期刊
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
卷 29, 期 5, 页码 806-824出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/13658816.2014.999245
关键词
cuckoo search; transition rules; CA; simulation; urban expansion
类别
资金
- National Science Foundation of China [41101349, 41471316]
- program of Natural Science Research of Jiangsu Higher Education Institutions of China [12KJA420001, 13KJB420003]
- Jiangsu Government Scholarship for Overseas Studies
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
This paper presents an intelligent approach to discover transition rules for cellular automata (CA) by using cuckoo search (CS) algorithm. CS algorithm is a novel evolutionary search algorithm for solving optimization problems by simulating breeding behavior of parasitic cuckoos. Each cuckoo searches the best upper and lower thresholds for each attribute as a zone. When the zones of all attributes are connected by the operator And' and linked with a cell status value, one CS-based transition rule is formed by using the explicit expression of if-then'. With two distinct advantages of efficient random walk of Levy flights and balanced mixing, CS algorithm performs well in both local search and guaranteed global convergence. Furthermore, the CA model with transition rules derived by CS algorithm (CS-CA) has been applied to simulate the urban expansion of Nanjing City, China. The simulation produces encouraging results, in terms of numeric accuracy and spatial distribution, in agreement with the actual patterns. Preliminary results suggest that this CS approach is well suitable for discovering reliable transition rules. The model validation and comparison show that the CS-CA model gets a higher accuracy than NULL, BCO-CA, PSO-CA, and ACO-CA models. Simulation results demonstrate the feasibility and practicability of applying CS algorithm to discover transition rules of CA for simulating geographical systems.
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