E-Moga: A General Purpose Platform for Multi Objective Genetic Algorithm Running on Cuda

Abstract

The Multi Objective Genetic Algorithm is the process of simultaneously optimizing two or more conflicting objectives subject to certain constraints. Compute Unified Device Architecture (CUDA) is a general purpose parallel computing architecture, with a new parallel programming model and instruction set architecture, which uses the parallel compute engine in NVIDIA GPUs to solve many complex computational problems in a more efficient way than on a CPU This thesis considers the development of an Enhanced Multi Objective Genetic Algorithm (E-MOGA) platform that runs on Compute Unified Device Architecture (CUDA) hardware as a general purpose tool that can solve conflict optimization problems. This tool demonstrates significant speed gains using affordable, scalable and commercially available hardware. The objectives of the research are: to enhance the general purpose Multi Objective Genetic Algorithm (MOGA) to be ready for execution on CUDA by parallelizing the time consuming parts, to test the performance of the enhanced MOGA on many testing cases, to test the quality of the result of the enhanced MOGA and to study ii