Energy Multi-objective Optimization Model Based On Genetic Algorithm

Multi-objective optimization is always a difficult and hot problem in energy science and engineering. And genetic algorithms can handle large space problem. We can get many feasible solutions in one evolution generation, and don't have request of prior knowledge. Therefore, solving energy optimization problems using genetic algorithms becomes research trend in this field. In fact, because energy optimization problems have some random and fuzzy factors, considering objective fuzzy optimization corresponds to the reality. Genetic algorithms have their own limitations. Solving fuzzy optimization problems using genetic algorithms have some defects. In view of this, study the theory and methods of genetic algorithms and fuzzy multi-objective optimization. Based on the existing multi-objective genetic algorithms, combine the feasible direction and membership function into genetic algorithm, and applied them into energy fuzzy optimizations model.Combine the feasible direction into genetic algorithm. This method can lead the individual to optimal solution region along feasible direction which approach the optimal solution sets. Through evaluating the degree of distance between chromosome and constrain, we introduce membership function into traditional GA and embed the information of infeasible solutions into fitness function. Propose a self-adapting evaluation function. This method can readjust the weights according to current group and then get the stress of searching to the ideal positive point. Taken environment and economy into traditional energy optimizations, set up FOLP-3E model with fuzzy objective and fuzzy constrain. Apply the improved multi-objective genetic algorithm in model, and achieve the purpose of global optimization. To a kind of fuzzy multi-objective optimization problem, propose a method of best satisfaction to transform the fuzzy models to clear ones and solve the models using GA based on interactive method. Then testify its validity though examples. Result show that model and algorithm are feasible to forecast of energy optimization.