关键词: 混沌粒子群, 权重, 分簇, 量子粒子群

Abstract:  In order to solve the problems of low convergence speed and sensitivity to local convergence for
particle swarm optimization clustering routing algorithm, a new clustering routing algorithm based on chaotic-
quantum TSPSO(Two-Swarm Particle Swarm Optimization) algorithm is proposed. The cost function of the
optimal cluster head is chosen according to the energy of the cluster head, the distance between the cluster head
and the convergence node and the distance structure of the cluster node. The main particle swarm is optimized by
using the chaotic particle swarm optimization, making the particle swarm alternately transformed between the
stable and chaotic states. The subgroups are optimized by quantum particle swarm optimization and the quantum
wave theory making the algorithm has better global convergence. The concave function decreasing strategy is
adopted to optimize the weight in the algorithm of TSPSO. The convergence speed is accelerated. The simulation
results show that the proposed algorithm can balance the energy consumption of the wireless sensor network nodes
and extend the network life cycle significantly, and compare with LEACH(Low-Energy Adaptive Clustering
Hierarchy)and PSO-C(Cluster setup using Particle Swarm Optimization algorithm)respectively extend by 80. 1%
and 41. 4%.

Key words: chaotic particle swarm, quantum particle group, weights,  clustering