A Hybrid Optimization Algorithm Based on Artificial Glowworm Swarm and Differential Evolution

When searching for the globally optimal solution of function,there exist some shortcomings in artificial glowworm swam optimization(GSO),such as the slow convergence speed,easily falling into the local optimum value,the low success rate of convergence and computational accuracy.This paper embeds predatory behavior of artificial fish swarm algorithm (AFSA) into GSO and proposes a hybrid optimization algorithm which combines the GSO with differential evolution (DE).Finally,the algorithm is put through four typical test functions and an application example.The results show that the hybrid algorithm has better convergence efficiency and higher computational precision,and its overall approximation performance is superior to basic artificial GSO and DE.