Evolutionary Topology Optimization Using Quadtree Genetic Programming
Naruhiko Nimura, Akira Oyama · 2022 IEEE Congress on Evolutionary Computation (CEC) · 2022
A new topology optimization method using genetic programming is proposed. To simultaneously achieve the gen-eration of shapes with high degrees of freedom and efficient optimization, the quadtree used in image processing is employed to reduce the number of design variables. Because the quadtree used in image processing implicitly holds coordinate information, we propose a new crossover and mutation method that inherits this information. For validation of the proposed approach, shape optimization and topology optimization are demonstrated where target airfoils including multi-element airfoils are reproduced. As a result, it is confirmed that the proposed method works for shape and topology optimizations with high efficiency.