A Clustering Routing Algorithm Based on PSO and Moss Growth Optimization in WSNs

Wireless sensor networks (WSNs) have the advantages of flexibility and convenience, low cost, and high scalability, but there are also problems such as limited transmission distance and high energy consumption. In order to solve the above problems, a hybrid clustering routing algorithm called adaptive Levy-Cauchy mutation moss particle growth optimization (ALC-MPGO) is proposed. Firstly, the levy flight mechanism is introduced in PSO, together with the particle swarm idea augmented by linear weight factor and adaptive acceleration factor to balance the search accuracy and speed of the algorithm and enhance the network lifetime. Secondly, the cauchy mutation strategy is introduced in MGO so as to effectively improve the convergence speed of the algorithm. Finally, the improved two algorithms are combined to optimize the data transmission path. Simulation analysis shows that the proposed algorithm performs well in terms of average residual energy of nodes, total residual energy of the network, and the number of surviving nodes.