基于混沌量子粒子群算法的无线传感器网络覆盖优化

Coverage Optimization of Wireless Sensor Networks Based on Chaotic Quantum-Behaved Particle Swarm Algorithm

针对传统粒子群算法在求解无线传感器网络覆盖问题上存在的收敛速度慢、易陷入局部极值等缺陷,以提高传感器网络覆盖率为主要优化目标,提出了基于量子粒子群和Logistic混沌映射相结合的优化算法CQPSO。该算法基于量子δ势阱模型,同时引入精英个体适应值方差的早熟判断机制,提高了搜索效率。仿真结果表明,对比基本粒子群、混沌粒子群以及量子粒子群三种算法,该算法在覆盖率、均匀度以及平均移动距离指标方面具有更好的覆盖优化效果。

The conventional PSO algorithm has its own shortages of low convergence speed,sensitivity to local con-vergence in solving problems of the WSN coverage rate. To address these problems,based on the combined utiliza-tion of quantum-behaved particle swarm algorithm and logistic chaotic map,taking the network coverage rate as theoptimized goal,a hybrid optimal algorithm（chaos quantum-behaved particle swarm optimization,CQPSO）is pro-posed. By using the new model based delta potential and judging the local convergence by the variance of the eliteindividuals＇ fitness,the algorithm improves the search＇s efficiency and precision. Simulating results show that theproposed algorithm is superior to other algorithms（namely PSO,QPSO and CPSO algorithm）on the coverage rate,network evenness and average traveling distance.