Research Of Artificial Bee Colony Algorithm And Applications

Artificial bee colony algorithm is a new bionic swarm algorithm which has been applied to solve the practical problems by simulating the intelligent foraging behavior of honeybee. The algorithm not only makes the local searching efficient but also has the ability of global optimization by the conversion of three kinds of bees in different situations and the help of heuristic search strategy. Because of its low complexity, strong robustness, less set parameters, fast searching speed, has been widely applied in signal optimization problems.However, there are still some weakness in the artificial bee colony algorithm, such as premature convergence, easily trapped in local optimum, poor accuracy, etc. In addition, the theory and application of ABC algorithm in multi-objective optimization problems need to be further studied.This paper mainly explore the application and the improvement mechanisms of the ABC algorithm. Aiming at deficiency of algorithm, the corresponding improved mechanism is proposed. Then, the algorithms have been used in the single and multi-objective optimization of DC motor system and robot path planning. The main work of this dissertation is as follows:Firstly, the paper describes the basic concept, methods and steps, optimal performance and advantages, implementation process of the algorithm. Then aiming at the easily trapped in local optimum problem, we introduce the information of global guidance into the improved algorithm, so that we can strengthened the search ability near the global optimal solution for fast convergence. In the simulation experiment, we can see that the performance of the improved algorithm is much better than the standard ABC algorithm.Secondly, aiming at the easily falling into local optimum problem, we introduce two perturbation vector into the search equation of employed bees. This can increase the diversity of the population and improve the global search ability. In experiment results show that the exploring ability of the improved algorithm is stronger than the standard ABC algorithm in solving the path planning problem of robot obstacle avoidance, which is the presentation of jumping out of the local optimum.Finally, in the view of search bias and diversity loss problems in the optimization process, a modified multi-objective ABC algorithm based on maximin fitness function is proposed. Normalization method and ?- dominance conception are introduced to the computation of maximin fitness function value. Besides, the conception of ?- dominance is important to the computation of maximin fitness function value, a alterable ?- dominance strategy is proposed, which solves the search bias and diversity loss problem in the searching process and improve the calculation accuracy of the algorithm. The final experiment tests on the optimization problem of speed control in DC motor system. The experiment results show that the proposed algorithm is reliable, and can obtain more satisfactory solutions in one time. It also provides an effective optimization method for improving control performance in motor system.