Abstract
Production process optimization is an indispensable step in industrial production. The optimization of the metal mines production process (MMPP) can increase production efficiency and thus promote the utilization rate of the metal mineral resources in the frame work of sustainable development. This study establishes a multi-objective optimization model for optimizing the MMPP by maximizing economic and resource benefits. To get better non-dominated Pareto optimal solutions, an improved non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. The symmetric Latin hypercube design is adopted to generate the initial population with high diversity. The mutation and crossover of the differential evolution algorithms are introduced into the NSGA-II to replace the genetic algorithm for improving convergence. The control parameters of the mutation scale factor and crossover rate of the differential evolution algorithm are adaptively adjusted to improve the diversity of candidate solutions. To verify the performance of the improved NSGA-II, four test functions from the ZDT series functions are chosen for experimentation. The experimental results indicate that the improved NSGA-II outperforms the comparative algorithms in diversity and convergence. Moreover, the application of the proposed method to the Yinshan copper mines shows that the improved NSGA-II is effective in optimizing the MMPP and a reliable method in promoting utilization rate of metal mineral resources in the framework of sustainable development.
Highlights
Metal mineral resources are non-renewable resources and raw materials for industrial development
In order to improve the convergence of non-dominated sorting genetic algorithm-II (NSGA-II), the mutation and crossover of the differential evolution algorithm are introduced into the NSGA-II to replace the genetic algorithm
2) RESULTS ANALYSIS OF YINSHAN COPPER MINES The improved NSGA-II, the NSGA-II, and the non-dominated sorting differential evolution (NSDE) are used to optimize the Yinshan copper mines in order to validate the outperformance of the improved NSGA-II
Summary
Metal mineral resources are non-renewable resources and raw materials for industrial development. X. Gu et al.: Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing MMPP many input indexes. The Lane’s theory, the dynamic programming method, and the single-objective evolutionary algorithms belong to the class of single-objective optimization method Those single-objective optimization methods have made many achievements in metal mines optimization, in which only a single objective is considered. The NSGA-II method was applied to optimize the multi-objective optimization model of the MMPP, which considers both the economic and resource benefits [16]. MULTI-OBJECTIVE OPTIMIZATION MODEL OF METAL MINES PRODUCTION PROCESS An multi-objective optimization model was introduced for the MMPP in a former work [16] as a first trial. This paper briefly introduces the multi-objective optimization model of the MMPP. It should be noted that the metal mines in China use the ‘‘double-grade’’ (geological cutoff grade and minimum industrial grade) instead of the international ‘‘single-grade’’ (cutoff grade)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
