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Hybrid many-objective particle swarm optimization algorithm for green coal production problem

文献类型：期刊论文

作者Cui, Zhihua4; Zhang, Jiangjiang4; Wu, Di4; Cai, Xingjuan4; Wang, Hui3; Zhang, Wensheng1; Chen, Jinjun2
刊名INFORMATION SCIENCES
出版日期2020-05-01
卷号518页码:256-271
关键词Coal production Many-objective optimization problems Evolutionary operators Particle swarm optimization (PSO)
ISSN号0020-0255
DOI10.1016/j.ins.2020.01.018
通讯作者Cai, Xingjuan(caixingjuan@tyust.edu.cn) ; Wang, Hui(huiwang@whu.edu.cn)
英文摘要The 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.
WOS关键词MULTIOBJECTIVE OPTIMIZATION ; EVOLUTIONARY ALGORITHM ; RATE ALLOCATION ; SELECTION ; NETWORKS ; MODEL ; POWER
资助项目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]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000517658600017
出版者ELSEVIER SCIENCE INC
资助机构National 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
源URL[http://ir.ia.ac.cn/handle/173211/38365]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Cai, Xingjuan; Wang, Hui
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing 100190, Peoples R China
2.Swinburne Univ Technol, Dept Comp Sci & Software Engn, Melbourne, Vic 3000, Australia
3.Nanchang Inst Technol, Sch Informat Engn, Nanchang 330099, Peoples R China
4.Taiyuan Univ Sci & Technol, Complex Syst & Computat Intelligence Lab, Taiyuan 030024, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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