Artificial Bee Colony Algorithm Based On Adaptive Hybrid Strategy And Its Application

With the progress of the times,the optimization problems in scientific research and engineering practice are more solved by algorithms.Designing effective algorithms has always been the goal of researchers.In recent years,evolutionary algorithm has attracted extensive attention in the industry because of its excellent ability to solve complex optimization problems.At present,evolutionary algorithm has been widely used in many fields.Artificial bee colony algorithm(ABC)is one of the swarm intelligence optimization algorithms.It mainly simulates the honey collection behavior of bee colony.The algo-rithm has simple structure,easy implementation and few parameters,so it has attracted the attention and research of many scholars.However,artificial bee colony algorithm also has some common problems of evolutionary algorithm.For example,the standard ABC algorithm also has some problems,such as slow convergence speed,low accuracy and too complex problem solving failure.To solve these problems,in order to improve the convergence accuracy,convergence speed and robustness of the algorithm,this paper im-proves the artificial bee colony algorithm in different ways,and gives practical application cases.The main work is as follows:An improved artificial bee colony algorithm with opposition-based learning(OABC for short)is proposed.In the following bee phase of the artificial bee colony algorithm,the population implement opposition-based learning according to probability instead of following bee search,retains the employed bee phase and scout bee phase in the stan-dard artificial bee colony algorithm to ensure the exploration ability and diversity of the population,and adds parameters to control the contraposition search range in the general opposition-based learning process,it makes full use of population information and indi-vidual information to optimize the population,improves the effectiveness of counterpoint,and improves the success rate of opposition-based learning.Simulation results show that the OABC algorithm effectively improves the optimization speed and convergence accu-racy of the algorithm.An artificial bee colony algorithm based on adaptive hybrid strategy(AHABC for short)is proposed.On the basis of OABC algorithm,the change rate of focus distance,namely population dispersion,is added.The population dispersion is used to reflect the search process of the algorithm,and then the selection of search equation is controlled,which not only ensures the utilization of population information,but also improves the utilization of optimal individual information.In addition,in order to alleviate the selection pressure of roulette selection mode,Sigma scaling method is also used to scale the fitness value.The simulation results of several benchmark functions show that AHABC has better optimization performance and faster convergence speed.Taking an Electric Vehicle Routing Problem(EVRP for short)as an example,this paper first introduces the case background and the necessity of optimizing EVRP,then describes and analyzes the problem,establishes the mathematical model of EVRP,and uses the AHABC to solve it,and obtains the optimal distribution strategy and the lowest cost under the corresponding conditions,which provides a reference for the application of the algorithm.