改进人工大猩猩部队优化算法的WSN网络覆盖

WSN Network Coverage to Improve Optimization Algorithm of Artificial Gorilla Troops

为了建立一个保障网络通信需求以及低耗能的无线传感器网络,提出一种融合智能优化算法的网络部署方案。对部署区域进行二维投影,并结合概率感知模型设计出一种网络覆盖模型;针对人工大猩猩部队优化算法(GTO)在网络部署问题中存在收敛速度较慢以及全局探索能力不足的问题,提出一种改进人工大猩猩部队优化算法(IGTO)。该算法通过引入Tent混沌策略和复合突变策略,改善算法全局探索能力,增加算法收敛速度。在网络覆盖模型中进行仿真测试,验证网络覆盖率评价方案的有效性和工程实用性。结果表明,由IGTO算法部署的无线网络覆盖效率更高,节点分布更加均匀。

In order to establish a wireless sensor network that guarantees network communication requirements and low energy consumption,a network deployment scheme integrating intelligent optimization algorithms is proposed.The two-dimensional projection of the deployment area is used,and a network coverage model is designed in combination with the probabilistic perception model.Aiming at the problems of slow convergence speed and insufficient global exploration ability of artificial gorilla force optimization algorithm(GTO)in network deployment,an improved artificial gorilla force optimization algorithm(IGTO)is proposed.By introducing the Tent chaos strategy,the algorithm increases the convergence speed of the algorithm,adds a composite mutation strategy,and improves the global exploration ability of the algorithm.The simulation test is carried out in the network coverage model,and the network coverage is used to evaluate the effectiveness and engineering practicability of the scheme,and the results show that the wireless network deployed by IGTO algorithm has higher coverage efficiency and more uniform node distribution.