A novel sparrow search algorithm with integrates spawning strategy

Sparrow Search Algorithm (SSA) has recently received more attention in the intelligent optimization group. However, the high randomness and local optimal problems of the algorithm limit the application of the algorithm. In this paper, a novel sparrow search algorithm (NSSA) combined with spawning technology is proposed, which effectively improves the problem of the algorithm falling into local optimization. First of all, it is proposed to replace the traditional stochastic method with the good point set theory to find the initial individual, so that the initial population is more evenly distributed in the search space and improve the quality of the initial solution. Secondly, the spawning strategy of the cuckoo algorithm is integrated into the discoverer stage, which improves the search method of the discoverer and enhances the global search ability, which makes the algorithm avoids the sudden decline of population diversity and premature convergence. Finally, Levy flight and Brownian motion are used to disturb the position of the sparrow dimension by dimension to improve the ability of the algorithm to jump out of the local optimal value in subsequent iterations. By comparing and analyzing NSSA and SSA, DE, ALO, GOA, DOA, HSSA and XSSA algorithms in 12 common evaluation functions and CEC-2017 test functions, and apply NSSA to the TSP problem. The simulation results show the effectiveness and superiority of the proposed scheme.