Research And Application Of Artificial Bee Colony Algorithm

Artificial bee colony algorithm(ABC)is a swarm intelligence optimization algorithm to simulate honey bee foraging behavior.It provides a new method for solving the global optimization problem in the field of science.However,the basic artificial bee colony algorithm such as premature convergence,easy to fall into local optimal shortcomings and convergence slow,in order to enhance the optimization performance of artificial bee colony algorithm effectively,this paper from the two aspects of its improvement,and a simple application.Specific work is as follows:First of all,in the early stage,the number of selective strategy is increased to change the single search strategy of onlooker and reduce the random search to make the onlookers search new nectar intelligently under the guidance of the improved strategy,thus the useless search will be reduced and the convergence will be accelerated.Secondly,in the late stage,unidimensional exploratory search strategy instead of random search strategy to execute sectional exploratory search by dimension and increase the probability to jump out of local optimum by this way,thus the phenomenon of premature convergence is delayed and the calculation accuracy is improved.Then,the improved bee colony algorithm and artificial bee colony algorithm(ABC),modified artificial bee colony algorithm based on optimal solution guide(GABC)and based on dynamic evaluation of bee colony algorithm(DSABC)under the same conditions were used for functions optimization,then the results are compared and analyzed.The results show that the improved algorithm in this paper has faster convergence speed and higher accuracy.Finally,the improved bee colony algorithm,K-Means clustering algorithm and basic artificial bee algorithm are used for cluster analysis of UCI data sets,and the clustering results were compared and analyzed.The results show that the improved artificial bee colony algorithm can improve the clustered accuracy significantly.