基于改进蛙跳算法的无线传感器网络覆盖优化

Wireless Sensor Network Coverage Optimization Based on Improved Leapfrog Algorithm[HS)]

摘要:

针对传统算法在解决无线传感器网络覆盖优化上存在的覆盖率较低和节点分布不够均匀的问题,提出了一种改进的蛙跳算法；为了同时达到增加算法的种群多样性和加快算法收敛速度的目的,改进蛙跳算法分别增加了个体高斯学习机制和根据粒子群思想改进的更新策略,让族内最差个体在自身附近进行局部搜索,若无效,则使族内最差个体同时向族内最优个体和全局最优个体学习；在性能评估实验中,对改进的蛙跳算法分别进行了标准函数测试和无线传感器网络覆盖优化测试；测试结果表明,在6个标准测试函数中,改进的蛙跳算法与其他算法相比在4个测试函数上的收敛精度有了明显提高；在无线传感器网络覆盖优化中,改进的蛙跳算法也能够使节点分布更加均匀,使网络覆盖率达到了85.6%。 

Abstract:

To solve the problems that the nodes is non-uniform distribution and the coverage is incomplete in wireless sensor network by using the traditional method, an improved leap frog algorithm is proposed. In order to increase the population diversity and accelerate the algorithm convergence speed of the algorithm, the improved leapfrog algorithm respectively increased a gaussian learning mechanism and a improved update strategy based on particle swarm thought. Let the worst individual of groups search in their own local nearby, if invalid, the worst individual approach to the best individual of groups and the global optimal individual. In the performance evaluation experiments, the standard function test and wireless sensor network coverage optimization test were performed. The results show that, comparing with other algorithms, the convergence accuracy of improved leapfrog algorithm has increased significantly on four of the six test functions. What’s more, the improved leapfrog algorithm is more effective to solve wireless sensor network coverage problem. The distribution of nodes is more uniform, and the network coverage rate is up to 85.6%.