Improved sparrow search algorithm by hybrid equalization optimizer

Abstract

Aiming at the problems of low population diversity, easy to convergence at a local optimum and slow convergence speed of Sparrow search algorithm (SSA), Improved sparrow search algorithm with hybrid equilibrium optimizer (ISSA) is proposed. Firstly, the information interaction among sparrow and the population diversity is strengthened by fusing equilibrium optimizer (EO) into sparrow algorithm search mechanism. Then, the generation mechanisms of the new population obtained to guide the orientation of the population and to improve the convergence speed of the algorithm. Finally, to verify the performance of our ISSA, four unconstrained classical benchmark functions are adopted, and three algorithms are selected for analysis together. Experimental results are considering both convergence and accuracy demonstrate the effectiveness of ISSA for global numerical sums.