A multi-subgroup hierarchical hybrid of genetic algorithm and particle swarm optimization
jin yong mi · Control theory & applications · 2013
To make use of the strong global search ability of the genetic algorithm and the high convergence rate of the particle swarm optimization,we combine these two algorithms and propose a multi-subgroup hierarchical hybrid of genetic algorithm and particle swarm optimization(HGA–PSO).This hybrid algorithm adopts a hierarchical structure;the base level is composed of a series of subgroups of Genetic algorithms,which provides the global search ability of the entire algorithm.The top level comprises all elite subgroups consisting of the best individual of each subgroup,which performs the accurate local search by using the particle swarm algorithm with cramped initial velocity.The global convergence analysis of HGA–PSO is given in this paper,and the performances of HGA–PSO have been evaluated through seven Benchmark functions.The experimental results show that the proposed method is superior to other related methods.