基于改进蛙跳算法的无线传感器网络覆盖优化

发布时间：2018-03-16 13:41

本文选题：蛙跳算法　切入点：群体智能算法　出处：《计算机测量与控制》2014年06期 　论文类型：期刊论文

【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]:In order to solve the problems of low coverage and uneven distribution of nodes in wireless sensor network coverage optimization, an improved leapfrog algorithm is proposed. In order to increase the population diversity of the algorithm and accelerate the convergence rate of the algorithm, the improved leapfrog algorithm increases the individual Gao Si learning mechanism and the updating strategy according to the particle swarm theory, respectively. Let the worst individual in the family conduct local search near itself, if not, make the worst individual in the family learn from the best individual in the family and the global optimal individual at the same time; in the performance evaluation experiment, the worst individual in the family can learn from the best individual and the global optimal individual at the same time. The improved leapfrog algorithm is tested by standard function test and wireless sensor network coverage optimization test, and the test results show that, among the six standard test functions, Compared with other algorithms, the improved leapfrog algorithm can improve the convergence accuracy of the four test functions, and the improved leapfrog algorithm can make the nodes distribute more evenly in the coverage optimization of wireless sensor networks. The network coverage has reached 85.6%.
【作者单位】：开封大学软件学院;河南科技大学信息工程学院;
【基金】：国家自然科学基金项目(61142002)
【分类号】：TP18;TP212.9;TN929.5

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