A Continuous Space Location Model and a Particle Swarm Optimization-Based Heuristic Algorithm for Maximizing the Allocation of Ocean-Moored Buoys
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 non-linea...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9359543/ |
| _version_ | 1818663127020994560 |
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| author | Miaomiao Song Shixuan Liu Wenqing Li Shizhe Chen Wenwen Li Keke Zhang Dingfeng Yu Lin Liu Xiaoyan Wang |
| author_facet | Miaomiao Song Shixuan Liu Wenqing Li Shizhe Chen Wenwen Li Keke Zhang Dingfeng Yu Lin Liu Xiaoyan Wang |
| author_sort | Miaomiao Song |
| collection | DOAJ |
| description | 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 non-linear, 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. |
| first_indexed | 2024-12-17T05:11:54Z |
| format | Article |
| id | doaj.art-c90d27edb910402cad592b71cad84418 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:11:54Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c90d27edb910402cad592b71cad844182022-12-21T22:02:13ZengIEEEIEEE Access2169-35362021-01-019322493226210.1109/ACCESS.2021.30604649359543A Continuous Space Location Model and a Particle Swarm Optimization-Based Heuristic Algorithm for Maximizing the Allocation of Ocean-Moored BuoysMiaomiao Song0https://orcid.org/0000-0002-7026-5528Shixuan Liu1https://orcid.org/0000-0002-7884-0435Wenqing Li2https://orcid.org/0000-0003-1563-5458Shizhe Chen3https://orcid.org/0000-0001-5802-3297Wenwen Li4https://orcid.org/0000-0003-2237-9499Keke Zhang5https://orcid.org/0000-0003-0836-3802Dingfeng Yu6https://orcid.org/0000-0003-2608-4911Lin Liu7https://orcid.org/0000-0002-6305-1548Xiaoyan Wang8https://orcid.org/0000-0002-7616-0102Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, ChinaSchool of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USAInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, ChinaOcean-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 non-linear, 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.https://ieeexplore.ieee.org/document/9359543/Continuous maximal coverage problemlocation modelingheuristic algorithmparticle swarm optimizationocean-moored buoyspatial optimization |
| spellingShingle | Miaomiao Song Shixuan Liu Wenqing Li Shizhe Chen Wenwen Li Keke Zhang Dingfeng Yu Lin Liu Xiaoyan Wang A Continuous Space Location Model and a Particle Swarm Optimization-Based Heuristic Algorithm for Maximizing the Allocation of Ocean-Moored Buoys IEEE Access Continuous maximal coverage problem location modeling heuristic algorithm particle swarm optimization ocean-moored buoy spatial optimization |
| title | A Continuous Space Location Model and a Particle Swarm Optimization-Based Heuristic Algorithm for Maximizing the Allocation of Ocean-Moored Buoys |
| title_full | A Continuous Space Location Model and a Particle Swarm Optimization-Based Heuristic Algorithm for Maximizing the Allocation of Ocean-Moored Buoys |
| title_fullStr | A Continuous Space Location Model and a Particle Swarm Optimization-Based Heuristic Algorithm for Maximizing the Allocation of Ocean-Moored Buoys |
| title_full_unstemmed | A Continuous Space Location Model and a Particle Swarm Optimization-Based Heuristic Algorithm for Maximizing the Allocation of Ocean-Moored Buoys |
| title_short | A Continuous Space Location Model and a Particle Swarm Optimization-Based Heuristic Algorithm for Maximizing the Allocation of Ocean-Moored Buoys |
| title_sort | continuous space location model and a particle swarm optimization based heuristic algorithm for maximizing the allocation of ocean moored buoys |
| topic | Continuous maximal coverage problem location modeling heuristic algorithm particle swarm optimization ocean-moored buoy spatial optimization |
| url | https://ieeexplore.ieee.org/document/9359543/ |
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