TY - GEN
T1 - Large-scale land use optimization by enhancing a scalable parallel genetic algorithm library
AU - Liu, Yan Y.
AU - Guo, Mengyu
AU - Wang, Shaowen
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - Optimization algorithms are often employed in spatial analysis and modeling to provide adaptive mechanisms at both individual and collective levels to enable decision-makers for the search of optimal solutions with respect to single/multiple objectives and constraints imposed by spatial configurations. This research aims to solve large-scale agricultural land use optimization problems by exploiting massive parallel computing resources provided by supercomputers such as those in XSEDE. The optimization of agricultural land use patterns finds an optimal assignment of crops (e.g., food and biofuel crops) on land parcels of a specified study area that maximizes the total yield and satisfies various competing constraints. These constraints often consider spatial factors such as contiguity and ownership, climate and land management factors (e.g., soil, precipitation, light, temperature, and ozone) and their effects on the productivity, suitability, and cost of assigning a crop on a land parcel. We have formulated the land use optimization problem as a classic combinatorial optimization problem - Generalized Assignment Problem (GAP) [2]. GAP is a well-known NP-hard problem [3]. When a landscape includes tens of thousands of land parcels (e.g., Figure 1), finding an exact optimal solution is computationally intractable.
AB - Optimization algorithms are often employed in spatial analysis and modeling to provide adaptive mechanisms at both individual and collective levels to enable decision-makers for the search of optimal solutions with respect to single/multiple objectives and constraints imposed by spatial configurations. This research aims to solve large-scale agricultural land use optimization problems by exploiting massive parallel computing resources provided by supercomputers such as those in XSEDE. The optimization of agricultural land use patterns finds an optimal assignment of crops (e.g., food and biofuel crops) on land parcels of a specified study area that maximizes the total yield and satisfies various competing constraints. These constraints often consider spatial factors such as contiguity and ownership, climate and land management factors (e.g., soil, precipitation, light, temperature, and ozone) and their effects on the productivity, suitability, and cost of assigning a crop on a land parcel. We have formulated the land use optimization problem as a classic combinatorial optimization problem - Generalized Assignment Problem (GAP) [2]. GAP is a well-known NP-hard problem [3]. When a landscape includes tens of thousands of land parcels (e.g., Figure 1), finding an exact optimal solution is computationally intractable.
KW - Genetic algorithm
KW - Heuristics
KW - Land use optimization
KW - Parallel computing
KW - Scalability
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U2 - 10.1145/2484762.2484824
DO - 10.1145/2484762.2484824
M3 - Conference contribution
AN - SCOPUS:84882339928
SN - 9781450321709
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the XSEDE 2013 Conference
T2 - Conference on Extreme Science and Engineering Discovery Environment, XSEDE 2013
Y2 - 22 July 2013 through 25 July 2013
ER -