Network Route Optimization Using Particle Swarm Intelligence Algorithm

This paper presents a hybrid algorithm based on particle swarm optimization (PSO) intelligence algorithm and a Tabu search meta-heuristics algorithm for efficient network route optimization. This hybrid search process combines particle swarm optimization (PSO) for iteratively computing a population of better solutions and Tabu search method for diversifying the local search scheme to solve this problem. A priority based indirect encoding and decoding scheme based on heuristics has been used for representing the shortest path problem parameters as a particle in PSO. Tabu search based meta-heuristics have been integrated in order to enhance the overall search efficiency. Specifically, an iteration of the proposed hybrid algorithm consists of a standard PSO iteration and Tabu search based algorithm applied to each improved particle for local search, where the neighborhood of each such particle is explored with two neighborhood generating operations on particles in order to escape possible local minima and to diversify the search. Simulation results in several networks with random topologies are used to illustrate the efficiency of the proposed hybrid algorithm for the optimal route computation. The simulation result reveals that the proposed algorithm outperforms than the comparison algorithms used on result analysis.