基于布谷鸟差分算法优化的DV-Hop改进算法

Localization Method Based on Modified Cuckoo Difference Optimization for Wireless Sensor Networks

经典DV-Hop定位算法中,三边测量法虽然避免了迭代运算,但对信标节点的依赖性较大;而极大似然估计法存在对误差进行累加的问题。针对传统定位算法存在的问题,提出了基于布谷鸟(CS)差分(DE)优化的DV-Hop改进算法,将定位问题转化为群体优化问题,利用CS和DE算法进行双种群并行搜索,动态调整CS中宿主发现入侵者的概率参数,随机缩放DE算法中变异因子,增强全局搜索能力,规避了距离误差在定位过程中的累加,有效提高了定位精度。

To solve the sensitive and accumulative ranging error issue in the trilateration method and the maximum likelihood estimation method for the DV-Hop localization algorithm, an algorithm based on the Cuckoo difference optimization was proposed. The proposed algorithm essentially turned the positioning calculation problem into a group optimization problem. Using the Cuckoo algorithm and the differential evolution algorithm for parallel optimization with double populations, the proposed algorithm fused the advantages of the two kinds of intelligent optimization algorithms, which dynamically rectified the abandoned factor and the scaled variation factor randomly at the same time. The Cuckoo differential evolution algorithm＇s global search ability was enhanced to maintain the population diversity, which made the estimated coordinates closer to the real values. Without any increase in communication overhead, the positioning precision was improved effectively.