Ant lion optimizer with adaptive boundary and optimal guidance

Abstract

Aiming at the shortcoming that the ant-lion algorithm has unbalanced exploration and development capability, an improved algorithm with adaptive boundary and optimal guidance is proposed. First, the ant lion adjust the scope of the border in order to balance the exploration and development capabilities. Second, through the adaptive best-guided equation, to improve the convergence speed and global search ability. The simulation results of six standard test functions show that the improved algorithm improves the accuracy and convergence speed of the optimal solution compared with other algorithms.