On The Artificial Bee Colony Algorithms And Applications

The traditional optimization method has been developing continuously.At the same time,evolutionary algorithm has sprung up and developed rapidly in the past 20 years.Based on Darwin's evolutionary theory,evolutionary algorithm solves the problem by simulating the evolutionary process and mechanism of organisms.Evolutionary algorithm does not require optimization functions to satisfy certain condition or property and is easy to solve.It has become a research hot spot of the intelligence algorithms.Artificial bee colony algorithm is an evolutionary algorithm with higher speed and higher accuracy.The structure of the algorithm is simple and easy to implement.It has attracted the attention and research of many scholars.Artificial bee colony algorithm has been widely used to solve various optimization problems,such as linear approximation? engineering optimization? job shop scheduling and so on.In this paper,by improving the search equation of artificial bee colony algorithm on employed bees stage and onlooker bees stage,adding and adjusting the search structure,the performance of the algorithm is improved quickly.Artificial bee colony algorithm is applied to solve 0-1 knapsack problem,portfolio model based on conditional risk value,flow shop scheduling problem and so on,and good results are obtained.The main results are as follows:1.In order to increase the speed and search ability of the ABC algorithm,The search equations are improved.Improved artificial bee colony algorithm(IABC algorithm and NBC algorithm)are proposed.A large number of data experiments are carried out to verify the superiority of the improved search equation.In the search equations of the employed bees stage and the onlooker bees stage,the current search optimal solution is introduced into the search equations accordingly.Neighborhood Search Centers on the Current Global Optimal Solution.And using random search strategy to jump out of the local optimal solution,a better solution result is obtained.It improves the algorithm's ability of random search,and balances the global search ability and the local search ability of the algorithm.The standard function set is used for testing,and a large number of numerical experiments are carried out.The results show that the improved artificial bee colony algorithms(IABC algorithm and NABC algorithm)have better advantages in terms of optimal value and solving speed.Then the improved search equation is applied to the differential evolution algorithm.The results show that the convergence speed of the improved differential evolution algorithm(IDE)is greatly improved.It is shown that the differential evolution algorithm is a relatively stable evolutionary algorithm in solving the optimal value of the problem.At the same time,it is shown again that the improved search equation is an excellent search method.2.The 0-1 knapsack problem is a widely used optimization problem.This paper tries to solve the 0-1 knapsack problem with IABC algorithm and get good data results.Based on the improved artificial bee colony algorithm(IABC algorithm),a discrete artificial bee colony algorithm(DIABC algorithm)is proposed by integrating and discretizing the search equation.And it is used to solve the 0-1 knapsack problem.The standard test set of 0-1 knapsack problem is used to carry out numerical experiments,and a better optimal value is obtained.Solution time is relatively short.It is verified that the discrete artificial bee colony algorithm(DIABC algorithm)has good convergence and validity in solving 0-1 knapsack problem.3.In recent years,portfolio model has been deeply studied and widely applied in the financial field.The improved artificial bee colony algorithm is applied to the CVa R portfolio optimization problem.And this paper analyzes the data with actual data.The results show that the model can reasonably diversify the market risk of portfolio.It can effectively improve the return on investment of investors.The calculation speed is better fast.It further illustrates the reliability and extensiveness of the improved artificial bee colony algorithm in solving practical problems.4.Flow shop scheduling problem is applied in all walks of life.It is mainly used to reduce working time,reduce working cost and improve working efficiency.On the basis of IABC algorithm framework,the search equation is adjusted for initialization stage,employed bees stage and onlooker bees stage respectively.A discrete artificial bee colony algorithm(HIABC algorithm)for solving hybrid flow shop scheduling problem is designed.This paper stands establishing search procedures for employed bees,onlooker bees and scout bees.Through 15 classical examples,data experiments are carried out.And after repeated calculations,good results are obtained.The effectiveness and stability of the improved discrete artificial bee colony algorithm(HIABC algorithm)for solving the hybrid flow shop scheduling problem is demonstrated.