Particle swarm optimization for solving vehicle routing problem with time windows

Hybridization of Self Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these hybrid architectures have weaknesses such as slow convergence time; always being trap...

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Main Author:	Abdullah, Nurashikin
Format:	Thesis
Published:	2010
Subjects:	Q Science (General)
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id	my-utm-ep.16582
record_format	uketd_dc
spelling	my-utm-ep.165822017-09-24T08:36:27Z Particle swarm optimization for solving vehicle routing problem with time windows 2010-10 Abdullah, Nurashikin Q Science (General) Hybridization of Self Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these hybrid architectures have weaknesses such as slow convergence time; always being trapped in the local minima and others. This study proposes a hybridization method by improving the Self Organizing Map (SOM) Lattice Structure with Particle Swarm Optimization (ESOMPSO) for solving classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The improvement of the SOM lattice structure using the proposed Enhanced SOM is implemented by optimizing the weights using PSO to obtain better output quality. The process is done in two stages: the first stage is conducted by training the weights using the Enhanced SOM, and the second stage is implemented by optimizing these weights with the PSO. The proposed method has been tested on various standard datasets. The comparisons are done on standard SOM, Enhanced SOM (ESOM), SOMPSO and ESOMPSO using various distance measurements. The performance of the proposed method is validated using classification accuracy and quantization error. The experiments have shown that ESOMPSO yields promising result with better average accuracy and quantization errors. 2010-10 Thesis http://eprints.utm.my/id/eprint/16582/ http://libraryopac.utm.my/client/en_AU/main/search/results?qu=Particle+swarm+optimization+for+solving+vehicle+routing+problem+with+time+windows&te= masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
topic	Q Science (General)
spellingShingle	Q Science (General) Abdullah, Nurashikin Particle swarm optimization for solving vehicle routing problem with time windows
description	Hybridization of Self Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these hybrid architectures have weaknesses such as slow convergence time; always being trapped in the local minima and others. This study proposes a hybridization method by improving the Self Organizing Map (SOM) Lattice Structure with Particle Swarm Optimization (ESOMPSO) for solving classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The improvement of the SOM lattice structure using the proposed Enhanced SOM is implemented by optimizing the weights using PSO to obtain better output quality. The process is done in two stages: the first stage is conducted by training the weights using the Enhanced SOM, and the second stage is implemented by optimizing these weights with the PSO. The proposed method has been tested on various standard datasets. The comparisons are done on standard SOM, Enhanced SOM (ESOM), SOMPSO and ESOMPSO using various distance measurements. The performance of the proposed method is validated using classification accuracy and quantization error. The experiments have shown that ESOMPSO yields promising result with better average accuracy and quantization errors.
format	Thesis
qualification_level	Master's degree
author	Abdullah, Nurashikin
author_facet	Abdullah, Nurashikin
author_sort	Abdullah, Nurashikin
title	Particle swarm optimization for solving vehicle routing problem with time windows
title_short	Particle swarm optimization for solving vehicle routing problem with time windows
title_full	Particle swarm optimization for solving vehicle routing problem with time windows
title_fullStr	Particle swarm optimization for solving vehicle routing problem with time windows
title_full_unstemmed	Particle swarm optimization for solving vehicle routing problem with time windows
title_sort	particle swarm optimization for solving vehicle routing problem with time windows
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2010
_version_	1747815078038601728

Particle swarm optimization for solving vehicle routing problem with time windows

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