Parallel evolutionary optimization with grid computing

90 p.A rising trend in science and engineering is in the utilization of increasingly highfidelity and accurate analysis codes in the design analysis and optimization process. In many application areas such as photonics, electromagnetics, aerospace, biomedical, micro-electro-mechanical systems and coupled-field multidisciplinary system design processes, simulation procedures involving Computational Structural Mechanics (CSM), Computational Fluid Dynamics (CFD) or Computational Electronics and Electromagnetics (CEE) are an important step in the design process. The time taken for these processes generally varies from many minutes to hours or days of supercomputing time. This often leads to high computing costs in the design optimization process, hence a much longer design cycle time to locate a near optimum design solution. This thesis presents a Gridenabled scalable parallel evolutionary framework for solving computationally expensive optimization problems under limited time budget.MASTER OF ENGINEERING (SCE