Spatial multi-objective land use optimization: extensions to the non-dominated sorting genetic algorithm-II

Abstract

A spatial multi-objective land use optimization model defined by the acronym ‘NSGA-II-MOLU’ or the ‘non-dominated sorting genetic algorithm-II for multi-objective optimization of land use’ is proposed for searching for optimal land use scenarios which embrace multiple objectives and constraints extracted from the requirements of users, as well as providing support to the land use planning process. In this application, we took the MOLU model which was initially developed to integrate multiple objectives and coupled this with a revised version of the genetic algorithm NSGA-II which is based on specific crossover and mutation operators. The resulting NSGA-II-MOLU model is able to offer the possibility of efficiently searching over tens of thousands of solutions for trade-off sets which define non-dominated plans on the classical Pareto frontier. In this application, we chose the example of Tongzhou New Town, China, to demonstrate how the model could be employed to meet three conflicting objectives based on minimizing conversion costs, maximizing accessibility, and maximizing compatibilities between land uses. Our case study clearly shows the ability of the model to generate diversified land use planning scenarios which form the core of a land use planning support system. It also demonstrates the potential of the model to consider more complicated spatial objectives and variables with open-ended characteristics. The breakthroughs in spatial optimization that this model provides lead directly to other properties of the process in which further efficiencies in the process of optimization, more vivid visualizations, and more interactive planning support are possible. These form directions for future research.

Keywords:

• spatial land use optimization
• NSGA-II-MOLU
• planning support systems
• land use planning
• multi-objective optimization
• Tongzhou New Town
• China

Acknowledgements

The authors would like to thank Jinyeu Tsou and Jie He for their data support, and Hui Lin and Yee Leung for their helpful comments. We would also like to thank the anonymous reviewers for their meaningful and perceptive comments and suggestions on previous versions of this article.