A continuous space location model and a particle swarm optimization-based heuristic algorithm for maximizing the allocation of ocean-moored buoys

Miaomiao Song, Shixuan Liu, Wenqing Li, Shizhe Chen, Wenwen Li, Keke Zhang, Dingfeng Yu, Lin Liu, Xiaoyan Wang

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number3060464
Pages (from-to)32249-32262
Number of pages14
JournalIEEE Access
Volume9
DOIs
StatePublished - 2021

Keywords

  • Continuous maximal coverage problem
  • Decision-support system
  • Heuristic algorithm
  • Location modeling
  • Ocean-moored buoy
  • Particle swarm optimization
  • Spatial optimization

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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