Abstract This dissertation presents a new algorithm by integrating evolutionary programming (EP), tabu search (TS) and quadratic programming (QP), named the evolutionary-tabu quadratic programming (ETQ) method, to solve the nonconvex economic dispatch problem (NED). This problem involves the economic dispatch with valve-point effects (EDVP), economic dispatch with piecewise quadratic cost function (EDPQ), and economic dispatch with prohibited operating zones (EDPO). EDPV, EDPQ and EDPO are similar problems when ETQ was employed. The problem was solved in two phases, the cost-curve-selection subproblem, and the typical ED solving subproblem. The first phase was resolved by using a hybrid EP and TS, and the second phase by QP. In the solving process, EP with repairing strategy was used to generate feasible solutions, TS was used to prevent prematurity, and QP was used to enhance the performance. Numerical results show that the proposed method is more effective than other previously developed evolutionary computation algorithms.

Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it still costs O(n2) computation time. In our SVM implementation, we can do training with huge data sets in a distributed manner (by breaking the dataset into chunks, then using Message Passing Interface (MPI) to distribute each chunk to a different machine and processing SVM training within each chunk). In addition, we moved the kernel calculation part in SVM classification to a graphics processing unit (GPU) which has zero scheduling overhead to create concurrent threads. In this thesis, we will take advantage of this GPU architecture to improve the classification performance of SVM.