基于混沌-量子粒子群的分簇路由算法

Chaotic-Quantum Behaved Particle Swarm Optimization Based Clustering Routing Algorithm

针对粒子群分簇路由优化算法存在的收敛速度慢、易陷入局部最优等问题,提出一种混沌-量子粒子群的双子粒子群分簇路由算法。该算法以簇头的能量、簇头与汇聚节点的距离以及与簇内成员节点的距离构造最优簇头的代价函数,主粒子群利用混沌粒子群寻优,辅粒子群利用量子粒子群寻优,加入量子波动理论,使算法具有较好的全局收敛性。双子粒子群采用收敛速度快的凹函数递减策略优化权重。仿真结果验证了该算法可使无线传感网络节点能量消耗均衡化,显著延长网络生命周期,与LEACH(Low-Energy Adaptive Clustering Hierarchy)协议、PSO-C(Cluster setup using Particle Swarm Optimization algorithm)协议相比生命周期分别延长了80.1%和41.4%。

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 chaoticquantum 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%.