Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation

Alexander E.I Brownlee,Jonathan A Wright

doi:10.1016/j.asoc.2015.04.010

Abstract

Reducing building energy demand is a crucial part of the global response to climate change, and evolutionary algorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool for this task. Further uptake of EAs in this industry is hindered by BPS being computationally intensive: optimisation runs taking days or longer are impractical in a time-competitive environment. Surrogate fitness models are a possible solution to this problem, but few approaches have been demonstrated for multi-objective, constrained or discrete problems, typical of the optimisation problems in building design. This paper presents a modified version of a surrogate based on radial basis function networks, combined with a deterministic scheme to deal with approximation error in the constraints by allowing some infeasible solutions in the population. Different combinations of these are integrated with Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and applied to three instances of a typical building optimisation problem. The comparisons show that the surrogate and constraint handling combined offer improved run-time and final solution quality. The paper concludes with detailed investigations of the constraint handling and fitness landscape to explain differences in performance.

Highlights

The building and construction industry is one which offers large potential for reduced environmental impact and improved sustainability
This means that improvements in the building design process to aid designers in reducing building carbon emissions are an important area of research
This paper explores the use of radial basis function networks (RBFN) to reduce the number of calls to the full building performance simulation, by approximating objective and constraint values

Summary

Introduction

The building and construction industry is one which offers large potential for reduced environmental impact and improved sustainability. Buildings are a large contributor to world carbon emissions, due to their heating, cooling and lighting energy demands (for example, over 50% of UK carbon emissions are related to building energy consumption [1]). Driven by expectation and building function, is for each individual building to be designed afresh, losing the benefit of mass-production where a single optimisation process applies to thousands or millions of units. This means that improvements in the building design process to aid designers in reducing building carbon emissions are an important area of research.

Methods

Results

Conclusion