Quantum-inspired evolutionary multicast algorithm
Yangyang Li, Licheng Jiao, Jingjing Zhao, Qiuyi Wu · 2009
As a global optimizing algorithm, genetic algorithm (GA) is applied to solve the problem of multicast more and more. GA has more powerful searching ability than traditional algorithm, however its property of “prematurity” makes it difficult to get a good multicast tree. A quantum-inspired evolutionary algorithm (QEA) to deal with multicast routing problem is presented in this paper, which saliently solves the “prematurity” problem in Genetic based multicast algorithm. Furthermore, in QEA, the individuals in a population are represented by multistate gene quantum bits and this representation has a better characteristic of generating diversity in population than any other representations. In the individual's updating, the quantum rotation gate strategy is applied to accelerate convergence. The algorithm has the property of simple realization and flexible control. The simulation results show that QEA has a better performance than CS and conventional GA.