蜂群算法在太阳能电池参数辨识中的应用

为了解决太阳能电池参数辨识中参数识别精度低的问题,提出了采取基于侦查蜂阶段加入遗忘因子和邻域因子的人工蜂群算法(ABS)的解决方法.ABS算法在搜索的初期通过遗忘因子和邻域因子来使侦查蜂调整路径,从而能快速收敛到最优食物源所在区域,并使全局收敛性能在搜索后期有所提高.实验及分析表明:ABS算法的优化精度明显优于粒子群优化算法、模式搜索算法、模拟退火算法和遗传算法,为太阳能参数辨识提供了一种新的方法.

Application of Artificial Bee Swarm Algorithm in Solar Cell Parameters Identification

In order to solve the problem of low accuracy of the parameters identification of the solar cell,a new artificial bee swarm algorithm (ABS) based on scouts adjusting phase joining forgetting factor and neighborhood factor is proposed.ABS algorithm in the early stage of the search by forgetting factor and neighborhood factor can make scouts to adjust path,which can quickly converge to the optimal food source area and can improve the global convergence performance in late search.The experiment and analysis show that the optimized precision of ABS algorithm is superior to particle swarm optimization algorithm,the pattern search algorithm,simulated annealing algorithm and genetic algorithm.So a new method is provided for parameter identification.