A Hybrid Research Of Artificial Bee Colony Algorithm

With the development of society,more optimization problems needed to be solved in science and engineering,and the problems become more and more complex.But traditional optimization algorithms have many limitations on the mathematical properties of optimization problems,and at the same time,find the solutions with low accuracy and use more computing resource.Since the 1980s,inspired by the intelligent behavior of biological groups in nature,researchers have proposed a series of intelligent optimization algorithms by simulating swarm intelligence behavior.The artificial bee colony(ABC)algorithm is one of the intelligent optimization algorithms,which was proposed by Turkish scholars in 2005.Due to its fewer parameters and better search performance,ABC has shown unique advantages in intelligent optimization algorithm,which has attracted the attention and research of many scholars in the world.In this paper,according to the characteristics and research situation of ABC,two improved ABCs are proposed,and compared with recently improved ABCs in standard test function set,and the effectiveness of the proposed algorithm is verified.The main research content of this paper is as follows:1.The basic ABC and its search strategy focus on exploration.In order to enhance exploitation of the algorithm,a hybrid artificial bee colony algorithm with adaptive search strategy is proposed in this article.Firstly,the objective function value information and optimal solution guiding information are introduced into the search strategy,so that we obtain a new search strategy with strong exploitation and self-adaptive mechanism;at the same time,to avoid premature convergence,we use three random food sources and Gaussian distribution to form a new search strategy with good exploration;And then,we balance exploration and exploitation by hybridizing the two search strategy in employed bee phase and enhance exploitation by using the search strategy with strong exploitation in onlooker bee phase.Compared with basic and some representative improved ABCs in 20 standard test functions,the experimental results show that the proposed algorithm has better search ability and faster convergence speed.2.In order to improve the exploitation of ABC under the condition of maintaining good exploration,a novel artificial bee colony algorithm with hybrid search strategy based on dual adaptive mechanism is proposed.Firstly,in order to maintain the good exploration of the algorithm and enhance the ability to jump out of the local optimal,inspired by the idea of niche,the first adaptive mechanism is proposed to divide the population and cooperated with two search strategies with good exploration.;And then,with the aim of enhancing the exploitation of the algorithm,the second adaptive mechanism is proposed to select the appropriate search strategy with strong exploitation in candidate pool.The two adaptive mechanisms are applied in employed bee phase and onlooker bee phase respectively.By comparing with the recently improved ABCs in 22 standard test functions and CEC2014 test functions,we verified that the algorithm proposed has good exploration and stronger exploitation.