Data-Driven Surrogate-Assisted Multi-Objective Optimization of Complex Beneficiation Operational Process


Most optimization algorithms suppose that there exist explicit evaluation functions to evaluate the candidate solutions obtained during the optimization process. However, in actual industrial processes, it is usually very difficult to build up precise mathematical models to describe complex industrial processes due to the lack of clear mechanisms. Instead, only some noisy historical data can be used, and optimization of such problem is often known as data-driven optimization. The optimization of complex beneficiation operational process is a typical data-driven optimization problem. To solve this problem, an evolutionary algorithm assisted by Gaussian process model is proposed in this paper. To be specific, a low-order neural network model is constructed by using the data collected from mineral processing factory as real objective function, and a Gaussian process model is developed as a surrogate to reduce the number of real function evaluations. We test the new method on a series of multi-objective test instances against two other algorithms. The experimental results indicate that the proposed method has the ability to achieve significant improvement at the limited budget of real function evaluations. In addition, the proposed algorithm is also successfully applied to the optimization of complex beneficiation operational process.