Abstract
The need of effective packet transmission to deliver advanced performance in wireless networks creates the need to find shortest network paths efficiently and quickly. This paper addresses a reduced uncertainty-based hybrid evolutionary algorithm (RUBHEA) to solve dynamic shortest path routing problem (DSPRP) effectively and rapidly. Genetic algorithm (GA) and particle swarm optimization (PSO) are integrated as a hybrid algorithm to find the best solution within the search space of dynamically changing networks. Both GA and PSO share context of individuals to reduce uncertainty in RUBHEA. Various regions of search space are explored and learned by RUBHEA. By employing a modified priority encoding method, each individual in both GA and PSO are represented as a potential solution for DSPRP. A complete statistical analysis has been performed to compare the performance of RUBHEA with various state-of-the-art algorithms. It shows that RUBHEA is considerably superior (reducing the failure rate by up to 50%) to similar approaches with increasing number of nodes encountered in the networks.