President & CEO
PAN Partners, New York City
Designing high-rise buildings involves various complexities in terms of form and function. During recent years, computational design advancements have been introduced to the building industry to facilitate this process and make the results more accurate and reliable. These tools can be employed for several purposes; one of them is design optimization: the selection of the best element with regard to some criterion from numerous available alternatives.
Nature is a great source of inspiration for optimization methods. There are several categories of algorithms that simulate existing behavior and patterns in nature, one of which is evolutionary algorithms. These can be seen as a massively parallel search method; rather than working on one alternative at a time, they test all the possible solutions simultaneously. Therefore, they can be tremendously helpful in building design optimization.
This presentation is an attempt to optimize a tall building with specific assumptions by means of a genetic algorithm, to illustrate how a genetic algorithm could be utilized to optimize several important objectives of a design simultaneously, including space allocation, form generation, environmental and structural issues. Multi-objective optimization is a very complex procedure. For instance, a building can be in its best possible state for absorbing the appropriate amount of sunlight, but the least optimized in terms of structural performance. This presentation introduces a method of optimization between several variables in a high-rise project, indicating that genetic algorithms could be employed to analyze and solve complex design problems.