CASIA OpenIR
Hybrid many-objective particle swarm optimization algorithm for green coal production problem
Cui, Zhihua1; Zhang, Jiangjiang1; Wu, Di1; Cai, Xingjuan1; Wang, Hui2; Zhang, Wensheng3; Chen, Jinjun4
Source PublicationINFORMATION SCIENCES
ISSN0020-0255
2020-05-01
Volume518Pages:256-271
Corresponding AuthorCai, Xingjuan(caixingjuan@tyust.edu.cn) ; Wang, Hui(huiwang@whu.edu.cn)
AbstractThe key aspect in coal production is realizing safe and efficient mining to maximize the utilization of the resources. A requirement for sustainable economic development is realizing green coal production, which is influenced by factors of coal economic, energy, ecological, coal gangue economic and social benefits. To balance these factors, this paper proposes a many-objective optimization model with five objectives for green coal production. Furthermore, a hybrid many-objective particle swarm optimization (HMaPSO) algorithm is designed to solve the established model. A new offspring of the alternative pool is generated by employing different evolutionary operators. The environmental selection mechanism is adopted to select and store the excellent solutions. Two sets of experiments are performed to verify the effectiveness of the proposed approach: First, the HMaPSO algorithm is tested on the DTLZ functions, and its performance is compared with that of several widely used many-objective algorithms. Second, the HMaPSO algorithm is applied to solve the many-objective green coal production optimization model. The computational results demonstrate the effectiveness of the proposed approach, and the simulation results prove that the designed approach can provide promising choices for decision makers in regional planning. (C) 2020 Elsevier Inc. All rights reserved.
KeywordCoal production Many-objective optimization problems Evolutionary operators Particle swarm optimization (PSO)
DOI10.1016/j.ins.2020.01.018
WOS KeywordMULTIOBJECTIVE OPTIMIZATION ; EVOLUTIONARY ALGORITHM ; RATE ALLOCATION ; SELECTION ; NETWORKS ; MODEL ; POWER
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2018YFC1604000] ; National Natural Science Foundation of China[61806138] ; National Natural Science Foundation of China[61663028] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61976212] ; Key R&D program of Shanxi Province (International Cooperation)[201903D421048] ; Key R&D program of Shanxi Province (High Technology)[201903D121119] ; Distinguished Young Talents Plan of Jiangxi Province[20171BCB23075] ; Postgraduate education Innovation project of Shanxi province[2019SY495] ; National Key Research and Development Program of China[2018YFC1604000] ; National Natural Science Foundation of China[61806138] ; National Natural Science Foundation of China[61663028] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61976212] ; Key R&D program of Shanxi Province (International Cooperation)[201903D421048] ; Key R&D program of Shanxi Province (High Technology)[201903D121119] ; Distinguished Young Talents Plan of Jiangxi Province[20171BCB23075] ; Postgraduate education Innovation project of Shanxi province[2019SY495]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Key R&D program of Shanxi Province (International Cooperation) ; Key R&D program of Shanxi Province (High Technology) ; Distinguished Young Talents Plan of Jiangxi Province ; Postgraduate education Innovation project of Shanxi province
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000517658600017
PublisherELSEVIER SCIENCE INC
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38366
Collection中国科学院自动化研究所
Corresponding AuthorCai, Xingjuan; Wang, Hui
Affiliation1.Taiyuan Univ Sci & Technol, Complex Syst & Computat Intelligence Lab, Taiyuan 030024, Peoples R China
2.Nanchang Inst Technol, Sch Informat Engn, Nanchang 330099, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing 100190, Peoples R China
4.Swinburne Univ Technol, Dept Comp Sci & Software Engn, Melbourne, Vic 3000, Australia
Recommended Citation
GB/T 7714
Cui, Zhihua,Zhang, Jiangjiang,Wu, Di,et al. Hybrid many-objective particle swarm optimization algorithm for green coal production problem[J]. INFORMATION SCIENCES,2020,518:256-271.
APA Cui, Zhihua.,Zhang, Jiangjiang.,Wu, Di.,Cai, Xingjuan.,Wang, Hui.,...&Chen, Jinjun.(2020).Hybrid many-objective particle swarm optimization algorithm for green coal production problem.INFORMATION SCIENCES,518,256-271.
MLA Cui, Zhihua,et al."Hybrid many-objective particle swarm optimization algorithm for green coal production problem".INFORMATION SCIENCES 518(2020):256-271.
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