群混合算法应用于异构传感网络节点的优化部署

Swarm hybrid algorithm for nodes optimal deployment in heterogeneous wireless sensor network

针对异构传感网络节点初始随机部署时产生覆盖盲区和覆盖冗余的问题,以降低节点成本和提高网络覆盖率为目标,引入ε-目标约束法,提出一种基于粒子群算法和鱼群算法的群混合算法。该群混合算法首先建立个体中心的概念,将鱼群算法的聚群行为和追尾行为的思想引入到粒子群算法中以快速寻取个体的最优位置的解域,再利用粒子群算法对个体的速度和位置进行迭代寻优。仿真结果表明,该群混合算法与标准粒子群算法和标准鱼群算法相比,在网络覆盖率和成本目标之间能达到更好的平衡和优化。

The coverage problem is a basic problem in the wireless sensor networks, which indicates the Quality of Service （QoS） of sensing by wireless sensor networks. A lot cover blind areas and cover redundancies will be produced, when the nodes are deployed initially in the networks. A hybrid algorithm was proposed to deploy the heterogeneous network nodes reasonably to improve the coverage ratio and reduce the cost of the nodes, which introduced the ^-target constraint method based on Particle Swarm Optimization （PSO） and Fish Swarm Algorithm （FSA）. The swarm hybrid algorithm firstly set up the concept of individual center, to quickly search the best solution domain of the individuals＇ locations, introducing the idea of the cluster behavior and tracing cauda behavior into the PSO, and then used the PSO to find the optimized speed and optimized location of the individuals. The simulation results show that the swarm hybrid algorithm is better than the standard PSO and the standard FSA in pursuing the balance and optimization between the coverage ratio and the cost of the networks.