For constrained optimization problems, it is used to find the feasible region or the feasible region with better objective values. For unconstrained optimization problems, the data mining activities are used to find a smaller search region which contains the global or better local solutions. The activities of classification, association and clustering in data mining are employed to reduce the original design space. It is found in this research that data mining can accomplish this goal easily. The way to reach this goal is to find a smaller search space for gradient-based search methods. An alternative and practical way of thinking is thus proposed in this research to obtain an optimum design which may not be global but is better than most local optimum solutions that can be found by gradient-based search methods. Some computer simulations for a 4DF system are presented to show the improvement of location through use of the iterative method.Īn efficient and practical approach to obtain a better optimum solution for structural optimizationįor many structural optimization problems, it is hard or even impossible to find the global optimum solution owing to unaffordable computational cost. This improved method has higher accuracy and needs only a little more calculation time. An iterative calculation method is presented using the distance from the source to the DF as a weight factor. It is pointed out that using a weight factor of signal strength is not the most ideal method because of the inexact inverse signal strength-distance relation and the inaccurate signal amplitude. An improvement of this method, which takes the distance of source-DF's as a constant, is presented. The location of the optimum source is obtained by using multiple direction finders (DF's) on a spherical surface. An iterative method for obtaining the optimum lightning location on a spherical surfaceĪ brief introduction to the basic principles of an eigen method used to obtain the optimum source location of lightning is presented.
0 kommentar(er)
