基于多粒子群算法的WSNs覆盖优化策略研究*

为了改善无线传感网络的性能，提高网络的覆盖率，在粒子进化的多粒子群算法的基础上，提出了一种无线传感网络覆盖的优化策略。该策略通过多个粒子群彼此独立地搜索解空间, 提高了算法的寻优能力，有效地避免了基本粒子群算法容易出现的“早熟”问题,提高了算法的稳定性。仿真实验表明，与基本粒子群算法、传统遗传算法和新量子遗传算法的优化效果相比较，其覆盖率分别提高了8.39％、3.07％和0.75％；收敛速度提高了25.3％、23.8％和23.8％。因此粒子进化的多粒子群优化策略具有比这三种算法更好的覆盖优化效果。

Research on strategy for optimizing coverage of WSNs based on multi-particle PSO

In order to improve the network performance and increase network coverage rate, the present research proposed a wireless sensor networks (WSNs) coverage optimization strategy on the basis of the evolution of multi-particle particle swarm optimization (MPSO).By adopting a number of particles independently searching for solutions space, the strategy improved the optimization of the algorithm ability and thus effectively avoid the premature so that the stability of the algorithm was improved. The simulation shows, compared with elementary particle swarm optimization(PSO),the conventional genetic algorithms (CGA) and quantum of the new genetic algorithm (NQGA) interms of the optimization effectiveness,that the coverage rate increased by 8.39%,3.07% and 0.75% and convergence rate increased 25.3% and 23.8%.Therefore, the MPSO strategy has a better coverage optimization results than PSO, CGA and NQGA.