scholarly journals Passengers’ Evacuation in Ships Based on Neighborhood Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Gan-Nan Yuan ◽  
Li-Na Zhang ◽  
Li-Qiang Liu ◽  
Kan Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Degree Of Freedom ◽  
Swarm Optimization ◽  
The Real ◽  
Environment Factor ◽  
Evacuation Model ◽  
Freedom Movement ◽  
Intelligent Model ◽  
Test Scenarios

A new intelligent model to simulate evacuation behavior in ships called neighborhood particle swarm optimization is proposed. This model determines the rules of behavior and velocity updating formulas to solve staff conflicts. The individuals in evacuation are taken as particles in PSO and update their behaviors by individual attributes, neighborhood attributes, and social attributes. Putting the degree of freedom movement of ships into environment factor and using the real Ro-Ro ship information and IMO test scenarios to simulate the evacuation process, the model in this paper can truly simulate the behavior of persons in emergency and provide a new idea to design excellent evacuation model.

scholarly journals Cross-Correlation Wavelet-Domain-Based Particle Swarm Optimization for Lightning Mapping

2021 ◽  
Vol 11 (18) ◽  
pp. 8634
Author(s):  
Ammar Alammari ◽  
Ammar Ahmed Alkahtani ◽  
Mohd Riduan Ahmad ◽  
Ahmed Aljanad ◽  
Fuad Noman ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Cross Correlation ◽  
Particle Swarm ◽  
Sliding Window ◽  
Event Extraction ◽  
Wavelet Domain ◽  
Estimation Errors ◽  
Swarm Optimization ◽  
Lightning Strikes ◽  
The Real

Several processing methods have been proposed for estimating the real pattern of the temporal location and spatial map of the lightning strikes. However, due to the complexity of lightning signals, providing accurate lightning maps estimation remains a challenging task. This paper presents a cross-correlation wavelet-domain-based particle swarm optimization (CCWD-PSO) technique for an accurate and robust representation of lightning mapping. The CCWD method provides an initial estimate of the lightning map, while the PSO attempts to optimize the trajectory of the lightning map by finding the optimal sliding window of the cross-correlation. The technique was further enhanced through the introduction of a novel lightning event extraction method that enables faster processing of the lightning mapping. The CCWD-PSO method was validated and verified using three narrow bipolar events (NBEs) flashes. The observed results demonstrate that this technique offers high accuracy in representing the real lightning mapping with low estimation errors.

Application of Particle Swarm Optimization to Robot Path Planning Problem

2010 ◽  
Author(s):  
Masakazu Kobayashi ◽  
Higashi Masatake
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Particle Swarm ◽  
Degree Of Freedom ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Swarm Optimization ◽  
Articulated Robot ◽  
Path Planning Problem ◽  
Robot Path

A robot path planning problem is to produce a path that connects a start configuration and a goal configuration while avoiding collision with obstacles. To obtain a path for robots with high degree of freedom of motion such as an articulated robot efficiently, sampling-based algorithms such as probabilistic roadmap (PRM) and rapidly-exploring random tree (RRT) were proposed. In this paper, a new robot path planning method based on Particle Swarm Optimization (PSO), which is one of heuristic optimization methods, is proposed in order to improve efficiency of path planning for a wider range of problems. In the proposed method, a group of particles fly through a configuration space while avoiding collision with obstacles and a collection of their trajectories is regarded as a roadmap. A velocity of each particle is updated for every time step based on the update equation of PSO. After explaining the details of the proposed method, this paper shows the comparisons of efficiency between the proposed method and RRT for 2D maze problems and then shows application of the proposed method to path planning for a 6 degree of freedom articulated robot.

An improved particle swarm optimization algorithm and its application in solving forward kinematics of a 3-DoF parallel manipulator

2020 ◽  
pp. 095440622093910
Author(s):  
Shuzhen Zhang ◽  
Xiaolong Yuan ◽  
Paul D Docherty ◽  
Kai Yang ◽  
Chunling Li
Keyword(s):  
Particle Swarm Optimization ◽  
Parallel Manipulator ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Degree Of Freedom ◽  
Forward Kinematics ◽  
Swarm Optimization ◽  
Local Optima ◽  
Improved Particle Swarm Optimization

This paper proposes an improved particle swarm optimization to study the forward kinematic of a solar tracking device which has two rotational and one translational degree of freedom. The forward kinematics of the parallel manipulator is transformed into an optimization problem by solving the inverse kinematics equations. The proposed method combines inertial weight with the iterations number and the distance between current swarm particles and the optimum to improve convergence ability and speed. The novel cognitive and social parameters are adjusted by the inertia weight to enhance unity and intelligence of the algorithm. A stochastic mutation is used to diversify swarm for faster convergence via local optima evasion in high dimensional complex optimization problems. The performance of the proposed method is demonstrated by applying it to four benchmark functions and comparing convergence with three popular particle swarm optimization methods to verify the feasibility of the improved method. The behaviors of the proposed method using variable cognitive and social parameters and fixed value are also tested to verify fast convergence speed of variable parameters method. And further, an application example uses the method to determine the forward kinematics of a three-degree-of-freedom parallel manipulator. Finally, the mechanism simulations model of the parallel manipulator are carefully built and analyzed to verify the correctness of the proposed algorithm in PTC Creo Parametric software. In all cases tested, the proposed algorithm achieved much faster convergence and either improved or proximal fitness values.

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