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RFID网络规划问题是一个优化难题,文章给出一个含云生成算子的粒子群优化算法用于求解该问题.在该算法的子代生成框架中,新粒子通过云方式或PSO方式产生.(1)应用反向云生成算子,PSO认知种群被用于估计好解区域的期望、熵和超熵;(2)利用正向云生成算子,估计的期望、熵和超熵被用于生成云粒子;(3)来自PSO粒子的局部信息和来自云粒子的全局信息共同引导算法的下一步寻优.该算法优化文献中一些著名的RFID网络基准测试实例,实验结果显示该算法比原始的PSO有好的优化能力.
The problem of RFID network planning is an optimization problem. In this paper, a particle swarm optimization algorithm with cloud generation operator is proposed to solve this problem. In the generational generation framework of this algorithm, new particles are generated by cloud mode or PSO mode. 1) Applying the backward cloud generation operator, the PSO cognitive population is used to estimate the expected, entropy and hyper-entropy of the good-solution area; (2) Using the forward cloud to generate the operator, the estimated expectation, entropy and super-entropy are used (3) Local information from PSO particles and global information from cloud particles lead to the next optimization of the algorithm.The algorithm optimizes some well-known RFID network benchmark in the literature, the experimental results show that the algorithm The original PSO has good optimization capabilities.