Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25011
DC FieldValueLanguage
dc.contributorDepartment of Industrial and Systems Engineering-
dc.creatorZhang, X-
dc.creatorWang, Q-
dc.creatorAdamatzky, A-
dc.creatorChan, FTS-
dc.creatorMahadevan, S-
dc.creatorDeng, Y-
dc.date.accessioned2015-05-26T08:16:57Z-
dc.date.available2015-05-26T08:16:57Z-
dc.identifier.issn2356-6140en_US
dc.identifier.urihttp://hdl.handle.net/10397/25011-
dc.language.isoenen_US
dc.publisherHindawi Publishing Corporationen_US
dc.rightsCopyright © 2014 Xiaoge Zhang et al. 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.en_US
dc.rightsThe following article: Zhang, X., Wang, Q., Adamatzky, A., Chan, F. T., Mahadevan, S., & Deng, Y. (2014). An improved physarum polycephalum algorithm for the shortest path problem. The Scientific World Journal, 2014, is available at https//doi.org/10.1155/2014/487069en_US
dc.titleAn improved Physarum polycephalum algorithm for the shortest path problemen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume2014en_US
dc.identifier.doi10.1155/2014/487069en_US
dcterms.abstractShortest path is among classical problems of computer science. The problems are solved by hundreds of algorithms, silicon computing architectures and novel substrate, unconventional, computing devices. Acellular slime mould P. polycephalum is originally famous as a computing biological substrate due to its alleged ability to approximate shortest path from its inoculation site to a source of nutrients. Several algorithms were designed based on properties of the slime mould. Many of the Physarum-inspired algorithms suffer from a low converge speed. To accelerate the search of a solution and reduce a number of iterations we combined an original model of Physarum-inspired path solver with a new a parameter, called energy. We undertook a series of computational experiments on approximating shortest paths in networks with different topologies, and number of nodes varying from 15 to 2000. We found that the improved Physarum algorithm matches well with existing Physarum-inspired approaches yet outperforms them in number of iterations executed and a total running time. We also compare our algorithm with other existing algorithms, including the ant colony optimization algorithm and Dijkstra algorithm.-
dcterms.bibliographicCitationThe scientific world journal, 2014, v. 2014, 487069-
dcterms.isPartOfThe scientific world journal-
dcterms.issued2014-
dc.identifier.isiWOS:000333917600001-
dc.identifier.scopus2-s2.0-84899435228-
dc.identifier.pmid24982960-
dc.identifier.eissn1537-744Xen_US
dc.identifier.rosgroupidr69004-
dc.description.ros2013-2014 > Academic research: refereed > Publication in refereed journalen_US
dc.description.oaNot applicableen_US
Appears in Collections:Journal/Magazine Article
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