Sine Cosine Algorithm And Application Research Based On Ensemble Strategy

Sine Cosine Algorithm(SCA)is a new optimization algorithm based on population.Because of its simple structure and easy implementation,it has been well applied in some optimization fields.However,there are still some shortcomings,such as slow convergence speed,easy to fall into the local optimal solution,the application field needs to be further expanded etc.How to improve the overall performance of the algorithm and expand its application field is a key step in the performance of Sine Cosine Algorithm.Because different evolutionary operation methods have different roles in improving algorithm performance,the design of optimization methods based on ensemble ideas has become a hotspot in recent years.This paper starts from two aspects which is operation method and integrating multiple strategies,the Sine Cosine Algorithm is used as the basic optimization algorithm carrier to study the optimization algorithm based on ensemble strategy to improve the overall performance of the sine and cosine algorithm.In multi-threshold segmentation of images,a new method is provided for image segmentation.The main research contents of this paper are as follows:(1)A Sine Cosine Algorithm(ICMSCA)based on cross mutation mechanism is proposed for the lack of adaptability of the basic SCA algorithm and the single problem solving.In the method,the individual fitness values in SCA are sorted firstly,and adopts different cross operations according to the individual's own conditions.Adaptive operations in the cross strategy are introduced to improve the adaptability of the algorithm.Finally,adaptive mutation operation are used according to different dimensions,the performance of the algorithm is improved.Simulation experiments are carried out on the typical benchmark function set,and the performance of the improved SCA algorithm is verified.(2)Two improved methods of SCA algorithm based on ensemble optimization are proposed to solve the shortcomings of the basic SCA algorithm such as slow convergence speed and poor diversity: Sine Cosine Algorithm based on ensemblestrategy(ESCA)and Sine Cosine Algorithm based on sub-module ensemble(PMSCA).In ESCA algorithm,six improved mutation strategies are integrated into SCA algorithm with a special probability selection mechanism,which increases the diversity of the algorithm and improves the convergence rate.The PMSCA algorithm retains two mutation strategies of ESCA,combines two other cross strategies,and combines them in pairs.The sub-module is integrated into the Sine Cosine Algorithm,which reduces the possibility of the algorithm falling into local extreme value,which is conducive to the convergence of the algorithm to the whole.By comparing with other algorithms,the performance advantages of the two improved algorithms are proved.(3)In order to expand the application field of SCA algorithm,the proposed Sine Cosines Algorithm(ESCA)based on ensemble strategy is applied to image multi-threshold segmentation.Combining with the maximum entropy segmentation method(KSW)in multi-threshold segmentation,the quality of image segmentation is improved.Compared with other related algorithms,the improved algorithm is proved to be effective,and an effective method for image multi-threshold segmentation is provided.In summary,this thesis has carried on the thorough research and the analysis to ensemble optimization method and SCA algorithm.Several improved SCA algorithms are proposed and the application fields are broadened.