The Research And Application Of Dynamic Constrained Multiobjective Optimization Problems

Dynamic optimization problems are the most popular research direction in recent years at home and abroad, it is a kind of intelligent optimization algorithm of adaptive iteration, and a kind of evolutionary algorithm what is based on the basis of the dynamic constrained multiobjective intelligent optimization algorithms. To solving the decision variables what is the change of time (environment) or dynamic change on the question of good intelligence. It not only can ensure the original overall outside but also can effectively avoid the overall convergence algorithm of blind into local convergence so that the optimal solution cannot be found. Dynamic constraints, a multi-objective evolutionary algorithm is also to be a certain extent, to solve the intelligent optimization algorithm,it is not adaptive decision variables in the following time (environment) and the dynamic changes of intractable problems. At least in order to solve direction, it has a very good idea, this is the direction for the majority of the scholars has brought a lot of the Gospel. It has be presented in this paper on the research results of multi-objective evolutionary algorithm and gives some innovation after learning some dynamic constraints, such as looking for interval segmentation in the non-uniform mutation operator, in order to control the uniform degree of variation, but if we use the default value such as 0.5 in this passage that is not scientific, so that there are some improvement in this paper. Another is in the initial group is divided into dynamic constraints, a multi-objective evolutionary algorithm based on classification of population. The experimental results are also the two improved specific numerical experiment and evaluated.This paper does the main work as follows:1. This paper introduces the dynamic constraints briefly, the research history of a multi-objective evolutionary algorithm and current status of research, summarizes the research significance and the main content of the paper.2. The structure of the evolutionary algorithm is analyzed, including the basic principle of evolutionary algorithms, the algorithm process and parameter Settings as well as the advantages and disadvantages of the algorithm.3. Introduces the basic thought of dynamic constraints, a multi-objective evolutionary algorithm, the algorithm steps are given.4. The paper introduces the improved multi-objective evolutionary algorithm for dynamic constraints, and then gives the detailed algorithm process steps and algorithm, finally used in the numerical experiment and evaluation according to the results of the experimental results to verify the superiority of the algorithm.5. The paper introduces the dynamic constraints, a multi-objective evolutionary algorithm based on species classification, detailed algorithm steps and process are given, and numerical experiments to verify the performance of the algorithm above improvement.