Routing Optimization Based on Artificial Fish Swarm Algorithm

For multi-objective optimization in the QoS routing, this paper combines the artificial fish swarm algorithm and ant colony algorithm and tabu search algorithm, proposes a new improved algorithm, and delves into the application of solving the QoS routing. One main work in this paper is to put forward a mixed algorithm integrating artificial fish swarm and ant colony. Firstly, we randomly generate a network topology by the improved Salama algorithm. Secondly, we find out many feasible paths that satisfies constraint conditions by the parallel search feature of ant colony algorithm. Thirdly, we create the alternative path set. Lastly, we use artificial fish swarm algorithm to solve the optimum multicast tree in the created alternative path set through the foraging, bunching, tailgating, and other behaviors. The other main work in this paper is to put forward a mixed algorithm integrating the artificial fish swarm and the tabu search. Firstly, we divide the region needs to be searched into several sub regions and rapidly solve suboptimum solutions in each sub region with the fast convergence capacity. Secondly, we solve suboptimum solutions in each sub region by taking suboptimum solutions as initial solutions in the tabu search algorithm. Thirdly and lastly, we sort the optimal solutions in each sub region with the bubble sort and thus solve the global optimal solution.