Improved Analysis And Application Research Of Social-spider Optimization Algorithm

The social-spider optimization algorithm is to simulate the division of labor,information communication and breeding behavior of social-spider in the nature,which the emergence of a new kind of swarm intelligent optimization algorithm,this algorithm has the advantages of simple structure,strong stability and easy to understand.It has been widely concerned by scholars in the field since it was put forward.But with the in-depth study,some scholars found that the disadvantage of this algorithm of optimization accuracy is poor and slower convergence speed,which limits the social-spider optimization algorithm of theory development and application in some extent.In this paper,the mainly focus on the improvement of the existing problems in the social-spider optimization algorithm,and the improved optimization algorithm is applied to the practical optimization problems,the purpose of this paper is to further improve the theory of social-spider optimization algorithm and expand its application scope.The main content of this paper is divided into the following five aspects:1.A method of solving the 0-1 knapsack problem by using the social-spider optimization algorithm is proposed,which has the advantage of solving the high-dimensional 0-1 knapsack problem.The experimental results show that the algorithm has certain advantages in solving the high-dimensional 0-1 knapsack problem.2.A method of solving the coverage problem of wireless sensor based on the social-spider optimization algorithm is proposed,which can quickly find out the best scheme of wireless sensor and display it in a visual way.The experimental results show that the best optimal solution is obtained by using the social-spider optimization algorithm.3.A kind of social-spider optimization algorithm based on the elite reverse learning strategy is proposed.The main is to solve that the social-spider optimization algorithm is easy to fall into the local optimum.Therefore,the elite reverse learning strategy is introduced into the social-spider optimization algorithm,in order to achieve the purpose of expanding the search space and enhancing the diversity of the population,and based on the elite reverse learning strategies of social-spider optimization algorithm for function optimization problems.4.A social-spider optimization algorithm with differential evolution operator is proposed,which overcomes the shortcomings of the social-spider optimization algorithm to find the optimization ability is poor and slow convergence speed in some cases,the differential evolution operator is introduced into the social-spider optimization algorithm,and enhance the global search capability of the individual,then improve the performance of the algorithm,and the social-spider optimization algorithm with differential evolution operator is applied to job-shop scheduling problem.5.A kind of social-spider optimization algorithm based on quantum coding is proposed,the idea of quantum coding is introduced,which further expands the diversity of individual information,and uses the quantum rotation gate as the updating strategy,improve the local and global search capability of the algorithm,and the improved quantum social-spider optimization algorithm is applied to optimize the scheduling problem of hydropower station.