Grouped Quantum-inspired Particle Swarm Optimization algorithm for spectrum allocation in heterogeneous network
Up to now, classical Quantum Particle Swarm Optimization Algorithm in the late period of convergence has showed some drawbacks,such as population diversity reduce, convergence speed slow down and easy to fall into local optimal solution. This paper improves the classic QPSO algorithm and proposes Grouped Quantum-inspired Particle Swarm Optimization(G-QPSO). In this algorithm, quantum particles are grouped and regrouped periodically. Synthesizing the group optimal solution and the overall optimal solution, we update the speed and position of every quantum particle. We consider each solution vector as a viable spectrum allocation scheme and select the best one to achieve maximum value of Max-Sum-Reward(MSR) or Max-Proportional-Fair(MPF). As is shown in the simulation results, compared with the Genetic Type Algorithm, traditional Particle Swarm Optimization and Color-Sensitive-Graph Coloring Algorithm, this algorithm has better performance on the convergence speed and convergence precision, and avoids falling into the local optimal solution effectively.
Spectrum allocation quantum-particle swarm intelligence optimization algorithm G-QPSO Max-Sum-Reward Max-Proportional-Fair
Hua WEI Yong ZHANG Mei SONG Gang CHENG Chen CHENG
Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and Telecom China Unicom Research Institute, Beijing 100032, China
国际会议
秦皇岛
英文
1832-1837
2015-09-18（万方平台首次上网日期，不代表论文的发表时间）