Improved Analysis And Application Research Of Cognitive Behavior Optimization Algorithm

Cognitive behavior optimization algorithm(COA)is a new swarm intelligence optimization algorithm that simulates the behavior of Artificial bee colony algorithm(ABC)in the division of labor and exchange of information.The algorithm has the characteristics of simple structure,strong stability,easy to understand and so on.Since its introduction,scholars in the field have been widely concerned.However,with the deepening of research,some scholars find that this algorithm has some shortcomings such as poor precision and slow convergence rate,which limits the application range of cognitive behavior optimization algorithm to a certain extent.This dissertation mainly aims at improving some existing problems in the cognitive behavior optimization algorithm,and applying the improved optimization algorithm to the actual optimization problem.The purpose of this article is to further improve the theory of cognitive behavior optimization algorithms and expand their application scope.The content of this paper is divided into the following three aspects:(1)Propose a cultural cognitive algorithm that combines traditional cultural algorithms with cognitive behavior optimization algorithms.The double-layer structure of cultural algorithm is refined and expanded into a three-layer structure,to a large extent overcome the shortcomings,which like the poor search and slow convergence,and it also enhance the individual's global search ability.Thereby,the performance of the the cultural cognitive algorithm is improved,and the algorithm is applied to the function optimization problem.(2)Elite opposition-based cognitive behavior optimization algorithm is proposed to overcome the shortcoming that the cognitive behavior optimization algorithm is apt to fall into the local optimum.The elite opposition learning strategy is introduced into the cognitive behavior optimization algorithm to expand its search space and enhance the diversity of the population.Meanwhile,the cognitive behavior optimization algorithm based on the elite opposition learning strategy is applied to the engineering case.(3)The basic cognitive behavior optimization algorithm is applied to solve the large-scale 0-1 knapsack problem.The number of backpack heavy objects is up to 150,000.The advantage of this algorithm is to solve the 0-1 knapsack problem with high dimension.Experimental results show that the optimization algorithm of cognitive behavior optimization has some advantages in solving the 0-1 knapsack problem of high dimension.