Multiobjective optimization for planning of mineral processing under varied equipment capability

To solve multiobjective plan optimization problem of mineral processing under varied equipment capability, an improved dynamic multi-objective optimization algorithm based on random immigrants and elitism approach is proposed. Based on the G-NSGA-II [1], the random immigrant populations are introduced to enhance the diversity of population firstly. Secondly, the fitness function is calculated for the second parent and offspring when the variation occurs, and uses these fitness values of the new evolutionary computation to acquire optimal solution of dynamic multi-objective optimization problem. The proposed approach is validated using the actual production data collected from an iron ore beneficiation process. The experiment study on the constraint conditions (i.e. the equipment capability) changing with the working conditions in the planning optimization of production index decision problem is carried out. Experiment results show that the proposed algorithm can follow the changes of the environment, and be able to acquire the new environment optimal solution.