Multi-objective evolutionary algorithm using population diversity

Crossover and mutation plays an important role in evolutionary algorithms, probability selection determines the performance of the algorithm. Now the crossover and mutation probability is mainly calculated according to the fitness of individuals, while some shortcomings still exist, such as evolution easier to stall and so on. In the paper, we propose an adaptive adjustment strategy which genetic parameters change based on diversity of population, ensuring maintaining sufficient diversity and enhancing search capability of the algorithm during the evolution. Compared with AGA through the classic test functions and the robot path planning. Simulation results suggest the improved strategy has greatly improvement on the ability of fast convergence and stability of the algorithm, and more easy to jump out the local convergence.