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



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.





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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computational Intelligence LaboratoryBaddi University of Emerging Sciences and TechnologyBaddiIndia
  2. 2.I.K. Gujral Punjab Technical UniversityJalandharIndia

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