无线传感网络覆盖的粒子进化优化策略研究

Research on the Strategy of Wireless Sensor Networks Coverage by the Particle Optimization Evolutionary

为了实现网络覆盖范围的最大化,延长网络寿命,本文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优化策略。通过多种群并行搜索,采取粒子进化理论使陷入局部最优的粒子迅速跳出,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。通过仿真实验分析了节点感知半径对覆盖性能指标的影响,覆盖率和收敛速度随着感知半径的增大逐渐增大和加快。仿真实验结果表明粒子进化的多粒子群优化策略比基本粒子群算法、传统遗传算法和新量子遗传算法具有更好的覆盖优化效果。

To maximize the network coverage and extend the life of the network, a Wireless Sensor Networks （WSNs） coverage optimal strategy is proposed based on the evolution of Multi-particle Particle Swarm Optimization （MPSO）. By using the method of Multi-groups parallel searching, the particles, which fall into the best part area according to the theory of evolution,can be chosen rapidly. The strategy also avoids a phenomenon of ＂premature＂ which often occurs when using the method of elementary Particle Swarm Optimization （PSO）, and improves the stability of the algorithm. In the paper, the influence about perceived radius of the nodes on the coverage performance is analyzed through the simulation experiment. Coverage rate and convergence rate increases as the radius of perception speeds up gradually. Experimental results indicate that the MPSO strategy is better than PSO, the Conventional Genetic Algorithms （CGA）, the New Quantum Genetic Algorithm （NQGA） in coverage optimization.