G. Tregonning, S. Barr, R. Dawson, R. Ranjan
Future urban development must address climate-related risks, increasing populations and multiple sustainability objectives. This includes reducing development on green space; reducing flood risk; reducing urban sprawl; improving access to public transport; prioritising brownfield development and; reducing urban heat island. Decision makers are therefore confronted with the challenge of achieving multi-objective spatial optimization to determine synergies and trade-offs between sustainability objectives and various risks. Multi-objective optimization (MOO) methods are typically adopted within spatial optimization applications due to their ability to provide best possible trade off solutions to multiple objectives. However, the application of such methods within urban planning is limited due to the specialist knowledge required to facilitate the development of MOO frameworks. This paper explores the abilities of evolutionary computing techniques as a method to support multi-objective spatial optimization. The MOSO framework is applied to a number of UK-based case studies, two of which are presented within this paper (Greater Manchester and West Yorkshire). The study compares a set of spatial development plans and pareto-optimal fronts for each region and highlights the capability of multi-objective spatial optimization as a decision support tool within urban planning.
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