Improved Genetic Algorithm And The Application Of It In Cost Analysis Of Electricity Generating Enterprises

Genetic Algorithm, abbreviated as GA, put forward in 1975 by American scholar John Holland, was a probabilistic iterative search algorithm based on the Darwinian Theory of Biological Evolution. Applying Genetic Algorithm to solving complicated problems, such as Combinational Optimization, Pattern Recognition and image processing, etc, which are difficult to be solved by traditional methods, can obtain more satisfactory results. Recent years, the robustness, globality, implicit parallelism and adaptivity, reflected by GA when solving the functional optimization of continuous variable and the combinational optimization of discrete variable, make GA as an intelligent optimization algorithm used widely nowadays.Recent years, with the rapid development of the national economy and the gradual increase of the consumption of energy, energy saving and consumption reduction become a basic national policy. It is difficult for the thermal power enterprises to raise the surfer electricity price or increase the surfer electricity quantity, in which case upgrading the management and lowering the power generating cost become feasible to improve the economic performance. The production process of the thermal electricity enterprises is one of chemical change and physical change. Given the same output, each boiler or machine unit will need different production costs according to different working conditions. Therefore, it adds complexity to the costing and it is difficult to use conventional searching methods to effectively solve the problems during the optimization process. However, the present rapidly developing thoughts, in which problems are solved according to natural principles, are exhibiting extraordinary potential in solving the large-scale combinational optimization problems. GA is one of the branches of these thoughts, thus becoming the research target of many researchers in studying the optimization and combination of every production segment in the thermal electricity enterprises.GA is a kind of random optimization searching algorithm building on the mechanism of natural selection and natural heredity in biosphere. It has the characteristics of simplicity, generality, stableness and robustness, etc. By using probability, it can converge the best overall optimization solution of the problems. Furthermore, it almost needs no knowledge about the problems in solving them. But it's also because of this that GA cannot fully utilize the relative information of the problems themselves, which affects the efficiency in solving the concrete problems. Consequently, there should be large space for improvement in researching and utilizing the GA to solve the costing problems of electricity generating plants, which is also the major purpose and significance of this paper in putting forward an improved GA to solve the costing problems of electricity generating enterprises.This paper, with a background of the daily cost analysis of a certain thermal electricity enterprise, based on the analysis research of the GA and the cost accounting of the electricity generating enterprises, applies the improved GA to the operational cost analysis mechanism of the electricity generating enterprise and to the industrial practice to prove, through the cost accounting and comparison of enterprises, the actual effects of the operational cost analysis strategy of the electricity generating enterprises based on the improved GA.This paper first introduces the developing history, current situation of research and the present application of GA, sums up the relative concepts, basic theories and steps of GA, makes a brief analysis of the standard GA and introduces some improved GA, emphasizing the representative improved GA in combination with the object features of the field researched by this paper, that is the process characteristics of the cost accounting of the thermal electricity factory. Then it introduces the implementation method and value of Activity Based Costing in thermal electricity enterprises, along with the relative contents of the implementation process of Activity Based Costing in the enterprises'management. Meanwhile,with the object of a certain typical thermal electricity enterprise, it induces the practical mathematical model of costing in thermal electricity enterprises according to the calculation ways and methods of Activity Based Costing. According to the mathematical model, this paper puts forward the operational cost analysis system based on the improved GA. It also introduces the classification of agents in Activity Based Costing and holds that the agents should be classified according to their different proportions in costing. In this way, it introduces a multi-swarm and multi-level GA to carry out an optimization algorithm in costing. At the same time,it classifies the group based on the traditional GA to improve the diversity of the group. It also utilizes GA to achieve the optimized search for the difficultly measured monomer agent and at the same time it classifies each group to increase the convergence speed of GA. Lastly, this paper uses the mixed program of C# Language and Delphi to conduct an algorithm and construct an operational cost analysis system of the thermal electricity enterprise based on the improved GA. The result of this system in the practical application to a certain thermal electricity enterprise in Jining reveals that the application of this algorithm improves the algorithm speed and optimizes the algorithm results. On the basis of this, this system conducts the algorithm evolution according to the practical demand of the enterprises'productivity so as to be adapted to the changes of the group quantity and various levels of groups and to obtain the more optimized group quantity and group grade, which lays a foundation for further improving the practical value of the algorithm.