In this course, we will explore the exciting intersection of architecture and optimization through the lens of metaheuristic and swarm-based algorithms. Students will delve into cutting-edge multi-objective multi-constraint optimization techniques that mimic natural processes, such as genetic algorithms, particle swarm optimization, and ant colony optimization, to solve complex design problems in architecture. By understanding how these algorithms can efficiently navigate the vast design space, students will learn to enhance creativity, efficiency, and sustainability in architectural projects.
By incorporating optimization techniques into the simulation process, students will discover how to fine-tune design parameters to achieve optimal performance outcomes. Through hands-on projects, students will gain practical skills in applying these algorithms to optimize architectural designs and push the boundaries of innovation in the field. Even if you have no prior experience with Python programming, in this course you will gain a thorough understanding of the language as the optimization algorithms are written from the ground up in Python.
Moreover, in this course, an introduction to state-of-the-art machine learning techniques will be provided. Machine learning is revolutionizing architectural design by providing architects with powerful tools to analyze, generate, and optimize design solutions.
