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

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.

A Systematic Literature Review of Maritime Transportation Safety Management

“…Many different approaches have been developed to address maritime transportation safety and emergency management problems. Recently, new methods that have appeared in maritime safety and emergency management research include STPA, cognitive reliability error analysis method (CREAM), DBN, emergency assessment-based simulation [88], probabilistic risk assessment-based simulation [89], resilience assessment-based simulation [90], and mathematical modeling and optimization methods, such as non-linear optimization and enhanced particle swarm optimization (EPSO) models [91], multi-objective particle swarm algorithm [47], and dynamic multi-objective optimization model [92]. At present, machine learning is introduced to improve maritime safety and management.…”

“…tion [90], and mathematical modeling and optimization methods, such as non-linear optimization and enhanced particle swarm optimization (EPSO) models [91], multi-objective particle swarm algorithm [47], and dynamic multi-objective optimization model [92]. At present, machine learning is introduced to improve maritime safety and management.…”

“…There needs to be a balance between the focus on preventative safety efforts and the extent of emergency preparedness provisions. Currently, existing research focuses on the location of the emergency supplies reserve base, rescue resource allocation, and configuration optimization of salvage vessels [91,115]. Moreover, most research has taken oil spills as a case to analyze emergency resource dispatching [92,116].…”

Modeling and designing a robotic swarm: A quantum computing approach

“…Concerning the software improvements, there are several optimization approaches to robotic swarms. A notorious example is the application of particle swarm optimization (PSO) [22] to refine search and rescue robotic missions [23,24], where each particle represents a robot, also in maritime scenarios [25].…”

A Search Strategy and Vessel Detection in Maritime Environment Using Fixed-Wing UAVs

“…There is plenty of literature related to UAV search in mar-itime environments, usually considered in search and rescue problems using multiple UAVs [7]. In [8], the authors focus on solving the path planning problem for deploying multiple UAVs in a maritime area proposing a two-phase framework.…”