GAPN-LA: A framework for solving graph problems using Petri nets and learning automata

A fusion of learning automata and Petri nets, referred to as APN-LA, has been recently introduced in the literature for achieving adaptive Petri nets. A number of extensions to this adaptive Petri net have also been introduced; together we name them the APN-LA family. Members of this family can be u...

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Bibliographic Details
Published in:Engineering applications of artificial intelligence Vol. 77; pp. 255 - 267
Main Authors: Vahidipour, S.M., Esnaashari, M., Rezvanian, A., Meybodi, M.R.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2019
Subjects:
Adaptive Petri net
Graph-based problems
Learning automata
Modular algorithms
Modular algorithms
Graph-based problems
Adaptive Petri net
Learning automata
ISSN:0952-1976, 1873-6769
Online Access:Get full text
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Summary:A fusion of learning automata and Petri nets, referred to as APN-LA, has been recently introduced in the literature for achieving adaptive Petri nets. A number of extensions to this adaptive Petri net have also been introduced; together we name them the APN-LA family. Members of this family can be utilized for solving problems in the domain of graph problems; each member is suitable for a specific category within this domain. In this paper, we aim at generalizing this family into a single framework, called generalized APN-LA (GAPN-LA), which can be considered as a framework for solving graph-based problems. This framework is an adaptive Petri net, organized into a graph structure. Each place or transition in the underlying Petri net is mapped into exactly one vertex of the graph, and each vertex of the graph represents a part of the underlying Petri net. A vertex in GAPN-LA can be considered as a module, which, in cooperation with other modules in the framework, helps in solving the problem at hand. To elaborate the problem-solving capability of the GAPN-LA, several graph-based problems have been solved in this paper using the proposed framework.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2018.10.013