Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84803
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dc.contributor.authorKarthikeyan, P.en
dc.contributor.authorBaskar, S.en
dc.contributor.authorAlphones, Arokiaswamien
dc.date.accessioned2013-07-23T03:15:44Zen
dc.date.accessioned2019-12-06T15:51:21Z-
dc.date.available2013-07-23T03:15:44Zen
dc.date.available2019-12-06T15:51:21Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationKarthikeyan, P., Baskar, S., & Alphones, A. Improved genetic algorithm using different genetic operator combinations (GOCs) for multicast routing in ad hoc networks. Soft computing.en
dc.identifier.issn1432-7643en
dc.identifier.urihttps://hdl.handle.net/10356/84803-
dc.description.abstractIn this paper, a Modified Topology Crossover (MTC), Energy-II and Energy-III mutations and Genetic Operator Combinations (GOCs) for integer coded Genetic Algorithm (GA) with sequence and topological representations are proposed to improve the efficiency of the GA for multicast routing in ad hoc networks. Combined lifetime improvement and time delay minimization are considered as objectives. To study the effect of genetic operators on the performance of multicast routing optimization problem, crossover methods such as sequence and topology crossover, topology crossover and mutation methods such as node mutation, energy mutation, inverse mutation and insert mutation are considered. Penalty parameter-less constraint handling scheme is used for handling the number of broken links which are identified during reproduction. The simulations are conducted on different size graphs generated using Waxman’s graph generator. Three case studies namely Case-1: Performance comparison of various crossover methods with node mutation, Case-2: Performance comparison of various mutation methods with the proposed MTC and Case-3: Performance comparisons of four GOCs are investigated. The above three cases are experimented with nonparametric statistical tests such as Friedman, Aligned Friedman and Quade. From these tests, it is proved that GOCs perform better for both large scale and small scale networks. These results also endorse that the proposed GOCs can be used to improve the GA for solving multicast routing problems more effectively.en
dc.language.isoenen
dc.relation.ispartofseriesSoft computingen
dc.rights© 2012 Springer-Verlag Berlin Heidelberg.en
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleImproved genetic algorithm using different genetic operator combinations (GOCs) for multicast routing in ad hoc networksen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doi10.1007/s00500-012-0976-4en
item.fulltextNo Fulltext-
item.grantfulltextnone-
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