Application Research On Multi-strategy Chimp Optimization Algorithm

Chimp Optimization Algorithm(ChOA)is a swarm intelligence optimization algorithm proposed by simulating the cooperative predation behavior of chimp groups in nature.The algorithm has a clear structure,few adjustment parameters,and has achieved great success in many optimization fields.With the deepening of the research,the researchers found that the algorithm is easy to fall into the local optimum,and the imbalance between the global exploration ability and the local development ability greatly affects the optimization accuracy of the algorithm.Aiming at the shortcomings of the chimp optimization algorithm,this paper analyzes and improves the algorithm from the aspect of multi-strategy mixing,and applies the improved algorithm to image processing problems to further improve the performance of the ChOA and broaden its application range.The main research work of this paper is as follows:(1)In the initial stage of ChOA,an opposition-based learning mechanism was introduced to improve population diversity.Sine-Cosine Algorithm(SCA)was introduced in the development process to improve the convergence speed and accuracy of the algorithm,so as to balance the exploration and exploitation ability of the algorithm.The improved algorithm was compared with different types of heuristic algorithms in 20 benchmark functions and CEC2019 test set,and was used to solve the minimum spanning tree.The experimental results show that the improved ChOA has obvious advantages in searching ability,which verifies the effectiveness and feasibility of the improved algorithm.(2)In order to improve the natural mechanism of the ChOA,make the search agent more visualized and the algorithm more intelligent,a somersault strategy and dynamic adaptive weights are introduced,and an improved ChOA is proposed.The improved ChOA is applied to UAV 3D path planning.The experimental results show that the improved ChOA has a high solution accuracy in the UAV 3D path planning problem.(3)Aiming at the shortcomings of traditional incomplete beta function in image enhancement applications,an image enhancement method based on the fusion of bilateral gamma correction function and incomplete beta function based on global brightness is proposed.In this method,the ChOA is used for adaptive selection of normalized incomplete beta function parameters.In order to prove the effectiveness and feasibility of the proposed method,the proposed method is compared with different image enhancement techniques.Experimental results show that this method has good enhancement effects in avoiding over-enhancement and mixing complex images with multiple properties,and retains more image detail information,and the enhancement results are significantly better than other image enhancement methods.