Integrated Differential Evolution Algorithm And Its Application

With the development of science and technology,some function optimization problems involved in the field of engineering are becoming more and more complicated.They often have the characteristics of unguided,discontinuous,and multi-peak.Traditional mathematical methods have been difficult to obtain ideal results.For the function optimization problem,it can be solved by evolutionary algorithm.Among them,the differential evolution algorithm has proved to be one of the most powerful evolution algorithms.However,the setting of some parameters in the differential evolution algorithm has a very large impact on performance.In order to solve this defect,researchers have proposed many different parameter adaptation techniques.Different parameter adaptation techniques have different characteristics,and they are suitable for different types of function optimization problems.The integrated framework of the differential evolution algorithm can combine different parameter adaptation techniques of the differential evolution algorithm,thereby improving the original performance of the algorithm.In the framework,the entire population is divided into several subpopulations,and each subpopulation uses different variants of the differential evolution algorithm,and they have their own parameter adaptation techniques.During the evolution process,the evolution of each sub-population does not affect each other.Only when mutated individuals are generated,they are selected from the entire population in order to exchange information.As an example,the differential evolution algorithm of two existing parameter adaptation techniques was applied to the framework,and related experiments were conducted.Through the test of TSP(Travelling salesman problem)and a function test set commonly used internationally,the final experimental results show that by using the proposed framework,the integrated differential evolution algorithm is more stable and convergent than the previous separate algorithms.A certain improvement,and can solve more types of function optimization problems.