A Novel Modified Sparrow Search Algorithm Based on Adaptive Weight and Improved Boundary Constraints

Abstract

A novel modified sparrow search algorithm based on adaptive weight and improved boundary constraints is proposed to tackle disadvantages of sparrow search algorithm, which tends to fall into local optimum and has limited convergence speed. The convergence speed of algorithm is improved by adaptive weight, and the improved boundary handling strategy improves the convergence accuracy of algorithm to a certain extent. In order to verify the effectiveness of improved algorithm, a total of nine benchmark test functions of three types were calculated, and the ant lion optimizer, seagull optimization algorithm, tunicate swarm algorithm and standard sparrow search algorithm were compared and analyzed statistically. The simulation results indicate that the improved algorithm can overcome precocious convergence problem effectively, and is superior to the other four algorithms in terms of convergence speed and precision.