This dissertation presents an exhaustive comparative study on optimization in structural acoustics. A combination of a commercially available finite element software package and additional user-written programs is used to modify the shape of a structure. This is done iteratively and without manual intervention to achieve significant improvements of the objective function. The optimization process continues automatically until the predefined maximum number of function evaluations is reached. The design variables are the structure's local geometry modification values at selected surface key-points. The objective of the optimization includes the minimization of the root mean square level of structure borne sound (a general measure of the vibrational sensitivity of a structure). In addition, the structural mass remains constant and the allowable ranges of design variable values are restricted by prescribed upper and lower limits. The optimization procedure is tested on the finite element model of a rectangular plate made of steel. Twelve different optimization methods are tested against each others. These methods are considered either as approximate or exact. The approximate optimization methods use either an approximated value of objective function, e.g., hybrid design of experiments and hybrid neural networks, or the approximated values of the first and the second derivatives of the objective function, e.g., method of feasible directions, sequential quadratic programming method, Newton method, limited memory Broyden-Fletcher-Goldfarb-Shanno method for bound constrained problems, method of moving asymptotes, mid-range multi- oints method and controlled random search method. The exact optimization methods, e.g., genetic algorithms, tabu search and simulated annealing, are derivative-free methods and they use the exact value of objective function. Furthermore, a statistical approach is followed for the comparison of methods. Advantages and disadvantages of each optimization algorithm are reported in details. The rate of convergence (a measure of optimization speed) and the robustness level of each optimization method are evaluated. Some optimization methods are classified as fast, medium and slow. Method of moving asymptotes and mid-range multi-points method are introduced as the fastest methods. Finally, it is experienced that the use of e ective structural-acoustic analysis methods can drastically reduce the total optimization time. If the powerful optimization methods become equipped with effective (fast and reliable) structural acoustic analysis methods, then they can present more desirable optimization results in a shorter period of computation time. In this case, they can even be considered as a suitable replacement for the complex and the multi-stages hybrid optimization algorithms.

This dissertation presents an exhaustive comparative study on optimization in structural acoustics. A combination of a commercially available finite element software package and additional user-written programs is used to modify the shape of a structure. This is done iteratively and without manual intervention to achieve significant improvements of the objective function. The optimization process continues automatically until the predefined maximum number of function evaluations is reached. The design variables are the structure's local geometry modification values at selected surface key-points. The objective of the optimization includes the minimization of the root mean square level of structure borne sound (a general measure of the vibrational sensitivity of a structure). In addition, the structural mass remains constant and the allowable ranges of design variable values are restricted by prescribed upper and lower limits. The optimization procedure is tested on the finite element model of a rectangular plate made of steel. Twelve different optimization methods are tested against each others. These methods are considered either as approximate or exact. The approximate optimization methods use either an approximated value of objective function, e.g., hybrid design of experiments and hybrid neural networks, or the approximated values of the first and the second derivatives of the objective function, e.g., method of feasible directions, sequential quadratic programming method, Newton method, limited memory Broyden-Fletcher-Goldfarb-Shanno method for bound constrained problems, method of moving asymptotes, mid-range multi- oints method and controlled random search method. The exact optimization methods, e.g., genetic algorithms, tabu search and simulated annealing, are derivative-free methods and they use the exact value of objective function. Furthermore, a statistical approach is followed for the comparison of methods. Advantages and disadvantages of each optimization algorithm are reported in details. The rate of convergence (a measure of optimization speed) and the robustness level of each optimization method are evaluated. Some optimization methods are classified as fast, medium and slow. Method of moving asymptotes and mid-range multi-points method are introduced as the fastest methods. Finally, it is experienced that the use of e ective structural-acoustic analysis methods can drastically reduce the total optimization time. If the powerful optimization methods become equipped with effective (fast and reliable) structural acoustic analysis methods, then they can present more desirable optimization results in a shorter period of computation time. In this case, they can even be considered as a suitable replacement for the complex and the multi-stages hybrid optimization algorithms.