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Development of evolutionary based techniques with applications to engineering. / Carwyn Lloyd Pelley

Swansea University Author: Carwyn Lloyd Pelley

Abstract

Every possible problem can be considered to have a set of possible states by which amongst them, some are considered better than others by some chosen measure. It is the intention of optimisation to discover such states that perform better than all others for any given problem. It is an important to...

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Published: 2013
Institution: Swansea University
Degree level: Master of Philosophy
Degree name: M.Phil
URI: https://cronfa.swan.ac.uk/Record/cronfa42756
first_indexed 2018-08-02T18:55:28Z
last_indexed 2019-10-21T16:48:24Z
id cronfa42756
recordtype RisThesis
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spelling 2018-08-16T14:39:02.9105634 v2 42756 2018-08-02 Development of evolutionary based techniques with applications to engineering. 2b4b002a4c6130875b6d82a0e8995302 NULL Carwyn Lloyd Pelley Carwyn Lloyd Pelley true true 2018-08-02 Every possible problem can be considered to have a set of possible states by which amongst them, some are considered better than others by some chosen measure. It is the intention of optimisation to discover such states that perform better than all others for any given problem. It is an important tool within an array of subject areas, arguably all, in particular engineering, which tackles such applications as shape optimisation and industrial scheduling to name but a few. The aims of this work, are to increase the performance of the in-house general-purpose particle swarm optimiser designed at the department of engineering at Swansea University. This is to be achieved through its hybridisation with a local search, considering both solution refinement and early triggering mechanisms. In the discrete domain, an ant colony algorithm is to be chosen and evaluated by way of a parameter study and comparison against other leading ant colony algorithms made for the purpose of development for the future application to scheduling problems. Objectives are achieved through the increased refinement properties of the particle swarm optimiser with its hybridisation with local search. Additionally, an early switching mechanism is derived for the local search, resulting on average in a 20% reduction in the number of function evaluations required for constrained problems. With the highly unpredictable responses to unconstrained problems, only stagnation measures are derived. This study bridges the gap between the in-house optimiser and other hybrid particle swarm techniques available in the literature, resulting in competitive performance. An extensive literature review of ant colony identified the population-based ant colony algorithm (PACO) for further investigation. A detailed parameter study is conducted, resulting in the realisation of the strongly coupled parameters present. Following this, a hybrid off-line tuning method is devised, hybridising a simple particle swarm optimiser with the ant colony algorithm, resulting in an overall better performing algorithm. This indicated clear strengths in some cases over the more popular of ant colony algorithms. E-Thesis Computer science. 31 12 2013 2013-12-31 COLLEGE NANME Engineering COLLEGE CODE Swansea University Master of Philosophy M.Phil 2018-08-16T14:39:02.9105634 2018-08-02T16:24:30.3673959 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Carwyn Lloyd Pelley NULL 1 0042756-02082018162519.pdf 10807525.pdf 2018-08-02T16:25:19.5070000 Output 15166248 application/pdf E-Thesis true 2018-08-02T16:25:19.5070000 false
title Development of evolutionary based techniques with applications to engineering.
spellingShingle Development of evolutionary based techniques with applications to engineering.
Carwyn Lloyd Pelley
title_short Development of evolutionary based techniques with applications to engineering.
title_full Development of evolutionary based techniques with applications to engineering.
title_fullStr Development of evolutionary based techniques with applications to engineering.
title_full_unstemmed Development of evolutionary based techniques with applications to engineering.
title_sort Development of evolutionary based techniques with applications to engineering.
author_id_str_mv 2b4b002a4c6130875b6d82a0e8995302
author_id_fullname_str_mv 2b4b002a4c6130875b6d82a0e8995302_***_Carwyn Lloyd Pelley
author Carwyn Lloyd Pelley
author2 Carwyn Lloyd Pelley
format E-Thesis
publishDate 2013
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description Every possible problem can be considered to have a set of possible states by which amongst them, some are considered better than others by some chosen measure. It is the intention of optimisation to discover such states that perform better than all others for any given problem. It is an important tool within an array of subject areas, arguably all, in particular engineering, which tackles such applications as shape optimisation and industrial scheduling to name but a few. The aims of this work, are to increase the performance of the in-house general-purpose particle swarm optimiser designed at the department of engineering at Swansea University. This is to be achieved through its hybridisation with a local search, considering both solution refinement and early triggering mechanisms. In the discrete domain, an ant colony algorithm is to be chosen and evaluated by way of a parameter study and comparison against other leading ant colony algorithms made for the purpose of development for the future application to scheduling problems. Objectives are achieved through the increased refinement properties of the particle swarm optimiser with its hybridisation with local search. Additionally, an early switching mechanism is derived for the local search, resulting on average in a 20% reduction in the number of function evaluations required for constrained problems. With the highly unpredictable responses to unconstrained problems, only stagnation measures are derived. This study bridges the gap between the in-house optimiser and other hybrid particle swarm techniques available in the literature, resulting in competitive performance. An extensive literature review of ant colony identified the population-based ant colony algorithm (PACO) for further investigation. A detailed parameter study is conducted, resulting in the realisation of the strongly coupled parameters present. Following this, a hybrid off-line tuning method is devised, hybridising a simple particle swarm optimiser with the ant colony algorithm, resulting in an overall better performing algorithm. This indicated clear strengths in some cases over the more popular of ant colony algorithms.
published_date 2013-12-31T04:22:30Z
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score 11.090464

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