Analysis And Application Research Of Poor And Rich Algorithm And Artificial Hummingbird Algorithm

Swarm intelligent optimization algorithm is based on the simulation of biological habits or physical phenomena,so as to establish the corresponding mathematical model,and used to solve the optimization problem.At present,swarm intelligent optimization algorithm has been widely used in science,engineering and other fields.Poor and rich optimization algorithm(PRO)and artificial hummingbird algorithm(AHA)are new swarm intelligent optimization algorithms proposed in recent years,but they still have the problem of premature convergence.In order to improve the optimization performance of these two algorithms,this paper analyzes the shortcomings of the poor and rich optimization algorithm and artificial hummingbird algorithm,and carries out improvement research on them,and has obtained certain research results.The specific research results are as follows:(1)An improved version of the poor and rich optimization algorithm(IPRO)is proposed,which adopts multiple search strategies.The rich individuals in the search process not only consider the difference between themselves and the richest individual but also take into account the class difference between themselves and the optimal poorest individual.The Levy step size is introduced to increase the diversity of search by rich individuals.In addition,a dynamic search mechanism is introduced to increase the diversity of search by poor individuals,to balance the global search and local exploitation of the algorithm.The optimization performance of IPRO is verified through numerical simulation experiments.(2)A differential artificial hummingbird algorithm using skewed Laplace step size is proposed.The skewed Laplace step size is used to enhance the exploration and exploitation capabilities of the algorithm.The differential optimization operator is introduced to increase the diversity of population search,thus enhancing the global search capability of the algorithm.Optimal solution guidance strategy and adaptive flight parameters are utilized to improve the convergence speed of the algorithm.Experimental results show that the proposed improved AHA algorithm has significantly improved optimization accuracy and convergence speed.(3)A new discrete artificial hummingbird algorithm(DAHA)is proposed:Firstly,a discrete coding method is used to imitate the movement pattern of hummingbirds for the three stages(foraging guidance,area foraging,and migration foraging)in the artificial hummingbird algorithm.Secondly,DAHA is applied to solve the hybrid flow shop scheduling problem.Experimental results show that DAHA exhibits good optimization performance in solving discrete engineering problems.