The selection pressure of genetic algorithm reveals the degree of balance between the global exploration and local optimization.A novel algorithm called the hybrid multi-population cellular genetic algorithm(HCGA)is p...The selection pressure of genetic algorithm reveals the degree of balance between the global exploration and local optimization.A novel algorithm called the hybrid multi-population cellular genetic algorithm(HCGA)is proposed,which combines population segmentation with particle swarm optimization(PSO).The control parameters are the number of individuals in the population and the number of subpopulations.By varying these control parameters,changes in selection pressure can be investigated.Population division is found to reduce the selection pressure.In particular,low selection pressure emerges in small and highly divided populations.Besides,slight or mild selection pressure reduces the convergence speed,and thus a new mutation operator accelerates the system.HPCGA is tested in the optimization of four typical functions and the results are compared with those of the conventional cellular genetic algorithm.HPCGA is found to significantly improve global convergence rate,convergence speed and stability.Population diversity is also investigated by HPCGA.Appropriate numbers of subpopulations not only achieve a better tradeoff between global exploration and local exploitation,but also greatly improve the optimization performance of HPCGA.It is concluded that HPCGA can elucidate the scientific basis for selecting the efficient numbers of subpopulations.展开更多
基金Supported by National Natural Science Foundation of China(61262019)the Aeronautical Science Foundation of China(2012ZA56001)+2 种基金the Natural Science Foundation of Jiangxi Province(20114BAB201046)the Science and Technology Research Project of Jiangxi Provincial Department of Education(GJJ12435)the Open-End Foundation of the Key Laboratory of Nondestructive Testing(Ministry of Education)
文摘The selection pressure of genetic algorithm reveals the degree of balance between the global exploration and local optimization.A novel algorithm called the hybrid multi-population cellular genetic algorithm(HCGA)is proposed,which combines population segmentation with particle swarm optimization(PSO).The control parameters are the number of individuals in the population and the number of subpopulations.By varying these control parameters,changes in selection pressure can be investigated.Population division is found to reduce the selection pressure.In particular,low selection pressure emerges in small and highly divided populations.Besides,slight or mild selection pressure reduces the convergence speed,and thus a new mutation operator accelerates the system.HPCGA is tested in the optimization of four typical functions and the results are compared with those of the conventional cellular genetic algorithm.HPCGA is found to significantly improve global convergence rate,convergence speed and stability.Population diversity is also investigated by HPCGA.Appropriate numbers of subpopulations not only achieve a better tradeoff between global exploration and local exploitation,but also greatly improve the optimization performance of HPCGA.It is concluded that HPCGA can elucidate the scientific basis for selecting the efficient numbers of subpopulations.
