  Dynamic behaviour of a WMN imposes stringent constraints on the routing
policy of the network. In the shortest path based routing the shortest paths
needs to be evaluated within a given time frame allowed by the WMN dynamics.
The exact reasoning based shortest path evaluation methods usually fail to meet
this rigid requirement. Thus, requiring some soft computing based approaches
which can replace "best for sure" solutions with "good enough" solutions. This
paper proposes a framework for optimal routing in the WMNs; where we
investigate the suitability of Big Bang-Big Crunch (BB-BC), a soft computing
based approach to evaluate shortest/near-shortest path. In order to make
routing optimal we first propose to replace distance between the adjacent nodes
with an integrated cost measure that takes into account throughput, delay,
jitter and residual energy of a node. A fuzzy logic based inference mechanism
evaluates this cost measure at each node. Using this distance measure we apply
BB-BC optimization algorithm to evaluate shortest/near shortest path to update
the routing tables periodically as dictated by network requirements. A large
number of simulations were conducted and it has been observed that BB-BC
algorithm appears to be a high potential candidate suitable for routing in
WMNs.
