The Optimization Of Coal Cryogenic Distillation Process Based On Genetic Algorithm

Genetic algorithm is a highly parallel,randomized and adaptive search algorithm, develo- pmenting on natural selection and evolutionary of biological mechanisms.Its research history is short,early it is a constructed artificial system model of simulating the mechanisms of biol- ogical evolution from attempting to explain the complexity process of biological in the natur- al system.Formed in recent years around the world boom of evolutionary computation,compu- tational intelligence became one of the major directions of artificial intelligence research,as well as the subsequent rise of artificial life research,so that genetic algorithms is concerned broad.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 and some shortages of genetic algorithms are pointed out.(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. Manhattan distance of each individual is considered in the adaptive operators of crossover,mutation and the method to get the first generation.(3) Adaptive genetic algorithm with improved low temperature carbonization of coal issues during the optimization study, the whole system resource utilization, the overall efficiency is efficiency is maximized. The results demonstrate that the algorithm has very good quality.