Application Of Multi-Objective Genetic Algorithm In Enterprise Energy Plan Research

Energy and environmental issues has become the focus of widespread concern in society today. In the global energy shortage at present situation, the energy conservation and emission reduction has become very urgent. In the face of "12th Five-Year" severe energy situation, the government implements the supervision to the key energy-consuming enterprises strongly. Therefore, in order to save energy and cost, the optimization of enterprise energy system is very important. Enterprise energy optimization problem has become the research hotspot in multi-objective optimization problems. While the multi-objective genetic algorithm is current the most popular way of solve the multi-objective optimization problem. Multi-objective genetic algorithm has had the very good application in solving a lot of optimization problems.In this paper, based on the theory of multi-objective genetic algorithm, fast non-dominated sorting genetic algorithm (NSGA_II) is studied. According to the deficiency of NSGA_II in genetic operators, the NSGA_Ⅱ algorithm is improved. Arithmetic crossover operator is introduced and its coefficient improved, which solves insufficient performance problem of the simulated binary crossover operator. At the same time, the feasible direction method introduces into mutation operation and put forward a kind of feasible direction mutation operator. The individual along the feasible direction fast convergence to the optimal solution set. And the genetic cross-border processing method was improved in the process of operation. We design a new kind of cross-border processing method which is related to the endpoint value and the individual before operation. It make the diversity of species have better.In enterprise energy optimization problem, the theory of input-output method is studied in this paper. It introduces input-output method in enterprise energy planning. Taking a copper pipe enterprise for example, we collect and handle its data, and compile the input-output table. Considering the energy, economic and environment, the enterprise multi-objective input-output optimization model is established.Finally, through collecting the data of the copper pipe enterprise, its objective function and constraint conditions are calculated. Then, the improved multi-objective genetic algorithm is applied to the multi-objective input-output optimization model. We obtain the results through calculating the optimization model of calculation. The results show the individual has good convergence to optimal solution set. The optimal solution shows that algorithm achieves good effect to solve the model. Energy、economic、environmental benefits of the enterprise has been improved to some extent. It has the certain reference value to the enterprise.