动态优化问题的瞬态自适应麻雀搜索算法求解

Transient adaptive sparrow search algorithm for dynamic optimization problem

动态优化普遍存在于工业过程控制领域,是实现系统稳态与产值最大化的重要手段,应用并发展更加高效的动态优化方法逐渐成为了当前研究的热点。鉴于此,提出一种基于瞬态自适应麻雀搜索算法(TASSA)的动态优化问题求解方案。首先,分析了原始麻雀搜索算法的缺陷,为了提升全局勘探能力,引入瞬态搜索策略指导加入者的寻优过程;其次,采用随迭代而变化的惯性权重调节具体的搜索方式,增强了算法的动态适应能力,并通过九组基准函数的数值测试确认了改进策略的有效性;最后,采用时域等分的方式,在控制变量参数化(CVP)的框架下利用TASSA对三组典型的动态优化问题进行求解,对比不同文献中的方法,所提算法取得了更精确的结果。

Dynamic optimization widely exists in the field of industrial process control,which is an important means to realize the system’s steady-state and maximize the output value.The application and development of more efficient dynamic optimization methods have gradually become a hot spot of current research.This paper proposed a dynamic optimization solution based on the transient adaptive sparrow search algorithm(TASSA).Firstly,it analyzed the defects of the original sparrow search algorithm.To improve the global exploration ability,it introduced a transient search strategy to guide the optimization process of the entrants.Secondly,it used the inertia weight that changed with iterations to adjust the specific search method,which enhanced the dynamic adaptability of the algorithm.This paper confirmed the effectiveness of the improved strategy through the numerical test of nine groups of benchmark functions.Finally,it used TASSA to solve three groups of typical dynamic optimization problems in the framework of control variable parameterization(CVP)by time-domain equal division.Compared with different algorithm proposed by references,TASSA achieves more accurate results.