Improved multi-objective particle swarm optimization algorithm for aerofoil aerodynamic optimization design

Based on Multi-Objective Particle Swarm Optimization (MOPSO), simulated annealing algorithm is introduced into MOPSO to evaluate the best individual in the local. Share function (SF) based on object vector is used to judge the fitness. Function optimization results indicate that the algorithm possess a better convergence stability and convergence speed. Research in this paper indicates that the convergence speed of the algorithm (SF-MOPSO) enhanced fifty percents. Then these algorithms are applied on an airfoil shape aerodynamic optimization. Comparison of optimization results by these different algorithms reveals that the improved differential evolutionary algorithm has a better search capability.