智能变电站无线监测网络群优化算法研究

A Research on Optimization Algorithm of Wireless Monitoring Network Group in Smart Substation

针对目前物联网技术智能电网中搜索范围大、局部极值点多、难以选出最优解等问题,提出了基于捕食搜索策略的粒子群及萤火虫(P-GSO)和模糊层次的联合优化算法:采用标准粒子群算法和萤火虫算法相结合的方法弥补二者在全局和局部寻优中存在的不足,并利用模糊层次分析法计算目标函数的权重,计算出更有效的网络生命周期.理论分析和仿真结果表明,在大范围搜索和多极点的条件下,联合算法较PSO和GSO能够有效地减少搜索时间,并使结果的精度随着群搜索规模的增加逐渐提高.

The smart grid which is based on the technology of Internet of things has many problems,such as large search area,many local extreme points,and difficult to select the optimal solution. So this paper proposes the combined optimization algorithm based on predatory search strategy particle swarm optimization algorithm and the firefly algorithm（P-GSO） and fuzzy analytic hierarchy process： using the standard particle swarm algorithm and the method of combining the firefly algorithm for both the deficiencies in the global and local optimization,and through the fuzzy analytic hierarchy process to calculate the weights of the objective function,then calculating the more efficient network life cycle. Theoretical analysis and simulation results show that under the condition of large scope search and multi pole,the combined algorithm is better than the PSO and GSO can effectively reduce the search time,and the accuracy of the results with the gradual increase in swarm search scale increases.