Investigation of Simulated Annealing Applied to Structural Optimization Problems

摘要

This thesis investigates the feasibility of using Simulated Annealing (SA) instructural optimization problems. The investigation involves solving benchmark structural optimization problems with an SA algorithm, and comparing its solutions to those found by four other optimizers. Overall, the analysis shows that SA has limited applicability in structural optimization. Two primary factors were found to adversely impact the performance of the SA algorithm in these problems. These factors are high dimensionality, and high levels of constraint. The difficulty involved in solving these problems with a random search increases exponentially with the number of dimensions. The number, and non-linearity, of the constraints also have an appreciable effect on the success of the algorithm. A Measure of Complexity was created to quantify the combined effect of dimensionality and level of constraint. This measure can be used to predict the applicability of the SA algorithm in optimizing a given system of non-linear equations. Stochastic processes, Non-linear programming, Mathematical programming, Simulated annealing, Non-convex programming.