Coupling urban cellular automata with ant colony optimization for zoning protected natural areas under a changing landscape

Abstract

Optimal zoning of protected natural areas is important for conserving ecosystems. It is an NP-hard problem which is difficult to solve by using common geographic information system (GIS) functions. Another problem is that existing optimization methods ignore potential land-use dynamics in formulating optimal patterns. This article has developed a new method for solving complicated zoning problems by using ant colony optimization (ACO) techniques. Significant modifications have been made, so that traditional ACO can be extended to the solution of area optimization problems. Two strategies, the single-year coupling strategy and the merging-year coupling strategy, have been proposed to couple urban cellular automata with ACO for zoning protected natural areas under a changing landscape. This proposed method has been tested in the metropolitan region of Guangzhou, China, by using Geographical Simulation and Optimization System (GeoSOS) software. The experiments indicate that the modified ACO can effectively solve this optimization problem without getting stuck in local optima. This method has better performances compared to other traditional methods, such as simulated annealing (SA), iterative relaxation (IR), and density slicing (DS). The use of the best coupling strategy can improve the accumulative utility value of the zoning by 4.3%. Moreover, it is also found that the adoption of the best protection pattern could significantly promote the compactness of future urban forms in the study area.