Research And Application Of Artificial Bee Colony Algorithm

With the rapid development of society,there will be many optimization problems in the fields of computer and industrial development.Traditional methods have not been able to solve these problems well.Therefore,people are constantly exploring and improving,and many representative optimization algorithms have been proposed.Optimization algorithms are mainly divided into traditional algorithms and swarm intelligence optimization algorithms.Among them,swarm intelligence algorithm is an intelligent optimization algorithm that evolves by simulating various behaviors of simple organisms in nature.It can solve the limitations of traditional algorithms and the insufficient calculation amount.The problem has the advantages of wide applicability and high efficiency.Artificial bee colony algorithm is just such a new type of bionic intelligent algorithm,which has the advantages of simplicity,high efficiency,strong robustness and high accuracy.However,the bee colony algorithm has problems such as slower convergence speed and being easily trapped in a local optimum.Therefore,in view of these problems,this paper,after consulting domestic and foreign materials,conducts in-depth research and exploration of the bee colony algorithm,and studies the improvement and optimization and application of the algorithm: the initial solution of the algorithm and the bee colony search strategy Improve and apply the improved algorithm to the traveling salesman problem.The research work of this article is as follows:First,because the standard bee colony algorithm randomly generates initial solutions,although it guarantees the diversity of the initial solutions,it also makes the solutions random,and the quality of the solutions cannot be guaranteed.In this paper,a contrast mechanism is introduced to each dimension of each solution.Multiple iterations are used to compare and compete to improve the quality of the initial solution.Secondly,bees in the reconnaissance stage,because the information shared between individuals in the neighborhood is reduced during the process of screening the optimal solution,this article introduces algorithm factors,and adjusts the algorithm factors during the search process to further control the information between individuals.The degree of sharing improves the search ability,so that the reconnaissance bee can jump out of the local optimal value,find the optimal value in a larger range,and avoid falling into the local optimal value.The improved algorithm,through experimental comparison of 5 standard test functions,can find that the algorithm in this paper has faster convergence speed,and effectively avoids easy to fall into a local optimum.For the application of the algorithm,this paper applies the improved artificial bee colony algorithm to the traveling salesman optimization problem.In the application process,we use integer coding to solve the problem of bee colony algorithm discretization.Adopting the nearest neighbor method as the initial solution generation strategy can allow the travelling salesman to quickly find the shortest city from its own location and use exchange Factors and inversion factors to improve neighborhood search.Finally,the experimentaldata verification proves that using the optimization algorithm in this paper,the traveling salesman problem can be better solved.