| QoS multicast routing problem with multiple constraints is a key issue fordeveloping the next generation Internet. There are very important application value andacademic value to study QoS multicast routing problem with multiple constraints,which is proved as a NP hard problem. Therefore, traditional graph theory method couldnot deal with it. Developing heuristic algorithms is an excellent choice. In the lastdecade, the popular intelligent algorithms have been widely used due to their excellentoptimization performance. So far, all of the intelligent algorithms almost have beenapplied to solve the problem of multi-constrained QoS multicast routing problem.Nevertheless, the existing intelligent algorithms still have many shortcomings, such asimmature convergence, prone to stagnation, time-consuming and too complex and soon.In this paper, aiming at the drawbacks of intelligent algorithms, We study them andget some research works, including:①Aiming at the existing genetic algorithm applied to multi-constrained QoSmultcast routing problem have many drawbacks such as slow convergence speed, easilyfalling into local optimum etc,We improve the population initialization, crossover andthe mutation operation to enhance its performance. And then apply it to solveeight-node network instance to demonstrate the effectiveness of improved geneticalgorithm.②In order to overcome the drawbacks of the glowworm swarm optimizationapplied to multi-constrained QoS multicast routing problem when there is atime-consuming, poor self-adaptability and robustness defects, We improve the methodat fluorescein updating, dynamic decision domain updating, and neighbors updating. Weuse the eight-node instance to test the improved glowworm swarm algorithm. Andexperiments show that the proposed algorithms are effective.③An improved Salama algorithm is proposed to simulate the Internet. And thentest the two improved algorithms on the stimulating Internet. Experimental simulationsshow their superiority. |
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