Application of multi-objective optimization based on genetic algorithms in mining processing enterprises

Abstract

Mining industry has been the pillar industry of the economic and social development but it is also an industry with high level of pollution and emission. Modern mining enterprises not only manage to maximize the economic benefits, but also consider environment protection while they make decisions. In order to solve the problem, we apply Genetic Algorithms (GA), which is most commonly used in Multi-objective optimization. However, the probability of crossover and mutation is a fixed value in common genetic algorithms. In this paper, we use auto-tuning strategy to adjust these probabilities, which means adjust the values by fitness function. We have taken the importance of environmental protection into account in the mathematic model. So the model can be widely used in other enterprises with high level of pollution and emissions. In the policy of sustainable development of China, the model is universal and objective.