Lion Swarm Optimization Based on Balanced Local and Global Search with Different Distributions
Keqin Jiang, Mingyan Jiang · 2021
In view of the shortcomings of the basic lion swarm optimization, which is prone to local optimization and low convergence accuracy in partial optimization, this paper proposes a lion swarm optimization based on balanced local and global search with different distributions. The improved algorithm adds chaos search and different distributed perturbation strategies to the positions of lions in the earlier stage, which improves the optimization efficiency of the algorithm in the optimization process. These disturbance strategies include variations based on Cauchy mutation, t probability distribution, and levy flight. The simulation results of the test functions show that the optimization accuracy of the improved algorithm is much higher than that of the basic lion swarm optimization. The improved algorithm effectively prevents the swarm optimization from easily falling into the local optimization value in the extremely difficult optimization functions.