Multi-objective Optimization Based on Improved Differential Evolution Algorithm

On the basis of the fundamental differential evolution (DE), this paper puts forward several improved DE algorithms to find a balance between global and local search and get optimal solutions through rapid convergence. Meanwhile, a random mutation mechanism is adopted to process individuals that show stagnation behaviour. After that, a series of frequently-used benchmark test functions are used to test the performance of the fundamental and improved DE algorithms. After a comparative analysis of several algorithms, the paper realizes its desired effects by applying them to the calculation of single and multiple objective functions

Image Feature Extraction Based on Differential Evolution Neural Network

Abstract Image feature extraction result determines the analysis and understanding of the final image, and plays an important role in image engineering. Based on image feature extraction as the research object, this paper processes the image by using the good generalization ability, robustness and numerical approximation ability of the neural network to form the input data of BP neural network, then adopts the differential evolution (DE) algorithm to improve the BP neural network, which make it realize the faster convergence during the sample training, thus, the image feature extraction effect is increased and the time complexity is reduced, and in this way, the feature extraction result is guaranteed to not be distorted to the maximum. That the algorithm in this paper has better performance in the image feature extraction is testified by experimental simulation analysis and comparison