A Sub-boundary Approach for Enhanced Particle Swarm Optimization and Its Application to the Design of Artificial Magnetic Conductors

2007 ◽  
Vol 55 (3) ◽  
pp. 766-770 ◽  
Author(s):  
Simone Genovesi ◽  
Agostino Monorchio ◽  
Raj Mittra ◽  
Giuliano Manara
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Artificial Magnetic Conductors ◽  
Enhanced Particle Swarm Optimization

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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