Heuristic optimization in design

This paper presents a workflow called the ‘heuristic optimization workflow’ that integrates Octopus, a Multi-Objective Optimization (MOO) engine with Grasshopper3D, a parametric modeling tool, and multiple simulation software. It describes a process that enables the designer to integrate disparate domains via Octopus and complete a feedback loop with the developed interactive, real-time visualization tools. A retrospective design of the Bow Tower in Calgary is used as a test case to study the impact of the developed workflow and tools, as well as the impact of MOO on the performance of the solutions. Seven optimization runs over two different parametric iterations were conducted with the aim of increasing the Floor Area Ratio (FAR) and thus financial profit, average daylight factor, views score, and decreasing the shaded area and the glare produced by the building. The overall workflow makes MOO-based results more accessible to designers and encourages a more interactive ‘heuristic’ exploration of various geometric and topological trajectories. The workflow also reduces design decision uncertainty, design cycle latency through the incorporation of a feedback loop between geometric models and their associated quantitative data and the exploration of trade-offs of multiple solutions all within one platform.