Novel particle swarm optimizers with hybrid, dynamic and adaptive neighborhood structures

Many real world problems can be formulated as optimization problems with various parameters to be optimized. Some problems only have one objective to be optimized, some may have multiple objectives to be optimized at the same time and some need to be optimized subjecting to one or more constraints....

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Main Author: Liang, Jing
Other Authors: Chan Chi Chiu
Format: Theses and Dissertations
Language:English
Published: 2010
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Online Access:https://hdl.handle.net/10356/41803
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-418032023-07-04T16:53:28Z Novel particle swarm optimizers with hybrid, dynamic and adaptive neighborhood structures Liang, Jing Chan Chi Chiu Ponnuthurai Nagaratnam Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Many real world problems can be formulated as optimization problems with various parameters to be optimized. Some problems only have one objective to be optimized, some may have multiple objectives to be optimized at the same time and some need to be optimized subjecting to one or more constraints. Thus numerous optimization algorithms have been proposed to solve these problems. Particle Swarm Optimizer (PSO) is a relatively new optimization algorithm which has shown its strength in the optimization world. This thesis presents two PSO variants, Comprehensive Learning PSO and Dynamic Multi-Swarm PSO, which have good global search ability and can solve complex multi-modal problems for single objective optimization. The latter one' is extended to solve constrained optimization and multi-objective optimization problems successfully with a novel constraint-handling mechanism and a novel updating criterion respectively. Subsequently, the Dynamic Multi-Swarm PSO is applied to determine the Bragg wavelengths of the sensors in an FBG sensor network and a tree search structure is designed to improve the accuracy and reduce the computation cost. DOCTOR OF PHILOSOPHY (EEE) 2010-08-12T08:55:05Z 2010-08-12T08:55:05Z 2008 2008 Thesis Liang, J. (2008). Novel particle swarm optimizers with hybrid, dynamic and adaptive neighborhood structures. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/41803 10.32657/10356/41803 en 213 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::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Liang, Jing
Novel particle swarm optimizers with hybrid, dynamic and adaptive neighborhood structures
description Many real world problems can be formulated as optimization problems with various parameters to be optimized. Some problems only have one objective to be optimized, some may have multiple objectives to be optimized at the same time and some need to be optimized subjecting to one or more constraints. Thus numerous optimization algorithms have been proposed to solve these problems. Particle Swarm Optimizer (PSO) is a relatively new optimization algorithm which has shown its strength in the optimization world. This thesis presents two PSO variants, Comprehensive Learning PSO and Dynamic Multi-Swarm PSO, which have good global search ability and can solve complex multi-modal problems for single objective optimization. The latter one' is extended to solve constrained optimization and multi-objective optimization problems successfully with a novel constraint-handling mechanism and a novel updating criterion respectively. Subsequently, the Dynamic Multi-Swarm PSO is applied to determine the Bragg wavelengths of the sensors in an FBG sensor network and a tree search structure is designed to improve the accuracy and reduce the computation cost.
author2 Chan Chi Chiu
author_facet Chan Chi Chiu
Liang, Jing
format Theses and Dissertations
author Liang, Jing
author_sort Liang, Jing
title Novel particle swarm optimizers with hybrid, dynamic and adaptive neighborhood structures
title_short Novel particle swarm optimizers with hybrid, dynamic and adaptive neighborhood structures
title_full Novel particle swarm optimizers with hybrid, dynamic and adaptive neighborhood structures
title_fullStr Novel particle swarm optimizers with hybrid, dynamic and adaptive neighborhood structures
title_full_unstemmed Novel particle swarm optimizers with hybrid, dynamic and adaptive neighborhood structures
title_sort novel particle swarm optimizers with hybrid, dynamic and adaptive neighborhood structures
publishDate 2010
url https://hdl.handle.net/10356/41803
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