Optimization of capillary tube stacking

Genetic algorithm is an evolutionary numerical method, which helps solving optimization problems in an efficient way. Genetic algorithm is based on the belief that “the fittest survives”, a theory proposed by Darwin in his evolution theory. A typical genetic algorithm maps the physical parameters i...

Full description

Saved in:
Bibliographic Details
Main Author: Liu, Qinghua
Other Authors: Shao Xuguang
Format: Final Year Project
Language:English
Published: 2015
Subjects:
Online Access:http://hdl.handle.net/10356/64639
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-64639
record_format dspace
spelling sg-ntu-dr.10356-646392023-07-07T16:50:44Z Optimization of capillary tube stacking Liu, Qinghua Shao Xuguang Shum Ping School of Electrical and Electronic Engineering DRNTU::Engineering Genetic algorithm is an evolutionary numerical method, which helps solving optimization problems in an efficient way. Genetic algorithm is based on the belief that “the fittest survives”, a theory proposed by Darwin in his evolution theory. A typical genetic algorithm maps the physical parameters into biological entities, and imitates the natural selection process. A solution candidate is mapped into a set of single or multiple binary bit strings, called chromosomes. A solution candidate is an individual in its generation. An individual’s fitness is measured by a fitness function, and the fitter individuals will be selected through the selection operator to participate in the reproduction of the next generation individual. Through a well - designed selection and crossover method, the fittest individuals and “genes” will be preserved, maintained and generated, and eventually, all the individuals will converge towards the fittest value. One advantage of genetic algorithm is its generic nature, which enables it to solve all types of optimization problems: as long as a candidate solution can be modeled as a set of chromosomes, its optimized solution can be found. Currently in photonic crystal fibre design, there is lack of an automated method to help the scientists and researcher to decide the sizes and positions the filler capillaries, whose functionality is to provide physical structural support for the capillaries. Currently, designers and researchers have to manually produce the design graphs; this means the outcomes are not precise and often lead to the failure of production. This thesis is aimed to analyze the crystal fibre structure, and utilize genetic algorithm, to design and implement a computer program to assist the designers in their stacking process. The program is aimed to be able to calculate the size and the position of the filler capillaries, and thus, provide an optimized solution to the photonic crystal fibre stacking problem. Bachelor of Engineering 2015-05-29T02:29:41Z 2015-05-29T02:29:41Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/64639 en Nanyang Technological University 68 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
spellingShingle DRNTU::Engineering
Liu, Qinghua
Optimization of capillary tube stacking
description Genetic algorithm is an evolutionary numerical method, which helps solving optimization problems in an efficient way. Genetic algorithm is based on the belief that “the fittest survives”, a theory proposed by Darwin in his evolution theory. A typical genetic algorithm maps the physical parameters into biological entities, and imitates the natural selection process. A solution candidate is mapped into a set of single or multiple binary bit strings, called chromosomes. A solution candidate is an individual in its generation. An individual’s fitness is measured by a fitness function, and the fitter individuals will be selected through the selection operator to participate in the reproduction of the next generation individual. Through a well - designed selection and crossover method, the fittest individuals and “genes” will be preserved, maintained and generated, and eventually, all the individuals will converge towards the fittest value. One advantage of genetic algorithm is its generic nature, which enables it to solve all types of optimization problems: as long as a candidate solution can be modeled as a set of chromosomes, its optimized solution can be found. Currently in photonic crystal fibre design, there is lack of an automated method to help the scientists and researcher to decide the sizes and positions the filler capillaries, whose functionality is to provide physical structural support for the capillaries. Currently, designers and researchers have to manually produce the design graphs; this means the outcomes are not precise and often lead to the failure of production. This thesis is aimed to analyze the crystal fibre structure, and utilize genetic algorithm, to design and implement a computer program to assist the designers in their stacking process. The program is aimed to be able to calculate the size and the position of the filler capillaries, and thus, provide an optimized solution to the photonic crystal fibre stacking problem.
author2 Shao Xuguang
author_facet Shao Xuguang
Liu, Qinghua
format Final Year Project
author Liu, Qinghua
author_sort Liu, Qinghua
title Optimization of capillary tube stacking
title_short Optimization of capillary tube stacking
title_full Optimization of capillary tube stacking
title_fullStr Optimization of capillary tube stacking
title_full_unstemmed Optimization of capillary tube stacking
title_sort optimization of capillary tube stacking
publishDate 2015
url http://hdl.handle.net/10356/64639
_version_ 1772829033932783616