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.