In order to solve the problems， such as insufficient search ability and low search efficiency of Heap-Based optimizer （HBO） in solving complex problems， a Differential disturbed HBO （DDHBO） was proposed. Firstly， a random differential disturbance strategy was proposed to update the best individual’s position to solve the problem of low search efficiency caused by not updating of this individual by HBO. Secondly， a best worst differential disturbance strategy was used to update the worst individual’s position and strengthen its search ability. Thirdly， the ordinary individual’s position was updated by a multi-level differential disturbance strategy to strengthen information communication among individuals between multiple levels and improve the search ability. Finally， a dimension-based differential disturbance strategy was proposed for other individuals to improve the probability of obtaining effective solutions in initial stage of original updating model. Experimental results on a large number of complex functions from CEC2017 show that compared with HBO， DDHBO has better optimization performance on 96.67% functions and less average running time （3.445 0 s）， and compared with other state-of-the-art algorithms， such as Worst opposition learning and Random-scaled differential mutation Biogeography-Based Optimization （WRBBO）， Differential Evolution and Biogeography-Based Optimization （DEBBO）， Hybrid Particle Swarm Optimization and Grey Wolf Optimizer （HGWOP）， etc.， DDHBO also has significant advantages.

In current international society， as the international language， English characters appear in many public occasions， as well as the Chinese pinyin characters in Chinese environment. When these characters appear in the image， especially in the image with complex style， it is difficult to edit and modify them directly. In order to solve the problems， an image character editing method based on improved character generation network named Font Adaptive Neural network （FANnet） was proposed. Firstly， the salience detection algorithm based on Histogram Contrast （HC） was used to improve the Character Adaptive Detection （CAD） model to accurately extract the image characters selected by the user. Secondly， the binary image of the target character that was almost consistent with the font of the source character was generated by using FANnet. Then， the color of source characters were transferred to target characters effectively by the proposed Colors Distribute-based Local （CDL） transfer model based on color complexity discrimination. Finally， the target editable characters that were highly consistent with the font structure and color change of the source character were generated， so as to achieve the purpose of character editing. Experimental results show that， on MSRA-TD500， COCO-Text and ICDAR datasets， the average values of Structural SIMilarity（SSIM）， Peak Signal-to-Noise Ratio （PSNR） and Normalized Root Mean Square Error （NRMSE） of the proposed method are 0.776 5， 18.321 1 dB and 0.435 8 respectively， which are increased by 18.59%，14.02% and decreased by 2.97% comparing with those of Scene Text Editor using Font Adaptive Neural Network（STEFANN） algorithm respectively， and increased by 30.24%，23.92% and decreased by 4.68% comparing with those of multi-modal few-shot font style transfer model named Multi-Content GAN（MC-GAN） algorithm（with 1 input character）respectively. For the image characters with complex font structure and color gradient distribution in real scene， the editing effect of the proposed method is also good. The proposed method can be applied to image reuse， image character computer automatic error correction and image text information restorage.