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Elitist Nondominated Sorting Genetic Algorithm And Its Application

Posted on:2007-11-28Degree:MasterType:Thesis
Country:ChinaCandidate:Q ZhengFull Text:PDF
GTID:2178360182990527Subject:Control theory and control engineering
Abstract/Summary:PDF Full Text Request
Multi-objective optimization is a research focus as well as a difficult problem in the fields of science and engineering. The conventional methods of multi-objective optimization have been used to solve the problems associated with the multi-objective optimization, but these methods show several shortcomings in some complicated multi-objective problems, and they are gradually replaced by other methods. Nondominated Sorting Genetic Algorithm(NSGA) shows great advantages in the problems of multi-objective optimization, but it has also aroused criticisms in several aspects after widely used. In order to solve the problems of multi-objective optimization more effectively, the Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) is proposed on the basis of NSGA.The research of NSGA-II from both theoretical and practical perspectives is conducted extensively in other countries, however, the research associated with this aspect is rather limited domestically. In this paper, the basic theory of NSGA-II is studied, and more importantly, NSGA-II is resorted to solve some practical problems of multi-objective optimization. With the great advantages of NSGA-II, those practical problems are solved well.The main contents are as follows:(1) The problems of multi-objective optimization and the current state of the research on these problems are introduced. Meanwhile, the basic theory of genetic algorithm is also systematically presented. In addition, greatimportance is attached to the introduction of NSGA and especially the basic theory of NSGA-II.(2) The concept and the harm of harmonic as well as how the harmonic come into being are introduced, and in order to eliminate the harmonic, NSGA-II is used to design the passive filter. Through analyzing the harmonic from an aluminum factory, the minimum initial cost and the maximum filtering rate of passive filter are set as the objectives of the passive filter's optimization model in this paper. After optimized by NSGA-II, a series of optimal solutions are obtained. And this method of optimization proves effective through simulation.(3) NSGA-II is adopted to identify the parameters of the kinetic model of methanol-to-hydrocarbons process and the dynamic model of catalytic cracking of diesel oil. These models can be denoted by differential equations, but the models are uncertain due to the unknown parameters. In order to get the exact models, the identification of the unknown parameters is a necessity. This paper proposed a new method of parameters identification using NSGA-II and one-step-integral Treanor algorithm, and this method is proved to be more effective than conventional ones.
Keywords/Search Tags:Multi-objective optimization, Non-dominated Sorting Genetic Algorithm (NSGA), Elitist Nondominated Sorting Genetic Algorithm(NSGA-II), Harmonic, Passive filter, Parameters identification, Optimal solutions, Treanor method
PDF Full Text Request
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