基于改进自适应PSO算法的WSN覆盖优化方法

在标准粒子群优化（particle swarm optimization, PSO）算法的基础上提出了一种带有动态惯性权重的自适应粒子群算法, 以实现移动WSN对被监测区域的覆盖。新算法引入了粒子群进化度因子和粒子群聚合度因子, 这两个因子的数值主要受粒子群的平均适应值、局部最优值和全局最优值影响。使用这两个因子调整惯性权重会使算法带有一定的自适应性, 这种自适应性使得算法在迭代过程中既不会因步长过小而局部收敛, 也不会因步长过大而跳过待求解问题的最优值。仿真结果表明, 相比标准PSO算法, 改进后的自适应PSO算法使移动WSN的覆盖率提升了5%~8%。

Improving coverage of wireless sensor network using enhancedadaptive PSO algorithm

To achieve the maximum coverage, this paper proposed a novel adaptive particle swarm optimization (PSO) technique with dynamic inertia weights. It introduced two parameters in the new algorithm, which were the degree of evolutionary and the degree of polymerization, to adjust the dynamic inertia weights in an adaptive manner. The use of these two parameters reduced the probability that the PSO algorithm was trapped in local minima due to decreased step size as well as the chance that the PSO algorithm missed the globally optimal solution because of larger step size. Simulation results show that the new proposed algorithm improves the WSN coverage ratio by an amount of 5%~8%, compared with the standard PSO approach.