Optimization as a design strategy. Considerations based on building simulation-assisted experiments about problem decomposition

TL;DR

This paper explores a decomposition-based optimization method using building simulation to improve micro-urban building design, introducing heuristics and indicators to enhance search efficiency and effectiveness.

Contribution

It develops heuristics and indicators from simulation experiments to optimize block coordinate search in building design, linking structure sharing to search novelty and efficiency.

Findings

Heuristic indicators support efficient search structure design.

Indicators relate structure sharing to search novelty.

Potential integration with genetic algorithms is discussed.

Abstract

In this article the most fundamental decomposition-based optimization method - block coordinate search, based on the sequential decomposition of problems in subproblems - and building performance simulation programs are used to reason about a building design process at micro-urban scale and strategies are defined to make the search more efficient. Cyclic overlapping block coordinate search is here considered in its double nature of optimization method and surrogate model (and metaphore) of a sequential design process. Heuristic indicators apt to support the design of search structures suited to that method are developed from building-simulation-assisted computational experiments, aimed to choose the form and position of a small building in a plot. Those indicators link the sharing of structure between subspaces ("commonality") to recursive recombination, measured as freshness of the search wake and novelty of the search moves. The aim of these indicators is to measure the relative effectiveness of decomposition-based design moves and create efficient block searches. Implications of a possible use of these indicators in genetic algorithms are also highlighted.