Research Of Differential Evolutionary Algorithm To Solve Multi-objective Optimization Problems

There are many optimization problems in our lives,the multi-objective optimization problems have multiple object functions,which usually influence each other and even contradict each other.Nowadays,using the evolutionary algorithm to solve multi-objective optimization problem has become a research hotspot in the field of multi-objective optimization,to solve different types of multi-objective optimization problems,many researchers have proposed different multi-objective evolutionary algorithms,and some excellent algorithms have been successfully applied in solving the practical problems.It can be said that using evolutionary algorithm to solve multi-objective optimization problems,is receiving more and more people's attention.This dissertation expounded the basic theoretical knowledge of multi-objective evolutionary algorithms,and introduced the current research status of multi-objective evolutionary algorithms.And then two excellent algorithms are designed to solve different type of Multi-objective optimization problem.In this dissertation,the main research work is as follows:This dissertation proposes a new kind of multi-objective differential evolution algorithm:Multi-objective Differential Evolution Algorithm Base on Excellent Individual and Evolutionary-strategy Self-Adaption.On the basis of the traditional finite difference algorithm,MODEA-EESA introduced a kind of evolutionary strategy based on excellent individuals,at the same time,combining with different mutation operators which have different effect,this dissertation proposes a dynamic selection mutation operator,and given the concept of a threshold for the selection of mutation operator intervention.In the distribution of degree maintain strategy,is using the classic distribution degree maintain method based on crowding distance.MODEA-EESA will compare with NSGA-?,MODEA and PAES through a series of test functions.This dissertation presents a new Multi-Objective Optimization Algorithm Based on Non-Dominated Sorting and Bidirectional Local Search(NSDLS).The algorithm takes local beam search as the main body,NSDLS outputs the non-dominated solution set through continuous iterative search when iteration termination condition is satisfied.It's worthy to note that the iteration of NSDLS is similar to the generation of evolutionary algorithm;therefore,this dissertation uses generation to represent the iterations.The main algorithmic process of NSDLS can be summarized as:NSDLS maintains a population of size N and leads algorithm to approximate the Pareto-optimal front through continuous iteration.In each iteration process,set a given population Pt first,in which t represents the generation;then,algorithm introduces a bidirectional local search strategy based on improved differential operation to produce a better population Pt';then,algorithm uses fast non-dominated sorting algorithm to sort the combined population Pt' U Pt and generate partial sequence boundaries,in this process,algorithm introduces a new distribution maintaining strategy based on the sampling theory to combine with the fast non-dominated sorting algorithm in order to select a new population into the next iteration.NSDLS will compare with three classical algorithms:NSGA-II,MOEA/D-DE,and MODEA through a series of bi-objective test problem,the proposed NSLS is able to find a better spread of solutions and a better convergence to the true Pareto-optimal front compared to the other four algorithms.