Pareto genetic algorithm for multi-objective optimization design
Xiaopeng Wang · Systems engineering and electronics · 2003
Stochastic and implicitly parallel properties of genetic algorithm make it possible to search for multiple local optimal solutions and obtain optimal solution aggregate. In order to elaborate the population search advantages of genetic algorithm and improve the efficiency and flexibility of multi-objective optimization design Pareto genetic algorithm, a new method suitable for multi-objective optimization design, is established based on self-adaptive genetic algorithm which population ranking technique, niche technique and pareto solution set filter are introduced. Pareto optimal solution aggregate may be provided in the form of Pareto front from which designers may select some suitable optimization design results according to their inclination. Finally the Pareto genetic algorithm is applied to carry out multi-objective aerodynamic optimization design of transonic airfoils. The design results show that pareto genetic algorithm is effective enough to be used in multi-objective optimization design.