加入淘汰机制的改进麻雀搜索算法

Improved Sparrow Search Algorithmwith Elimination Mechanism

传统麻雀算法(sparrow search algorithm,SSA)具有搜索精度高、寻优能力强等优点,但同时也存在早熟收敛、迭代过程中容易陷入局部最优值等问题。针对这些问题,提出了一种加入Tent混沌映射和末位淘汰机制的麻雀搜索算法(sparrow search algorithm with tent,TESSA)。采用2N分段Tent混沌映射初始化种群位置。同时在算法迭代后期引入非线性末位淘汰机制,提高其收敛速度和精度。经过与其他4种群智能算法在6个基准函数上求解性能相比,TESSA的收敛速度、寻优精度、标准误差等性能指标有明显的优势。

The traditional Sparrow Algorithm(SSA)has the advantages of high search accuracy and strong optimization ability,but such problems as premature convergence and easy to fall into the local optimal value in the iterative process also exist.To solve these problems,a Sparrow Search Algorithm(TESSA)with Tent chaotic mapping and last place elimination mechanism is proposed.The 2N segmented Tent chaotic mapping is used to initialize the population position.At the same time,the nonlinear last place elimination mechanism is introduced in the later stage of the algorithm iteration to improve its convergence speed and accuracy.After comparing the performance of TESSA with other four population intelligent algorithms in solving six benchmark functions,the convergence speed,optimization accuracy,standard error and other performance indicators of TESSA have obvious advantages.