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为了改善无线传感器网络(WSN)中节点随机部署不均匀,提高网络的覆盖性能。针对传统粒子群优化算法容易陷入局部极值和收敛速度慢等不足,通过研究种群多样性与粒子群算法进化的关系,提出了一种动态自适应混沌量子粒子群优化算法(Dynamic Self-Adaptive Chaotic Quantum-Behaved Particle Swarm Optimization,DACQPSO)。该算法将种群分布熵引入粒子群的进化控制,以sigmoid函数模型为基础,给出了量子粒子群算法收缩扩张系数(CE coefficient)的计算方法;以平均粒距作为混沌搜索的判别条件进行混沌扰动。将DACQPSO算法应用于WSN的覆盖优化中,并做了仿真分析。实验结果表明,DACQPSO算法在覆盖率指标上比标准粒子群、量子粒子群、混沌量子粒子群算法分别提高了2.825%、2.25%和1.625%,有效地提高了WSN的覆盖性能。
In order to improve the random deployment of nodes in Wireless Sensor Network (WSN), improve network coverage performance. To overcome the shortcomings of traditional particle swarm optimization algorithms such as easy falling into local extremum and slow convergence speed, a dynamic adaptive chaotic quantum particle swarm optimization (Dynamic Self-Adaptive Chaotic) algorithm is proposed by studying the relationship between swarm diversity and particle swarm evolution. Quantum-Behaved Particle Swarm Optimization, DACQPSO). In this algorithm, the population distribution entropy is introduced into the evolutionary control of particle swarm optimization. Based on the sigmoid function model, the calculation method of the CE coefficient of the quantum particle swarm optimization algorithm is given. The average particle distance is used as the chaos search criteria to determine the chaos Disturbed. The DACQPSO algorithm is applied to the coverage optimization of WSN, and the simulation analysis is done. The experimental results show that the DACQPSO algorithm improves the coverage performance of WSNs by 2.825%, 2.25% and 1.625% respectively than the standard PSO, QPSO and Chaos Quantum Particle Swarm Optimization.