Maritime search and rescue (SAR) plays a very important role in emergency waterway traffic situations, which is supposed to trigger severe personal casualties and property loss in maritime traffic accidents. The study aims to exploit an optimal allocation strategy with limited SAR resources deployed at navigation-constrained coastal islands. The study formulates the problem of SAR resource allocation in coastal areas into a non-linear optimization model. We explore the optimal solution for the SAR resource allocation problem under constraints of different ship and aircraft base station settings with the help of an enhanced particle swarm optimization (EPSO) model. Experimental results suggest that the proposed EPSO model can reasonably allocate the maritime rescue resources with a large coverage area and low time cost. The particle swarm optimization and genetic algorithm are further implemented for the purpose of model performance comparison. The research findings can help maritime traffic regulation departments to make more reasonable decisions for establishing SAR base stations.

Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method / Sun, Yang; Ling, Jun; Chen, Xinqiang; Kong, Fancun; Hu, Qinyou; Biancardo, Salvatore Antonio. - In: JOURNAL OF MARINE SCIENCE AND ENGINEERING. - ISSN 2077-1312. - 10:7(2022), p. 906. [10.3390/jmse10070906]

Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method

Biancardo, Salvatore Antonio
2022

Abstract

Maritime search and rescue (SAR) plays a very important role in emergency waterway traffic situations, which is supposed to trigger severe personal casualties and property loss in maritime traffic accidents. The study aims to exploit an optimal allocation strategy with limited SAR resources deployed at navigation-constrained coastal islands. The study formulates the problem of SAR resource allocation in coastal areas into a non-linear optimization model. We explore the optimal solution for the SAR resource allocation problem under constraints of different ship and aircraft base station settings with the help of an enhanced particle swarm optimization (EPSO) model. Experimental results suggest that the proposed EPSO model can reasonably allocate the maritime rescue resources with a large coverage area and low time cost. The particle swarm optimization and genetic algorithm are further implemented for the purpose of model performance comparison. The research findings can help maritime traffic regulation departments to make more reasonable decisions for establishing SAR base stations.
2022
Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method / Sun, Yang; Ling, Jun; Chen, Xinqiang; Kong, Fancun; Hu, Qinyou; Biancardo, Salvatore Antonio. - In: JOURNAL OF MARINE SCIENCE AND ENGINEERING. - ISSN 2077-1312. - 10:7(2022), p. 906. [10.3390/jmse10070906]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/889593
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 9
  • ???jsp.display-item.citation.isi??? 7
social impact