polynomial time. In addition, we compare the performance of the algorithm with a special branch and bound algorithm for general instances. The ant colony algorithm is shown to be particularly effective for larger instances of the problem