Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem

dc.contributor.authorMohiuddin, Mohammad A.
dc.contributor.authorKhan, Salman Ahmad
dc.contributor.authorEngelbrecht, Andries P.
dc.contributor.emailengel@cs.up.ac.zaen_ZA
dc.date.accessioned2016-11-17T09:12:17Z
dc.date.issued2016-10
dc.description.abstractThe open shortest path first (OSPF) routing protocol is a well-known approach for routing packets from a source node to a destination node. The protocol assigns weights (or costs) to the links of a network. These weights are used to determine the shortest paths between all sources to all destination nodes. Assignment of these weights to the links is classified as an NP-hard problem. The aim behind the solution to the OSPF weight setting problem is to obtain optimized routing paths to enhance the utilization of the network. This paper formulates the above problem as a multi-objective optimization problem. The optimization metrics are maximum utilization, number of congested links, and number of unused links. These metrics are conflicting in nature, which motivates the use of fuzzy logic to be employed as a tool to aggregate these metrics into a scalar cost function. This scalar cost function is then optimized using a fuzzy particle swarm optimization (FPSO) algorithm developed in this paper. A modified variant of the proposed PSO, namely, fuzzy evolutionary PSO (FEPSO), is also developed. FEPSO incorporates the characteristics of the simulated evolution heuristic into FPSO. Experimentation is done using 12 test cases reported in literature. These test cases consist of 50 and 100 nodes, with the number of arcs ranging from 148 to 503. Empirical results have been obtained and analyzed for different values of FPSO parameters. Results also suggest that FEPSO outperformed FPSO in terms of quality of solution by achieving improvements between 7 and 31 %. Furthermore, comparison of FEPSO with various other algorithms such as Pareto-dominance PSO, weighted aggregation PSO, NSGA-II, simulated evolution, and simulated annealing algorithms revealed that FEPSO performed better than all of them by achieving best results for two or all three objectives.en_ZA
dc.description.departmentComputer Scienceen_ZA
dc.description.embargo2017-10-31
dc.description.librarianhb2016en_ZA
dc.description.urihttp://link.springer.com/journal/10489en_ZA
dc.identifier.citationMohiuddin, M.A., Khan, S.A. & Engelbrecht, A.P. Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem. Applied Intelligence (2016) 45: 598-621. doi:10.1007/s10489-016-0776-0.en_ZA
dc.identifier.issn0924-669X (print)
dc.identifier.issn1573-7497 (online)
dc.identifier.other10.1007/s10489-016-0776-0
dc.identifier.urihttp://hdl.handle.net/2263/58128
dc.language.isoenen_ZA
dc.publisherSpringeren_ZA
dc.rights© Springer Science+Business Media New York 2016. The original publication is available at : http://link.springer.comjournal/10489.en_ZA
dc.subjectParticle swarm optimization (PSO)en_ZA
dc.subjectSwarm intelligenceen_ZA
dc.subjectMulti-objective optimizationen_ZA
dc.subjectFuzzy logicen_ZA
dc.subjectOpen shortest path first (OSPF)en_ZA
dc.subjectFuzzy particle swarm optimization (FPSO)en_ZA
dc.subjectFuzzy evolutionary PSO (FEPSO)en_ZA
dc.titleFuzzy particle swarm optimization algorithms for the open shortest path first weight setting problemen_ZA
dc.typePostprint Articleen_ZA

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Mohiuddin_Fuzzy_2016.pdf
Size:
425.14 KB
Format:
Adobe Portable Document Format
Description:
Postprint Article

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description: