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...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8985327/ |
id |
doaj-31c089d0658443e5b92d22c6359033ba |
---|---|
record_format |
Article |
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/ |
work_keys_str_mv |
AT xiaoweigu amultiobjectiveoptimizationmodelusingimprovednsgaiiforoptimizingmetalminesproductionprocess AT xunhongwang amultiobjectiveoptimizationmodelusingimprovednsgaiiforoptimizingmetalminesproductionprocess AT zaobaoliu amultiobjectiveoptimizationmodelusingimprovednsgaiiforoptimizingmetalminesproductionprocess AT wenhuazha amultiobjectiveoptimizationmodelusingimprovednsgaiiforoptimizingmetalminesproductionprocess AT xiaochuanxu amultiobjectiveoptimizationmodelusingimprovednsgaiiforoptimizingmetalminesproductionprocess AT mingguizheng amultiobjectiveoptimizationmodelusingimprovednsgaiiforoptimizingmetalminesproductionprocess AT xiaoweigu multiobjectiveoptimizationmodelusingimprovednsgaiiforoptimizingmetalminesproductionprocess AT xunhongwang multiobjectiveoptimizationmodelusingimprovednsgaiiforoptimizingmetalminesproductionprocess AT zaobaoliu multiobjectiveoptimizationmodelusingimprovednsgaiiforoptimizingmetalminesproductionprocess AT wenhuazha multiobjectiveoptimizationmodelusingimprovednsgaiiforoptimizingmetalminesproductionprocess AT xiaochuanxu multiobjectiveoptimizationmodelusingimprovednsgaiiforoptimizingmetalminesproductionprocess AT mingguizheng multiobjectiveoptimizationmodelusingimprovednsgaiiforoptimizingmetalminesproductionprocess |
_version_ |
1724185609178710016 |