TY - JOUR
T1 - A continuous space location model and a particle swarm optimization-based heuristic algorithm for maximizing the allocation of ocean-moored buoys
AU - Song, Miaomiao
AU - Liu, Shixuan
AU - Li, Wenqing
AU - Chen, Shizhe
AU - Li, Wenwen
AU - Zhang, Keke
AU - Yu, Dingfeng
AU - Liu, Lin
AU - Wang, Xiaoyan
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 41801296, Grant 41976179, and Grant 42076195, and in part by the Shandong Provincial Natural Science Foundation of China under Grant ZR2020MF022.
Publisher Copyright:
© 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Ocean-moored buoys play an important role in global ocean environment monitoring. Motivated by building a sustainable ocean buoy observational network, a spatial optimization approach is proposed to site buoy stations to maximize spatial monitoring efficiency (SME). To achieve this goal, a nonlinear, continuous maximum coverage location model named CMCP-Ocean was established, associated with a measurement method of the SME. Meanwhile, a heuristic framework based on the particle swarm optimization (PSO) algorithm was built to solve the CMCP-Ocean model, and optimization strategies including the multi-core parallel computing strategy, the particle velocity updating strategy based on spatial matching, and two potential station selection strategies related to the centroid-based random radiation method (CRRM) and random grid division method (RGDM) were established to improve computing performance. The effectiveness and efficiency of the PSO-based algorithm and the CMCP-Ocean model were verified by a series of experiments; the proposed computing schema named PSO-for-CMCP-Ocean has also proven to be practical and efficient. Finally, the PSO-for-CMCP-Ocean was applied to the buoy station selection of water mass monitoring in the Laizhou Bay of China, and a multi-scale sustainable site planning solution is reported.
AB - Ocean-moored buoys play an important role in global ocean environment monitoring. Motivated by building a sustainable ocean buoy observational network, a spatial optimization approach is proposed to site buoy stations to maximize spatial monitoring efficiency (SME). To achieve this goal, a nonlinear, continuous maximum coverage location model named CMCP-Ocean was established, associated with a measurement method of the SME. Meanwhile, a heuristic framework based on the particle swarm optimization (PSO) algorithm was built to solve the CMCP-Ocean model, and optimization strategies including the multi-core parallel computing strategy, the particle velocity updating strategy based on spatial matching, and two potential station selection strategies related to the centroid-based random radiation method (CRRM) and random grid division method (RGDM) were established to improve computing performance. The effectiveness and efficiency of the PSO-based algorithm and the CMCP-Ocean model were verified by a series of experiments; the proposed computing schema named PSO-for-CMCP-Ocean has also proven to be practical and efficient. Finally, the PSO-for-CMCP-Ocean was applied to the buoy station selection of water mass monitoring in the Laizhou Bay of China, and a multi-scale sustainable site planning solution is reported.
KW - Continuous maximal coverage problem
KW - Decision-support system
KW - Heuristic algorithm
KW - Location modeling
KW - Ocean-moored buoy
KW - Particle swarm optimization
KW - Spatial optimization
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U2 - 10.1109/ACCESS.2021.3060464
DO - 10.1109/ACCESS.2021.3060464
M3 - Article
AN - SCOPUS:85101780708
SN - 2169-3536
VL - 9
SP - 32249
EP - 32262
JO - IEEE Access
JF - IEEE Access
M1 - 3060464
ER -