An improved knowledge-informed NSGA-II for multi-objective land allocation (MOLA)

作     者：Mingjie Song Dongmei Chen 

作者机构：Department of Geography and PlanningQueen’s UniversityKingstonOntarioCanada 

出 版 物：《Geo-Spatial Information Science》 (地球空间信息科学学报（英文）)

年 卷 期：2018年第21卷第4期

页      面：273-287页

核心收录：

学科分类：0401[教育学-教育学] 04[教育学] 

主　　题：Multi-objective land allocation(MOLA) non-dominated sorting genetic algorithm II(NSGA-II) knowledge-informed rules 

摘      要：Multi-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 *** 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 *** 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 *** better performance proves that knowledge-informed NSGA-II is a more reasonable and desirable approach in the planning context.