Aerodynamic optimization using a hybrid MOGA-local search method

A novel adaptive local search method for hybrid multi-objective evolutionary algorithms (MOEAs) wasa pplied to multi-objective aerodynamic optimization problems for convergence improvement. A novel directional operator without explicit gradient information is also adopted, comprising the selection of search direction and a local one-dimensional search. Probability of the directional operator is adaptively changed based on the relative effectiveness of the directional local search operator and evolutionary operators such as crossover and mutation. The adaptive directional operator is combined with a baseline MOEA. Comparisons are made for the baseline and the hybrid MOEA on multi-objective airfoil design optimization problems. Results show that the present adaptive local search strategy enables remarkable enhancement of convergence when a local search is effective, while minimizing unnecessary computation for cases where a local search is not well suited for.