基于改进萤火虫算法的WSN覆盖优化

WSN coverage optimization based on Improved Firefly Algorithm

摘要:

针对无线传感器网络节点部署不均所导致的网络覆盖率较低问题，以无线传感器网络覆盖率最大化为目标，提出一种基于改进萤火虫算法(IFA)的网络覆盖优化方法。该方法运用佳点集方法初始化种群，提高种群的多样性，奠定全局搜索基础；利用具有非线性指数递减的变形Sigmoid函数作为惯性权重，平衡算法的全局搜索和局部开发能力；采用高斯扰动策略对个体位置扰动更新，避免算法早熟。仿真结果表明，该算法与人工鱼群算法(AFSA)、种子杂交粒子群算法(HSPSO)和混沌萤火虫算法(CGSO)相比，能有效提高网络覆盖率，使节点部署分布更均匀。

Abstract:

Aiming at the problem of low network coverage caused by uneven deployment and distribution of Wireless Sensor Network nodes, with the goal of maximizing wireless sensor network coverage, a network coverage optimization strategy based on Improved Firefly Algorithm(IFA) is proposed. This method uses the good point set method to initialize the population, improve the diversity of the population and lay the foundation for the global search. Simultaneously, it uses the sigmoid function with non-linear exponential decline as the inertia weight to balance the global and local search capabilities of the algorithm. Then, Gaussian disturbance strategy is employed to perturb individual position update and avoid the premature of the algorithm. The simulation results indicate that compared with Artificial Fish Swarm Algorithm(AFSA), seed Hybrid Particle Swarm Optimization(HSPSO) and Chaotic Glowworm Swarm Optimization(CGSO), this algorithm effectively enhance the network coverage rate and make the WSN more evenly distributed.