An emergency response system by dynamic simulation and enhanced particle swarm optimization and application for a marine oil spill accident

2021 ◽  
Vol 297 ◽  
pp. 126591
Author(s):  
Xudong Ye ◽  
Bing Chen ◽  
Kenneth Lee ◽  
Rune Storesund ◽  
Pu Li ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Simulation ◽  
Emergency Response ◽  
Oil Spill ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Marine Oil ◽  
Emergency Response System ◽  
Response System ◽  
Enhanced Particle Swarm Optimization

Marine Oil Spill Emergency Response Research

2012 ◽  
Vol 605-607 ◽  
pp. 234-238
Author(s):  
Feng Yu ◽  
Xiao Feng Sun ◽  
Xin Yu Zhang
Keyword(s):  
Emergency Response ◽  
Oil Spill ◽  
3D Modeling ◽  
Special Interest ◽  
System Simulation ◽  
Emergency Plan ◽  
Marine Oil ◽  
Emergency Response System ◽  
Response System

Oil spill emergency response is a way of controlling and cleaning oil spill fast and effectively according to emergency plan; it would decrease the pollution of oil spill. Refer to the vessel oil spill emergency response system status of China, based on the research of vessel oil spill emergency response system, this paper analyzes the function and parameter of a variety of oil spill emergency equipments, then does 3d modeling to WQJ1000 oil containment boom, this is of special interest to the oil spill emergency response system simulation.

scholarly journals Enhanced Particle Swarm Optimization for Path Planning of Unmanned Aerial Vehicles

2020 ◽  
Vol 14 (1) ◽  
pp. 67-78
Author(s):  
Kai Yit Kok ◽  
Parvathy Rajendran
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Aerial Vehicles ◽  
Trial And Error Method ◽  
The Impact ◽  
Enhanced Particle Swarm Optimization ◽  
Error Method

This paper presents an enhanced particle swarm optimization (PSO) for the path planning of unmanned aerial vehicles (UAVs). An evolutionary algorithm such as PSO is costly because every application requires different parameter settings to maximize the performance of the analyzed parameters. People generally use the trial-and-error method or refer to the recommended setting from general problems. The former is time consuming, while the latter is usually not the optimum setting for various specific applications. Hence, this study focuses on analyzing the impact of input parameters on the PSO performance in UAV path planning using various complex terrain maps with adequate repetitions to solve the tuning issue. Results show that inertial weight parameter is insignificant, and a 1.4 acceleration coefficient is optimum for UAV path planning. In addition, the population size between 40 and 60 seems to be the optimum setting based on the case studies.

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