Optimization of nonlinear functions based on improved genetic algorithm

Premature convergence and falling into local optimum easily are important problems for genetic algorithm. Therefore a new algorithm is constructed in this paper, which can be performed among multiple populations by introducing different optimization parameters. The final formation is aggregated into an elite population, where genetic operations such as selection, crossover, mutation, etc. do not exist, for the purpose of ensuring that the optimal individuals selected between different populations are not destroyed and lost. The simulation results show that the algorithm used in this paper has better optimization ability and higher calculation accuracy for solving local optimal problems of nonlinear functions.