Improved Sparrow Search Algorithm Based on Normal Cloud Model and Niche Recombination Strategy

To overcome the drawback of optimizing process with biased, slower convergence speed and easily falling into local optimum on high-dimensional optimization problems of Sparrow Search Algorithm (SSA), an improved Sparrow Search Algorithm (CRSSA) based on the Normal Cloud Model (NCM) and niche recombination strategy is proposed. Firstly, the position update strategy of the producers with alarm value less than safe value based on the NCM is proposed for avoiding the original update strategy to converge to the center of domain gradually; Secondly, the adaptive parameter $En$En in the NCM can better balance the exploitation and exploration capabilities of the optimization process; Then, the position update strategy of the followers with poor fitness value based on the NCM is presented for enhancing the diversity of population at the end of iteration and avoiding the algorithm to fall into local optimum; Finally, when the algorithm is stagnating, a niche-based recombination strategy is used to further avoid falling into the local optimum, and accelerating the convergence speed. Simulation results show that the CRSSA can not only avoid optimizing with biased of SSA, but keep better accuracy and convergence speed for solving high-dimensional complex optimization problems.