Abstract
This paper explores the promise of genetic algorithms as a tool for optimization of buildings at a neighborhood scale across the conflicting demands of social, environmental, and economic sustainability. A large urban site in Chicago, Illinois, is selected to test the viability of using a multi criteria genetic algorithm to optimize the potential building mix in a newly planned development. Two variables, the number of buildings of a given use-type and their height, are analyzed against cost functions for social, economic, and environmental objectives. Single-objective algorithms are utilized to optimize the variables individually. A non-dominated genetic sorting algorithm (NGSAII) is then utilized to identify the Pareto-optimal solutions considering the three objectives simultaneously. Single-objective results are found to vary substantially by objective, with different variable values for social, economic, and environmental sustainability. For multi-objective algorithms, the results support Campbell’s notion of the three nodes of sustainability being in conflict. Solutions performing well across economic and environmental objectives were most common. Solutions performing well among environmental and social objectives were less common. Solutions performing well across economic and social performance were rare. This suggests that while economic and environmental conflicts are to some degree resolvable, conflicts between social and either of economic or environmental performance are more difficult to resolve. The failure of any solution to perform well across all three objectives lends credence to the idea of design as a series of trade-offs and that one super optimum solution may not exist. The process provides insights into the trade-offs implicit in the building design and development process and raises questions regarding the balancing of competing sustainability objectives.
Highlights
The 1990s literature on building design reflects a burgeoning recognition that environmental sustainability must play an integral role in the building and urban design process
This paper explores one such approach, a multi-variate genetic algorithm, and its potential for application to complex architecture and urban design-driven sustainable development problems
This paper explores the promise of genetic algorithms as a tool for the optimization of neighborhood-scale problems across the conflicting demands of social, environmental, and economic sustainability
Summary
The 1990s literature on building design reflects a burgeoning recognition that environmental sustainability must play an integral role in the building and urban design process. The nodes of profitability, social justice, and environmental performance have been identified as important components of a “triple bottom line” [1]. Scott Campbell [2] uses a similar set of three subject areas forming the vertices of “the Planner’s Triangle”. Campbell’s model is most interesting in that it is based on the notion that each of these nodes is in conflict with one another, and that the process of planning necessarily represents balancing a trade-off between these completing objectives. Some consider the notion of “trade-offs” as a fundamental element in design. In an article on engineering design published in 1991, Otto and Antonsson [3] wrote:
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