A path finding simulation model for human crowd using agent based ant colony optimization

Ant Colony Optimization (ACO) is a popular meta-heuristic used to solve NP problems such as TSP and is popular in areas such as Robotic. This paper will study the feasibility of using ACO to plan a path for human crowd simulation. The idea here is not to find only optimal paths, but to find suitable...

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Main Author: Tin Htet Kyaw @ Jame Ng.
Other Authors: School of Computer Engineering
Format: Final Year Project
Language:English
Published: 2011
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Online Access:http://hdl.handle.net/10356/44668
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-446682023-03-03T20:24:39Z A path finding simulation model for human crowd using agent based ant colony optimization Tin Htet Kyaw @ Jame Ng. School of Computer Engineering Michael Harold Lees DRNTU::Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling Ant Colony Optimization (ACO) is a popular meta-heuristic used to solve NP problems such as TSP and is popular in areas such as Robotic. This paper will study the feasibility of using ACO to plan a path for human crowd simulation. The idea here is not to find only optimal paths, but to find suitable paths for human crowd which can be more than one and not necessarily optimal. As the traditional ACO uses Pheromone convergence to find optimal paths, this paper will look at a Hybrid ACO which incorporates shortest Euclidean distance with the traditional ACO to do path planning. This means that ant in the Hybrid model will move with some influence of choosing shortest Euclidean distance path and converge using the traditional Pheromone method. With the paths found, it can then be used to derive waypoints which can then be used for human agents in a crowd simulation. Experiments conducted compared the performance of the Hybrid ant model with the traditional Pheromone ant model and the extreme Euclidean Distance bias ant model. The performance measures will include the exploration, convergence, and number of ants that found food. Bachelor of Engineering (Computer Engineering) 2011-06-03T01:38:01Z 2011-06-03T01:38:01Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/44668 en Nanyang Technological University 69 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
Tin Htet Kyaw @ Jame Ng.
A path finding simulation model for human crowd using agent based ant colony optimization
description Ant Colony Optimization (ACO) is a popular meta-heuristic used to solve NP problems such as TSP and is popular in areas such as Robotic. This paper will study the feasibility of using ACO to plan a path for human crowd simulation. The idea here is not to find only optimal paths, but to find suitable paths for human crowd which can be more than one and not necessarily optimal. As the traditional ACO uses Pheromone convergence to find optimal paths, this paper will look at a Hybrid ACO which incorporates shortest Euclidean distance with the traditional ACO to do path planning. This means that ant in the Hybrid model will move with some influence of choosing shortest Euclidean distance path and converge using the traditional Pheromone method. With the paths found, it can then be used to derive waypoints which can then be used for human agents in a crowd simulation. Experiments conducted compared the performance of the Hybrid ant model with the traditional Pheromone ant model and the extreme Euclidean Distance bias ant model. The performance measures will include the exploration, convergence, and number of ants that found food.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Tin Htet Kyaw @ Jame Ng.
format Final Year Project
author Tin Htet Kyaw @ Jame Ng.
author_sort Tin Htet Kyaw @ Jame Ng.
title A path finding simulation model for human crowd using agent based ant colony optimization
title_short A path finding simulation model for human crowd using agent based ant colony optimization
title_full A path finding simulation model for human crowd using agent based ant colony optimization
title_fullStr A path finding simulation model for human crowd using agent based ant colony optimization
title_full_unstemmed A path finding simulation model for human crowd using agent based ant colony optimization
title_sort path finding simulation model for human crowd using agent based ant colony optimization
publishDate 2011
url http://hdl.handle.net/10356/44668
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