Multi-strategy Improved Sparrow Search Algorithm

Abstract The multi-strategy improved sparrow search algorithm (MSISSA) is proposed to address the problems that (SSA) not rich in population diversity, and prone fall into local optimality poor accuracy solving multi-dimensional functions. Firstly, Cat mapping used initialize SSA population. Secondly, an elite reverse learning strategy introduced increase diversity improve global ability of SSA. Then, number discoverers aware-at-risk sparrows are dynamically adjusted by improving scaling factor. Finally, individuals subjected Cauchy variation or Tent chaos perturbation according their fitness values effectively solve problem falling optimality. Simulation results show MSISSA has higher performance finding optimum compared with classical optimization algorithms such as