  In Multi-Channel Multi-Radio Wireless Mesh Networks (MCMR-WMN), finding the
optimal routing by satisfying the Quality of Service (QoS) constraints is an
ambitious task. Multiple paths are available from the source node to the
gateway for reliability, and sometimes it is necessary to deal with failures of
the link in WMN. A major challenge in a MCMR-WMN is finding the routing with
QoS satisfied and an interference free path from the redundant paths, in order
to transmit the packets through this path. The Particle Swarm Optimization
(PSO) is an optimization technique to find the candidate solution in the search
space optimally, and it applies artificial intelligence to solve the routing
problem. On the other hand, the Genetic Algorithm (GA) is a population based
meta-heuristic optimization algorithm inspired by the natural evolution, such
as selection,mutation and crossover. PSO can easily fall into a local optimal
solution, at the same time GA is not suitable for dynamic data due to the
underlying dynamic network. In this paper we propose an optimal intelligent
routing, using a Hybrid PSO-GA, which also meets the QoS constraints. Moreover,
it integrates the strength of PSO and GA. The QoS constraints, such as
bandwidth, delay, jitter and interference are transformed into penalty
functions. The simulation results show that the hybrid approach outperforms PSO
and GA individually, and it takes less convergence time comparatively, keeping
away from converging prematurely.
  Keywords: Wireless mesh networks, Multi-radio, Multi-channel, Particle swarm
optimization, Genetic algorithm, Quality of service.
