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

作　　者：Mingjie Song Dongmei Chen 

机构地区：[1]Department of Geography and Planning,Queen’s University,Kingston,Ontario,Canada

出　　处：《Geo-Spatial Information Science》2018年第4期273-287,共15页地球空间信息科学学报（英文）

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 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.

关 键 词：Multi-objective land allocation(MOLA) non-dominated sorting genetic algorithm II(NSGA-II) knowledge-informed rules 

分 类 号：G63[文化科学—教育学]