自适应混沌PSO算法在WSN覆盖优化中的应用

Application of Adaptive Chaos PSO Algorithm in WSN Coverage Optimization

粒子群算法容易陷入局部最优值和出现早熟现象,导致无线传感器网络覆盖不均匀和覆盖率较低。所以,提出了自适应混沌PSO算法。算法在基本的PSO算法中引入群体进化程度和相对聚集程度去控制惯性权重w,在算法迭代的过程中,当粒子会陷入局部最优时,引入混沌策略使粒子摆脱局部最优和早熟现象。实验结果表明,该算法能有效提高网络覆盖率,使无线传感器网络分布更均匀。

Particle swarm optimization is easy to fall into local optimum and prone to premature phenomenon, leading to uneven and fairly low coverage of wireless sensor networks, thus an adaptive chaotic PSO algorithm is proposed. The algorithm introduces the degree of group evolution and the degree of relative aggregation into the basic PSO algorithm,thus to control the inertia weight w. When the particles are likely to fall into the local optimum, a chaotic strategy is introduced into the process of algorithm iteration, thus to get the particles out of the local optimum and premature phenomenon. The experimental results indicate that the proposed algorithm could effectively improve the network coverage and make the wireless sensor network more evenly distributed.