A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process

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...

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Main Authors: Xiaowei Gu, Xunhong Wang, Zaobao Liu, Wenhua Zha, Xiaochuan Xu, Minggui Zheng
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8985327/
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spelling doaj-31c089d0658443e5b92d22c6359033ba2021-03-30T02:12:04ZengIEEEIEEE Access2169-35362020-01-018288472885810.1109/ACCESS.2020.29720188985327A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production ProcessXiaowei Gu0https://orcid.org/0000-0003-4581-2232Xunhong Wang1https://orcid.org/0000-0003-2660-1413Zaobao Liu2https://orcid.org/0000-0002-2047-5463Wenhua Zha3https://orcid.org/0000-0002-0500-8517Xiaochuan Xu4https://orcid.org/0000-0003-3696-3274Minggui Zheng5https://orcid.org/0000-0002-6950-9353Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, ChinaKey Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, ChinaKey Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, ChinaCollege of Civil and Construction Engineering, East China University of Technology, Nanchang, ChinaKey Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, ChinaResearch Center of Mining Trade and Investment, Jiangxi University of Science and Technology, Ganzhou, ChinaProduction 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.https://ieeexplore.ieee.org/document/8985327/Metal mines production processmulti-objective optimizationsymmetric Latin hypercube designdifferential evolutionparameter adaptationimproved NSGA-II
collection DOAJ
language English
format Article
sources DOAJ
author Xiaowei Gu
Xunhong Wang
Zaobao Liu
Wenhua Zha
Xiaochuan Xu
Minggui Zheng
spellingShingle Xiaowei Gu
Xunhong Wang
Zaobao Liu
Wenhua Zha
Xiaochuan Xu
Minggui Zheng
A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process
IEEE Access
Metal mines production process
multi-objective optimization
symmetric Latin hypercube design
differential evolution
parameter adaptation
improved NSGA-II
author_facet Xiaowei Gu
Xunhong Wang
Zaobao Liu
Wenhua Zha
Xiaochuan Xu
Minggui Zheng
author_sort Xiaowei Gu
title A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process
title_short A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process
title_full A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process
title_fullStr A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process
title_full_unstemmed A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process
title_sort multi-objective optimization model using improved nsga-ii for optimizing metal mines production process
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description 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.
topic Metal mines production process
multi-objective optimization
symmetric Latin hypercube design
differential evolution
parameter adaptation
improved NSGA-II
url https://ieeexplore.ieee.org/document/8985327/
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