一种混合麻雀搜索算法

元启发式算法由于可产生多样的解决方案在科学及工业领域受到了广泛的应用,麻雀搜索算法(SSA)是一种相对新颖的基于群体的元启发式算法,已被证明具有较好的寻优求解性能。由于在某些情况下麻雀种群多样性不足,导致算法寻优精度低,易陷入局部最优,因此提出了一种混合麻雀搜索算法(HSSA),首先利用反向对立学习策略提高初始种群质量,其次混合了模拟退火算法的Metropolis准则,避免算法陷入局部最优。为了验证算法的性能,利用HSSA对多个单峰和多峰测试函数进行求解,实验结果表明,与WOA、SSA和IPSO相比,HSSA具有更快的收敛速度和更高的求解精度。

A Hybrid Sparrow Search Algorithm

Meta-heuristic algorithm has been widely used in science and industry because it can produce a variety of solutions.The sparrow search algorithm(SSA)is a relatively new population-based meta-heuristic algorithm,which has been proved to have good performance in optimization.Due to the insufficient diversity of sparrow population in some cases,the optimization precision of the algorithm is low and it is easy to fall into the local optima.Therefore,the hybrid sparrow search algorithm(HSSA)is proposed,which firstly improves the initial population quality by using the Opposition-based learning strategy,and secondly the algorithm mixes the Metropolis criterion of simulated annealing algorithm to avoid the algorithm falling into local optimal.In order to verify the performance of the algorithm,HSSA was used to solve some unimodal and multimodal test functions.The experimental results show that compared with WOA,SSA and IPSO,the proposed HSSA has faster convergence rate and higher solution accuracy.