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Article

An Improved Parallelized Multi-Objective Optimization Method for Complex Geographical Spatial Sampling: AMOSA-II

by 1,2, 2,3, 1,*, 2,4 and 2,5
1
College of Land Science and Technology, China University of Geosciences, Beijing 100083, China
2
Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
3
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
4
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
5
Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(4), 236; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040236
Received: 1 March 2020 / Revised: 7 April 2020 / Accepted: 8 April 2020 / Published: 10 April 2020
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
Complex geographical spatial sampling usually encounters various multi-objective optimization problems, for which effective multi-objective optimization algorithms are much needed to help advance the field. To improve the computational efficiency of the multi-objective optimization process, the archived multi-objective simulated annealing (AMOSA)-II method is proposed as an improved parallelized multi-objective optimization method for complex geographical spatial sampling. Based on the AMOSA method, multiple Markov chains are used to extend the traditional single Markov chain; multi-core parallelization technology is employed based on multi-Markov chains. The tabu-archive constraint is designed to avoid repeated searches for optimal solutions. Two cases were investigated: one with six typical traditional test problems, and the other for soil spatial sampling optimization applications. Six performance indices of the two cases were analyzed—computational time, convergence, purity, spacing, min-spacing and displacement. The results revealed that AMOSA-II performed better which was more effective in obtaining preferable optimal solutions compared with AMOSA and NSGA-II. AMOSA-II can be treated as a feasible means to apply in other complex geographical spatial sampling optimizations. View Full-Text
Keywords: multi-objective optimization; AMOSA-II; multiple Markov chains; parallelization multi-objective optimization; AMOSA-II; multiple Markov chains; parallelization
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MDPI and ACS Style

Li, X.; Gao, B.; Bai, Z.; Pan, Y.; Gao, Y. An Improved Parallelized Multi-Objective Optimization Method for Complex Geographical Spatial Sampling: AMOSA-II. ISPRS Int. J. Geo-Inf. 2020, 9, 236. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040236

AMA Style

Li X, Gao B, Bai Z, Pan Y, Gao Y. An Improved Parallelized Multi-Objective Optimization Method for Complex Geographical Spatial Sampling: AMOSA-II. ISPRS International Journal of Geo-Information. 2020; 9(4):236. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040236

Chicago/Turabian Style

Li, Xiaolan, Bingbo Gao, Zhongke Bai, Yuchun Pan, and Yunbing Gao. 2020. "An Improved Parallelized Multi-Objective Optimization Method for Complex Geographical Spatial Sampling: AMOSA-II" ISPRS International Journal of Geo-Information 9, no. 4: 236. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040236

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