Abstract
ABSTRACTMulti-objective land allocation (MOLA) can be regarded as a spatial optimization problem that allocates appropriate use to certain land units subjecting to multiple objectives and constraints. This article develops an improved knowledge-informed non-dominated sorting genetic algorithm II (NSGA-II) for solving the MOLA problem by integrating the patch-based, edge growing/decreasing, neighborhood, and constraint steering rules. By applying both the classical and the knowledge-informed NSGA-II to a simulated planning area of 30 × 30 grid, we find that: when compared to the classical NSGA-II, the knowledge-informed NSGA-II consistently produces solutions much closer to the true Pareto front within shorter computation time without sacrificing the solution diversity; the knowledge-informed NSGA-II is more effective and more efficient in encouraging compact land allocation; the solutions produced by the knowledge-informed have less scattered/isolated land units and provide a good compromise between construction sprawl and conservation land protection. The better performance proves that knowledge-informed NSGA-II is a more reasonable and desirable approach in the planning context.
Highlights
Multi-objective land allocation (MOLA) can be regarded as a spatial optimization problem that aims to allocate appropriate use to certain land units subjecting to multiple objectives and constraints (Eastman, Jiang, and Toledano 1998; Datta et al 2007)
This article develops an improved knowledge-informed non-dominated sorting genetic algorithm II (NSGA-II) for solving the MOLA problem by integrating the patch-based, edge growing/decreasing, neighborhood, and constraint steering rules. By applying both the classical and the knowledge-informed NSGA-II to a simulated planning area of 30 × 30 grid, we find that: when compared to the classical NSGA-II, the knowledge-informed NSGA-II consistently produces solutions much closer to the true Pareto front within shorter computation time without sacrificing the solution diversity; the knowledge-informed NSGA-II is more effective and more efficient in encouraging compact land allocation; the solutions produced by the knowledge-informed have less scattered/isolated land units and provide a good compromise between construction sprawl and conservation land protection
This study aims to develop an improved knowledgeinformed NSGA-II by designing operators that integrate the patched-based, edge growing/decreasing, neighborhood, and constraint steering rules to solve the MOLA problem
Summary
Multi-objective land allocation (MOLA) can be regarded as a spatial optimization problem that aims to allocate appropriate use to certain land units subjecting to multiple objectives and constraints (Eastman, Jiang, and Toledano 1998; Datta et al 2007). GA, and PSO have been developed for solving multi-objective optimization objectives, such as, non-dominated sorting genetic algorithm (NSGA), Pareto archived evolution strategy (PAES), Pareto SA (PSA), and multi-objective PSO (MOPSO) (Knowles and Corne 2000; Deb et al 2002; Banks, Vincent, and Anyakoha 2007; Coello, Lamont, and Van Veldhuizen 2007; Duh and Brown 2007; Masoomi, Mesgari, and Hamrah 2013) When these modified algorithms are applied to multi-objective optimization, they can follow both the weighted-sum method and the Pareto-based method. This study aims to develop an improved knowledgeinformed NSGA-II by designing operators that integrate the patched-based, edge growing/decreasing, neighborhood, and constraint steering rules to solve the MOLA problem.
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