A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm

This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined...

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Main Authors:	Peibo, Duan, Changsheng, Zhang, Bin, Zhang
Format:	Online
Language:	English
Published:	Hindawi Publishing Corporation 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4106159/

id	pubmed-4106159
recordtype	oai_dc
spelling	pubmed-41061592014-08-07 A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm Peibo, Duan Changsheng, Zhang Bin, Zhang Research Article This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time. Hindawi Publishing Corporation 2014 2014-07-03 /pmc/articles/PMC4106159/ /pubmed/25105166 http://dx.doi.org/10.1155/2014/734975 Text en Copyright © 2014 Duan Peibo et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Peibo, Duan Changsheng, Zhang Bin, Zhang
spellingShingle	Peibo, Duan Changsheng, Zhang Bin, Zhang A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm
author_facet	Peibo, Duan Changsheng, Zhang Bin, Zhang
author_sort	Peibo, Duan
title	A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm
title_short	A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm
title_full	A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm
title_fullStr	A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm
title_full_unstemmed	A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm
title_sort	local stability supported parallel distributed constraint optimization algorithm
description	This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time.
publisher	Hindawi Publishing Corporation
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4106159/
_version_	1613116497421402112

A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm

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