Study On Genetic Algorithms Of Coal Co-production Chemical Industry

As a new optimization method,Genetic algorithms was widely used in the optimizations of many fields owing to the features of simplicity,easily handing and parallel processing .However Genetic algorithms theory is not perfect,such as there exist the problems of easily creating earliness and bad ability in local optimal,etc.In this paper,the elementary theory and methods of genetic algorithms is introduced.Some improvement of genetic algorithms on the astringency and searching efficiency are presented. The main content of this thesis includes the following:(1) the theory and realization of genetic algorithms is analyzed and summarized.The factors of genetic algorithms are analyzed synthetically those including coding method, genetic operator,fitness evaluate method and the parameters of genetic algorithms,some indexes for estimating the capability of genetic algorithms are given.(2) Genetic algorithms is widely used as a kind of global optimization method. In order to achieve the balance between its convergence speed and efficiency,an improved Adaptive Genetic algorithms is brought forward in this paper. Euclidean distance of each individual is considered in the adaptive operators of crossover,mutation and the method to get the first generation.(3) Using the improved Adaptive Genetic algorithms in the integrated co-production system of coal.The new system can realize the graded utilization of coal resource value by the optimum integration of several coal conversion technologies,so as to realize the maximization of value improvement,utilization efficiency and economic benefit during the utilization of coal resources. The simulation tests are made and the results demonstrate the efficiency of the above methods.