Double strategy fusion improved sparrow search algorithm

Abstract

Aiming at the shortcomings of sparrow search algorithm, such as dependence on initial solution, sudden drop of population diversity in the late iteration period, and easy to fall into local optimal value, a multi-strategy algorithm was proposed to improve sparrow search algorithm. In the initialization stage of the algorithm, the standard Brownian motion is introduced to enhance the global search ability of the algorithm and improve the population diversity. In the later stage of the algorithm iteration, the improved artificial bee colony search strategy is introduced to accelerate the convergence of the algorithm, and the influence of local extreme points on SSA can be reduced to improve the ability of the algorithm to jump out of the local optimal solution. The experimental results show that the improved sparrow search algorithm has faster global convergence speed, better optimization accuracy and stability in function optimization.

A Multi-Strategy Crazy Sparrow Search Algorithm for the Global Optimization Problem

“…Fu et al introduced an innovative method called the elite chaotic backward learning mechanism in the population initial-ization stage. By combining it with a randomized following strategy in the chicken flock algorithm, they enhanced the quality and diversity of individuals, resulting in improved search performance for updating the joiner location [43]. Duan et al employed Sobol sequence mapping to construct the population's beginning location and used horizontal as well as vertical crossing to prevent local optimum and sustain sparrow variety [44].…”