An Efficient Hybrid Evolutionary Optimization Method Coupling Cultural Algorithm with Genetic Algorithms and Its Application to Aerodynamic Shape Design

Evolutionary algorithms have been widely used to solve complex engineering optimization problems with large search spaces and nonlinearity. Both cultural algorithm (CA) genetic (GAs) a broad prospect in the field. The traditional CA has poor precision solving easily falls into local optima. An efficient hybrid evolutionary method coupling GAs (HCGA) is proposed this paper. HCGA reconstructs framework, which uses three kinds of knowledge build belief space, are as an model for population space. In addition, knowledge-guided t-mutation operator developed dynamically adjust mutation step introduced influence function. achieves balance between exploitation exploration through above strategies, thus effectively avoids falling optima improves efficiency. Numerical experiments comparisons several benchmark functions show that significantly outperforms other compared terms comprehensive performance, especially high-dimensional problems. further applied aerodynamic design, wing cruise factor being improved by 23.21%, demonstrating potential applications design.