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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2018

13.04.2018

Parallel Big Bang–Big Crunch Global Optimization Algorithm: Performance and its Applications to routing in WMNs

verfasst von: Shakti Kumar, Amar Singh, Sukhbir Walia

Erschienen in: Wireless Personal Communications | Ausgabe 4/2018

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Abstract

This paper presents the performance of Parallel Big Bang–Big Crunch (PB3C) global optimization algorithm on CEC-2014 test suite. The performance is compared with 16 other algorithms. It has been observed that PB3C gave best performance on 7 functions of the test bench. Out of seven, for 6 functions it gave the unmatched best performance whereas on one count its performance was equaled by other algorithm as well. Further this paper proposes a PB3C based new routing approach to wireless mesh networks (WMNs). Being dynamic; routing is a challenging issue in WMNs. The approach is a near shortest path route evaluation approach. The approach was simulated on MATLAB. The performance was compared with 7 other approaches namely ad hoc on-demand distance vector, dynamic source routing, ant colony optimization, biogeography based optimization, firefly algorithm, BAT and simple Big Bang–Big Crunch based approaches. For WMNs of size 1000 nodes and above the PB3C was observed to outperform rest of the 7 algorithms.
Vorheriger Artikel A Dual Polarized Triple Band Stacked Elliptical Microstrip Patch Antenna for WLAN Applications
Nächster Artikel Interdependency Analysis of Inter-Site-Distance on Configuration of Handover Control Parameter in LTE-A HetNets

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Metadaten
Titel
Parallel Big Bang–Big Crunch Global Optimization Algorithm: Performance and its Applications to routing in WMNs
verfasst von
Shakti Kumar
Amar Singh
Sukhbir Walia
Publikationsdatum
13.04.2018
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2018
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-018-5656-y

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