Application of hybrid PSO-GA algorithm in optimization of high-dimensional complex functions

Abstract

To improve the optimization of high-dimensional complex functions,In this paper,we combine both GA and PSO to propose an improved hybrid PSO-GA algorithm.First,the learning factors and inertial weights of the first half PSO are modified in the improved algorithm to optimize the local and global search.An adaptive GA is then introduced in the second half of the algorithm to balance population diversity and avoid falling into local optimal.Finally,this paper uses four typical test functions,performing a testing and comparative analysis of the algorithm.Experimental results show that the improved hybrid algorithm can not only effectively avoid the local optimum,but also improve the optimization ability of the function.