The Optimization Of Adaptive Genetic Algorithm In Mine Equipment System

The research of the underground metal mine equipment system optimization becomes more urgent. Because the real features of the underground metal mine make all kinds of classical optimization methods appearing powerless in this matter. Then how to find an effective optimization method using a reasonable optimization to optimize the system, in accordance with the actual production of the underground metal mine, becomes a key to this problem. Based on the actual production requirements of the Datun tin, this article does a research in solving the mine equipment system optimization problems. This article includes the following sections:1. From the actual production of the underground metal mine, analyzes the characteristics of the underground metal mine and its optimization mechanism. And on this basis, research the equipment production system's objective function of the mine production and propose the feasible objective function based on the optimization purposes.2. Study the genetic algorithm and its theory. According to the characteristics of the genetic algorithm, this article made a thorough study of the basic mechanism, the key technology, the formulation and the application of the genetic algorithms. On the basis of the genetic algorithm, the application of matter in solving the underground metal mine equipment system optimization is researched and studied.3. According to the database's data feature about the underground metal mine equipment system, the solution of the system optimization by using the adaptive genetic algorithm is proposed. Then the key technologies of the solution have been described in detail and the adaptive genetic algorithm to solve this problem which is feasible has been proved.4. Basing on the Datun tin's actual requirement of production, the preliminary optimization results of equipment system optimization are obtained. By analyzing the optimizing results and the mine's current actual production, the feasibility and superiority of the optimization results are demonstrated. With a detailed comparative analysis of the optimization results, elucidate the optimization differences in all cases, analysis the causes and give the reasonable explanation. Then advantages of adaptive genetic algorithm in equipment system optimization have been testified and the application scope of genetic algorithm has been broadened.