改进自适应粒子群算法在WSN覆盖优化中的应用

Application of Improved Adaptive Particle Swarm Optimization Algorithm in WSN Coverage Optimization

针对无线传感器网络（WSN）节点覆盖不均匀导致覆盖率低下的问题,提出了一种基于改进自适应粒子群优化算法的覆盖优化方法。首先,建立WSN覆盖优化的数学模型;然后将进化因子和聚合因子引入粒子群优化（PSO）算法中的惯性权重系数,使改进算法具有很强的自适应能力;接着在算法迭代过程中引入碰撞回弹策略保证粒子群的多样性,克服改进粒子群优化算法在优化后期容易陷入局部最优的弱点。实验表明,本文算法对WSN优化后的网络覆盖率均比其它文献算法提高了2%-6%,且传感器节点分布更加均匀。因此它能有效提高无线传感器网络的性能,是一种应用性较强的WSN覆盖优化算法。

Aiming at the problem that the coverage rate of Wireless Sensor Network（WSN）is low due to the unevencoverage of nodes,a method of coverage optimization based on improved adaptive particle swarm optimization algo-rithm is proposed. Firstly,the mathematical model of WSN coverage optimization is established. Then,the evolutionaryfactor and the polymerization factor are introduced in the inertia weight coefficient of the particle swarm optimization（PSO）algorithm in order to make the improved algorithm have a strong adaptive ability. And then,the collision resilientstrategy is introduced in the iterative process of the algorithm in order to overcome the weakness that the improved parti-cle swarm optimization algorithm is easy to fall into local optimum in the late optimization. The experimental shows thatthe network coverage rates after optimizations of WSN by the algorithm in this paper are improved by 2%-6% com-pared with algorithms in other literatures and the distribution of sensor nodes is more uniform. Therefore,it can effec-tively improve the performance of wireless sensor networks,is a strong application coverage optimization algorithm.