Differential evolution algorithm is a heuristic global optimization technique based on population which is easy to understand and implement. In this paper we proposed a classification model base on differential evolution algorithm. The proposed model provides a good practicability and promising future for pattern identification.

Differential Evolution (DE) is a simple and efficient optimizer, especially for continuous global optimization. Over the last few decades, DE has often been employed for solving various engineering problems. At the same time, the DE structure has some limitations in the complicated problems. This fact has inspired many researchers to improve on DE by proposing modifications to the original algo...

Aerodynamic design algorithms require an optimization strategy to search for the best design. The object of this paper is to compare the performance of some different strategies when used by an aerodynamic shape optimization routine which designs fan blade shapes. A recently developed genetic algorithm, Differential Evolution [1,2], outperforms more traditional techniques.

Differential evolution algorithm has been widely used, because of its efficient optimization and no complex operation and coding mechanism. But DE falls into the local optimum easily. So this study puts forward a memetic algorithm. The algorithm can increase the diversity of population and jump out the local extreme value point effectively. The convergence speed of the algorithm is improved, be...