Reactive approach for automating exploration and exploitation in ant colony optimization

Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-base...

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Main Author:	Allwawi, Rafid Sagban Abbood
Format:	Thesis
Language:	eng eng
Published:	2016
Subjects:	QA75 Electronic computers Computer science
Online Access:	https://etd.uum.edu.my/5776/1/depositpermission_s94099.pdf https://etd.uum.edu.my/5776/2/s94099_01.pdf
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id	my-uum-etd.5776
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Ku Mahamud, Ku Ruhana Abu Bakar, Muhamad Shahbani
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Allwawi, Rafid Sagban Abbood Reactive approach for automating exploration and exploitation in ant colony optimization
description	Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-based reactive search technique has three (3) problems. First, the memory model to record previous search regions did not completely transfer the neighborhood structures to the next iteration which leads to arbitrary restart and premature local search. Secondly, the exploration indicator is not robust due to the difference of magnitudes in distance matrices for the current population. Thirdly, the parameter control techniques that utilize exploration indicators in their feedback process do not consider the problem of indicator robustness. A reactive ant colony optimization (RACO) algorithm has been proposed to overcome the limitations of the reactive search. RACO consists of three main components. The first component is a reactive max-min ant system algorithm for recording the neighborhood structures. The second component is a statistical machine learning mechanism named ACOustic to produce a robust exploration indicator. The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. The performance of RACO is evaluated on traveling salesman and quadratic assignment problems and compared with eight metaheuristics techniques in terms of success rate, Wilcoxon signed-rank, Chi-square and relative percentage deviation. Experimental results showed that the performance of RACO is superior than the eight (8) metaheuristics techniques which confirmed that RACO can be used as a new direction for solving optimization problems. RACO can be used in providing a dynamic exploration and exploitation mechanism, setting a parameter value which allows an efficient search, describing the amount of exploration an ACO algorithm performs and detecting stagnation situations.
format	Thesis
qualification_name	Ph.D.
qualification_level	Doctorate
author	Allwawi, Rafid Sagban Abbood
author_facet	Allwawi, Rafid Sagban Abbood
author_sort	Allwawi, Rafid Sagban Abbood
title	Reactive approach for automating exploration and exploitation in ant colony optimization
title_short	Reactive approach for automating exploration and exploitation in ant colony optimization
title_full	Reactive approach for automating exploration and exploitation in ant colony optimization
title_fullStr	Reactive approach for automating exploration and exploitation in ant colony optimization
title_full_unstemmed	Reactive approach for automating exploration and exploitation in ant colony optimization
title_sort	reactive approach for automating exploration and exploitation in ant colony optimization
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2016
url	https://etd.uum.edu.my/5776/1/depositpermission_s94099.pdf https://etd.uum.edu.my/5776/2/s94099_01.pdf
_version_	1747827979561467904
spelling	my-uum-etd.57762016-07-13T15:06:54Z Reactive approach for automating exploration and exploitation in ant colony optimization 2016 Allwawi, Rafid Sagban Abbood Ku Mahamud, Ku Ruhana Abu Bakar, Muhamad Shahbani Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA75 Electronic computers. Computer science Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-based reactive search technique has three (3) problems. First, the memory model to record previous search regions did not completely transfer the neighborhood structures to the next iteration which leads to arbitrary restart and premature local search. Secondly, the exploration indicator is not robust due to the difference of magnitudes in distance matrices for the current population. Thirdly, the parameter control techniques that utilize exploration indicators in their feedback process do not consider the problem of indicator robustness. A reactive ant colony optimization (RACO) algorithm has been proposed to overcome the limitations of the reactive search. RACO consists of three main components. The first component is a reactive max-min ant system algorithm for recording the neighborhood structures. The second component is a statistical machine learning mechanism named ACOustic to produce a robust exploration indicator. The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. The performance of RACO is evaluated on traveling salesman and quadratic assignment problems and compared with eight metaheuristics techniques in terms of success rate, Wilcoxon signed-rank, Chi-square and relative percentage deviation. Experimental results showed that the performance of RACO is superior than the eight (8) metaheuristics techniques which confirmed that RACO can be used as a new direction for solving optimization problems. RACO can be used in providing a dynamic exploration and exploitation mechanism, setting a parameter value which allows an efficient search, describing the amount of exploration an ACO algorithm performs and detecting stagnation situations. 2016 Thesis https://etd.uum.edu.my/5776/ https://etd.uum.edu.my/5776/1/depositpermission_s94099.pdf text eng staffonly https://etd.uum.edu.my/5776/2/s94099_01.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Aleti, A. (2012). An adaptive approach to controlling parameters of evolutionary algorithms. 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