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Uinform Design and Entropy Based Genetic Algorithm and Its Application in Linear System Control

Author: ZhangChangLi
Tutor: DuYongGui
School: Taiyuan University of Technology
Course: Systems Engineering
Keywords: Genetic Algorithms Uniform Design Entropy LQ problem KX - function multi-input observer
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 49
Quote: 1
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Abstract


Many of the world's problems can be attributed to the search and optimization problems, the genetic algorithm for solving such problems has now become killer, especially for non-traditional multivariate complex problem solving. Oriented random search genetic algorithm is a simulation of natural biological process of evolution and genetic has strong robustness parallel heuristic optimization algorithm. Tai Chi fitness-oriented and random crossover and mutation mechanism to make it along with the global optimization and local search ability, but as a guide random search algorithm, solving the resulting instability, prematurity, it also has shortcomings. The advantages and disadvantages of genetic algorithm, we learn from each other, the heritage of previous work to further integrate into the genetic algorithm, uniform design and information entropy thought the idea of ??a deception and improved genetic algorithm. On the three commonly used benchmark function test results show that the proposed algorithm optimization performance, stability, convergence speed and high precision. The main work and innovation 1) This is the first genetic algorithm genetic operators emergence and development of the course and how to combine the unique perspective of the application with specific issues, elaborated on the development and application of genetic algorithm, and on this basis the the approximate development trend of the future of genetic algorithm. 2) This is the first systematic exposition of the uniform design method to generate the initial population, compared with randomly generated initial population distribution in the parameter space. 3) This is the first comprehensive introduction to the population entropy S defined genetic algorithm, genetic entropy G entropy E and population characteristics of the individual characteristics of entropy PE is defined and based on the simulation results of entropy is too large or is too expressed in physical meaning, given the conclusions. 4) the conclusions of the various entropy used in genetic algorithm, genetic algorithm search process to discover deception, and to guide the algorithm to jump out of the deception, also can be designed according to the population entropy S adaptive adjusting the size of the individual fitness fitness function. 5) This is the first application of genetic algorithm solving and multi-input KX-function observer design is optimized linear system control in infinite time linear quadratic optimal control matrix Riccati algebraic equations.

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