电力通信网络中QoS多播路由优化改进量子进化算法

his thesis studies the requirements of electric power communication system, and classifies traffic in the electric power system. An Advanced Quantum Evolutionary Algorithm (AQEA) for QoS multicast routing optimization is proposed to fulfill the requirements of multicast traffic in electric power communication network, e.g. bandwidth, delay, packet loss, etc. Our approach combines Quantum Evolutionary Algorithm and Minimum Spanning Tree algorithm. First, the current location of individual is represented by probability amplitudes of quantum bits. Quantum crossover is implemented in quantum individuals to keep better gene. Second, quantum gates update and adaptive adjustment of the searching area is achieved according to the phase of quantum bit. A dynamic adjusting mechanism of rotation angle is designed to update the individual pheromone, which can guarantee the strong population diversity and quickly find out the feasible solutions that satisfy all constraints as well. It overcomes the restriction of local optimization in traditional algorithms. Thirdly, Steiner minimal tree is generated with OMST (Optimized Minimum Spanning Tree) algorithm, which ensures a better performance of solutions in precision and speed. Simulations show AQEA has better optimization quality and efficiency in comparing with traditional Ant Colony Algorithm and Quantum Evolutionary Algorithm. Simulation results manifest that the cost and convergence time of the multicast tree obtained by AQEA superior to other evolutionary algorithms. This trend will be more distinct as the nodes increase. Simulations also declare the validity of the strategies in AQEA.