Basic farmland zoning and protection under spatial constraints with a particle swarm optimisation multiobjective decision model: a case study of Yicheng, China

The rapid development of the Chinese economy has led to an increasing fraction of agricultural land being converted to nonagricultural uses. The zoning and protection of farmland with the best agricultural quality (basic farmland) is extremely intensive and complicated work. In this paper we establish a remote sensing, geographic information system, and particle swarm optimisation (PSO) multiobjective decision model (MODM) to calculate the optimum solution for basic farmland protection. Furthermore, a new particle evolution rule combined with a genetic algorithm is introduced to improve the solution performance. The PSO-based zoning model is then utilised in the case study of Yicheng, Hubei Province, China, to demonstrate that our MODM framework excels in providing an optimum solution for balancing the three objectives of basic farmland zoning and protection: maximising farmland spatial compactness, maximising farmland soil fertility, and minimising transportation cost. In particular, the model compares alternative Pareto-optimal scenarios in which several objectives can be achieved without compromising the other objectives to obtain a real and practical blueprint for action. Our model enables urban planners to test and compare the different scenarios under various particle swarm conditions. In addition, the PSO-based zoning model constitutes a true guide for real-world planners, and this model can be extended to specify basic farmland protection optimisations in other regions of China.

[1]  J. Radke,et al.  An Application of Linear Programming and Geographic Information Systems: Cropland Allocation in Antigua , 1992 .

[2]  F. Helles,et al.  Spatial optimization by simulated annealing and linear programming , 1997 .

[3]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[4]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[5]  Richard E. Klosterman,et al.  The What If? Collaborative Planning Support System , 1999 .

[6]  Vijay V. Vazirani,et al.  Approximation algorithms for metric facility location and k-Median problems using the primal-dual schema and Lagrangian relaxation , 2001, JACM.

[7]  Marc E. McDill,et al.  Using the branch and bound algorithm to solve forest planning problems with adjacency constraints , 2001 .

[8]  Xia Li,et al.  A Constrained CA Model for the Simulation and Planning of Sustainable Urban Forms by Using GIS , 2001 .

[9]  Elizabeth Brabec,et al.  Agricultural land fragmentation: the spatial effects of three land protection strategies in the eastern United States , 2002 .

[10]  Rajan Batta,et al.  On the use of genetic algorithms to solve location problems , 2002, Comput. Oper. Res..

[11]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[12]  C. Woodcock,et al.  Monitoring land-use change in the Pearl River Delta using Landsat TM , 2002 .

[13]  G. J. Carsjens,et al.  Strategic land use allocation : dealing with spatial relationships and fragmentation of agriculture , 2002 .

[14]  J. Hamerlinck,et al.  Applications of land evaluation and site assessment (LESA) and a geographic information system (GIS) in East Park County, Wyoming , 2003 .

[15]  Hitoshi Iba,et al.  Particle swarm optimization with Gaussian mutation , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[16]  Zwe-Lee Gaing,et al.  Particle swarm optimization to solving the economic dispatch considering the generator constraints , 2003 .

[17]  Renato Assunção,et al.  A Simulated Annealing Strategy for the Detection of Arbitrarily Shaped Spatial Clusters , 2022 .

[18]  Theodor J. Stewart,et al.  A genetic algorithm approach to multiobjective land use planning , 2004, Comput. Oper. Res..

[19]  T. Saaty.  Analytic Hierarchy Process , 2005 .

[20]  Anthony Gar-On Yeh,et al.  Integration of genetic algorithms and GIS for optimal location search , 2005, Int. J. Geogr. Inf. Sci..

[21]  Steven A. Gabriel,et al.  A multiobjective optimization approach to smart growth in land development , 2006 .

[22]  Bernard De Baets,et al.  A spatial approach to forest‐management optimization: linking GIS and multiple objective genetic algorithms , 2006, Int. J. Geogr. Inf. Sci..

[23]  Daniel G. Brown,et al.  Knowledge-informed Pareto simulated annealing for multi-objective spatial allocation , 2007, Comput. Environ. Urban Syst..

[24]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[25]  Erik Lichtenberg,et al.  Assessing farmland protection policy in China , 2008 .

[26]  H. H. Zhang,et al.  Simulating Multi-Objective Spatial Optimization Allocation of Land Use Based on the Integration of Multi-Agent System and Genetic Algorithm , 2010 .

[27]  Yu Wang,et al.  Self-adaptive learning based particle swarm optimization , 2011, Inf. Sci..

[28]  Yan Liu,et al.  Modeling dynamic urban growth using cellular automata and particle swarm optimization rules , 2011 .

[29]  Mohammad Mehdi Ebadzadeh,et al.  A novel particle swarm optimization algorithm with adaptive inertia weight , 2011, Appl. Soft Comput..

[30]  Xiaoping Liu,et al.  Zoning farmland protection under spatial constraints by integrating remote sensing, GIS and artificial immune systems , 2011, Int. J. Geogr. Inf. Sci..

[31]  H. Jianhua,et al.  Rural land use spatial allocation in the semiarid loess hilly area in China: Using a Particle Swarm Optimization model equipped with multi-objective optimization techniques , 2012 .

[32]  YaoLin Liu,et al.  Rural land use spatial allocation in the semiarid loess hilly area in China: Using a Particle Swarm Optimization model equipped with multi-objective optimization techniques , 2012, Science China Earth Sciences.

[33]  Wei-Chung Cheng,et al.  Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm , 2014, BMC Bioinformatics.

[34]  Xia Li,et al.  Combining system dynamics and hybrid particle swarm optimization for land use allocation , 2013 .