一种改进的麻雀搜索算法

An Improved Sparrow Search Algorithm

麻雀搜索算法(SSA)作为一种新颖的群体智能优化算法,已被证明具有较好的寻优性能。但由于SSA在某些情况下迭代中后期搜索性减小,种群多样性降低,导致算法存在收敛速度慢、求解精度低、易陷入局部最优解等不足。针对SSA存在的缺陷,融合萤火虫算法(FA)迭代策略,提出了一种加入萤火虫搜索扰动的麻雀搜索优化算法(FSSA)。首先,在麻雀搜索后,利用萤火虫扰动策略对种群中所有个体进行位置更新,使得算法在解空间搜索更加充分,有效避免陷入局部最优,进而提升算法的收敛速度以及收敛精度。其次,通过6个基准测试函数对改进算法与粒子群优化算法(PSO)、鲸鱼优化算法(WOA)、原始的SSA算法进行对比,仿真结果表明该算法能够克服SSA易陷入局部最优的不足,在寻优精度、收敛速度以及鲁棒性等方面均获提升。最后,将FSSA应用于具有14座城市的旅行商问题(TSP)求解,仿真实验对比原始的SSA算法,该算法具有更好的结果,进一步验证了FSSA的寻优能力。

Sparrow search algorithm(SSA),as a novel swarm intelligence optimization algorithm,has been proved to be effective in searching.However,in some cases,the search ability and population diversity of SSA are reduced in the middle and late iterations,resulting in slow convergence speed,low accuracy and easy to fall into the local optimal solution.Aiming at the above defects of SSA,a sparrow search optimization algorithm with firefly search disturbance(FSSA)is proposed by fusing firefly algorithm(FA)iteration strategy.Firstly,after the sparrow search,the firefly disturbance strategy is used to update the position of all individuals in the population,which makes the algorithm search more fully in the solution space and effectively avoid falling into the local optimum,so as to improve the convergence speed and accuracy of the algorithm.Secondly,six benchmark functions are used to compare the improved algorithm with particle swarm optimization(PSO),whale optimization algorithm(WOA)and the original SSA algorithm.The simulation results show that the proposed algorithm can overcome the shortcoming that SSA is easy to fall into local optimization,and improve the optimization accuracy,convergence speed and robustness.Finally,FSSA is applied to solve the traveling salesman problem(TSP)with 14 cities.The simulation results show that the improved algorithm has better results than the original SSA algorithm,which further verifies the optimization ability of FSSA.