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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:English
English
Published: 2016
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://etd.uum.edu.my/5776/1/depositpermission_s94099.pdf
http://etd.uum.edu.my/5776/2/s94099_01.pdf
http://etd.uum.edu.my/5776/
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spelling my.uum.etd.57762016-07-13T15:06:54Z http://etd.uum.edu.my/5776/ Reactive approach for automating exploration and exploitation in ant colony optimization Allwawi, Rafid Sagban Abbood 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 NonPeerReviewed text en http://etd.uum.edu.my/5776/1/depositpermission_s94099.pdf text en http://etd.uum.edu.my/5776/2/s94099_01.pdf Allwawi, Rafid Sagban Abbood (2016) Reactive approach for automating exploration and exploitation in ant colony optimization. PhD. thesis, Universiti Utara Malaysia.
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Electronic Theses
url_provider http://etd.uum.edu.my/
language English
English
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
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
publishDate 2016
url http://etd.uum.edu.my/5776/1/depositpermission_s94099.pdf
http://etd.uum.edu.my/5776/2/s94099_01.pdf
http://etd.uum.edu.my/5776/
_version_ 1644277428780007424
score 13.211869

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