Genetic algorithms development for multiobjective design optimization of compressor cascade

Aerodynamic optimization design of compressor blade shape is a design challenge at present because it is inherently a multiobjective problem. Thus, multiobjective Genetic Algorithms based on the multi-branch simulated annealing selection and collection of Pareto solutions strategy have been developed and applied to the optimum design of compressor cascade. The present multiobjective design seeks high pressure rise, high flow turning angle and low total pressure loss at a low inlet Mach number. Pareto solutions obtain the better aerodynamic performance of the cascade than the existing Control Diffusion Airfoil. From the Pareto solutions, the decision maker would be able to find a design that satisfies his design goal best. The results indicate that the feasibility of multiobjective Genetic Algorithms as a multiple objectives optimization tool in the engineering field.