Learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions

Evolutionary Algorithms (EA) consist of several heuristics which are able to solve optimisation tasks by imitating some aspects of natural evolution. Two widely-used EAs, namely Harmony Search (HS) and Imperialist Competitive Algorithm (ICA), are considered for improving single objective EA and Mult...

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Main Author: Enayatifar, Rasul
Format: Thesis
Language:English
Published: 2014
Subjects:
Online Access:http://eprints.utm.my/id/eprint/78495/1/RasulEnayatifarPFC2014.pdf
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Institution: Universiti Teknologi Malaysia
Language: English
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spelling my.utm.784952018-08-26T04:56:34Z http://eprints.utm.my/id/eprint/78495/ Learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions Enayatifar, Rasul QA75 Electronic computers. Computer science Evolutionary Algorithms (EA) consist of several heuristics which are able to solve optimisation tasks by imitating some aspects of natural evolution. Two widely-used EAs, namely Harmony Search (HS) and Imperialist Competitive Algorithm (ICA), are considered for improving single objective EA and Multi Objective EA (MOEA), respectively. HS is popular because of its speed and ICA has the ability for escaping local optima, which is an important criterion for a MOEA. In contrast, both algorithms have suffered some shortages. The HS algorithm could be trapped in local optima if its parameters are not tuned properly. This shortage causes low convergence rate and high computational time. In ICA, there is big obstacle that impedes ICA from becoming MOEA. ICA cannot be matched with crowded distance method which produces qualitative value for MOEAs, while ICA needs quantitative value to determine power of each solution. This research proposes a learnable EA, named learning automata harmony search (LAHS). The EA employs a learning automata (LA) based approach to ensure that HS parameters are learnable. This research also proposes a new MOEA based on ICA and Sigma method, named Sigma Imperialist Competitive Algorithm (SICA). Sigma method provides a mechanism to measure the solutions power based on their quantity value. The proposed LAHS and SICA algorithms are tested on wellknown single objective and multi objective benchmark, respectively. Both LAHS and MOICA show improvements in convergence rate and computational time in comparison to the well-known single EAs and MOEAs. 2014-07 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/78495/1/RasulEnayatifarPFC2014.pdf Enayatifar, Rasul (2014) Learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions. PhD thesis, Universiti Teknologi Malaysia, Faculty of Computing. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:97929
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Enayatifar, Rasul
Learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions
description Evolutionary Algorithms (EA) consist of several heuristics which are able to solve optimisation tasks by imitating some aspects of natural evolution. Two widely-used EAs, namely Harmony Search (HS) and Imperialist Competitive Algorithm (ICA), are considered for improving single objective EA and Multi Objective EA (MOEA), respectively. HS is popular because of its speed and ICA has the ability for escaping local optima, which is an important criterion for a MOEA. In contrast, both algorithms have suffered some shortages. The HS algorithm could be trapped in local optima if its parameters are not tuned properly. This shortage causes low convergence rate and high computational time. In ICA, there is big obstacle that impedes ICA from becoming MOEA. ICA cannot be matched with crowded distance method which produces qualitative value for MOEAs, while ICA needs quantitative value to determine power of each solution. This research proposes a learnable EA, named learning automata harmony search (LAHS). The EA employs a learning automata (LA) based approach to ensure that HS parameters are learnable. This research also proposes a new MOEA based on ICA and Sigma method, named Sigma Imperialist Competitive Algorithm (SICA). Sigma method provides a mechanism to measure the solutions power based on their quantity value. The proposed LAHS and SICA algorithms are tested on wellknown single objective and multi objective benchmark, respectively. Both LAHS and MOICA show improvements in convergence rate and computational time in comparison to the well-known single EAs and MOEAs.
format Thesis
author Enayatifar, Rasul
author_facet Enayatifar, Rasul
author_sort Enayatifar, Rasul
title Learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions
title_short Learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions
title_full Learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions
title_fullStr Learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions
title_full_unstemmed Learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions
title_sort learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions
publishDate 2014
url http://eprints.utm.my/id/eprint/78495/1/RasulEnayatifarPFC2014.pdf
http://eprints.utm.my/id/eprint/78495/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:97929
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