The increase in global environmental concerns as well as advancement of computational tools and methods have had significant impacts on the way in which buildings are being designed. Building professionals are increasingly expected to improve energy performance of their design. To achieve a high level of energy performance, multidisciplinary simulation-based optimization can be utilized to help designers in exploring more design alternatives and making informed decisions. Because of the high complexity in setting up a building model for multi-objective design optimization, there is a great demand of utilizing and integrating the advanced modeling and simulation technologies, including BIM, parametric modeling, cloud-based simulation, and optimization algorithms, as well as a new user interface that facilitates the setup of building parameters (decision variables) and performance fitness functions (design objectives) for automatically generating, evaluating, and optimizing multiple design options. This study presents an integrated framework for Building Information Modeling (BIM)-based Performance Optimization (BPOpt). This framework enables designers to explore design alternatives using a visual programming interface, while assessing the environmental performance of the design models to search for the most appropriate design alternatives. BPOpt integrates the rich information stored in parametric BIM with building performance simulation tools to make performance optimization more accessible in the process of design. This framework uses evolutionary multi-objective optimization to explore the design space and provides a set of Pareto Optimal solutions to the designers. Using this framework, multiple competing objective functions such as construction and operation costs and environmental performance can be studied and a potential set of solutions can be presented. The BPOpt framework is developed by systematic integration of: 1) Parametric BIM-based Energy Simulation (PBES); 2) Parametric BIM-based Daylighting Simulation (PBDS); and 3) Optimo - an open-source Multi-Objective Optimization (MOO) in a visual programming interface tool, developed as part of this research, to provide efficient design space exploration for achieving high-performance buildings. This dissertation describes the prototype development and validation of PBES, PBDS, and Optimo, tools for BPOpt. Furthermore, the present document details the development process of BPOpt and also demonstrates the usefulness of this framework through multiple case studies. The case studies show the use of BPOpt in optimizing multidisciplinary conflicting criteria such as minimizing the annual energy cost while maximizing the appropriate daylighting level for the building models.
