Multi-strategy Improved Sparrow Search Algorithm

Abstract

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