Novel Artificial Bee Colony Algorithms for QoS-Aware Service Selection

Wang, X; Xu, X; Sheng, QZ; Wang, Z; Yao, L

Novel Artificial Bee Colony Algorithms for QoS-Aware Service Selection

Wang, X

Xu, X Sheng, QZ Wang, Z Yao, L

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Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Services Computing, 2018, 12, (2), pp. 1-1
Issue Date:: 2018

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Field	Value	Language
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9582-3445
dc.contributor.author	Xu, X
dc.contributor.author	Sheng, QZ
dc.contributor.author	Wang, Z
dc.contributor.author	Yao, L
dc.date.accessioned	2020-07-30T04:57:22Z
dc.date.available	2020-07-30T04:57:22Z
dc.date.issued	2018
dc.identifier.citation	IEEE Transactions on Services Computing, 2018, 12, (2), pp. 1-1
dc.identifier.issn	1939-1374
dc.identifier.issn	1939-1374
dc.identifier.uri	http://hdl.handle.net/10453/141930
dc.description.abstract	© 2008-2012 IEEE. Service selection is crucial to service composition in determining the composite Quality of Service (QoS). The proliferation of composable services on the Internet and the practical need for timely delivering optimized composite solutions motivate the adoption of population-based algorithms for QoS-aware service selection. However, existing population-based algorithms are generally complicated to use, and often used as a general approach to solving different optimization problems. We propose to develop specialized algorithms for QoS-aware service selection, based on the artificial bee colony algorithm (ABC). ABC is a new and simpler implementation of swarm intelligence, which has proven to be successful in solving many real-world problems, especially the numerical optimization problems. We develop an approximate approach for the neighborhood search of ABC, which enables effective local search in the discrete space of service selection in a way that is analogical to the search in a continuous space. We present three algorithms based on the approach. All the three algorithms are designed to improve the performance and meanwhile preserve the simplicity of ABC. Each algorithm applies a different technique to leverage the unique characteristics of the service selection problem. Experimental results show higher accuracy and convergence speed of the proposed algorithms over the state of the art algorithms.
dc.language	English
dc.publisher	IEEE COMPUTER SOC
dc.relation.ispartof	IEEE Transactions on Services Computing
dc.relation.isbasedon	10.1109/TSC.2016.2612663
dc.rights	© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.title	Novel Artificial Bee Colony Algorithms for QoS-Aware Service Selection
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-07-30T04:57:17Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	2

Abstract:

© 2008-2012 IEEE. Service selection is crucial to service composition in determining the composite Quality of Service (QoS). The proliferation of composable services on the Internet and the practical need for timely delivering optimized composite solutions motivate the adoption of population-based algorithms for QoS-aware service selection. However, existing population-based algorithms are generally complicated to use, and often used as a general approach to solving different optimization problems. We propose to develop specialized algorithms for QoS-aware service selection, based on the artificial bee colony algorithm (ABC). ABC is a new and simpler implementation of swarm intelligence, which has proven to be successful in solving many real-world problems, especially the numerical optimization problems. We develop an approximate approach for the neighborhood search of ABC, which enables effective local search in the discrete space of service selection in a way that is analogical to the search in a continuous space. We present three algorithms based on the approach. All the three algorithms are designed to improve the performance and meanwhile preserve the simplicity of ABC. Each algorithm applies a different technique to leverage the unique characteristics of the service selection problem. Experimental results show higher accuracy and convergence speed of the proposed algorithms over the state of the art algorithms.

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http://hdl.handle.net/10453/141930