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

doi:10.1016/j.ins.2020.01.018

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Hybrid many-objective particle swarm optimization algorithm for green coal production problem
	Cui, Zhihua 1; Zhang, Jiangjiang 1; Wu, Di 1; Cai, Xingjuan 1; Wang, Hui 2; Zhang, Wensheng3 ; Chen, Jinjun 4
发表期刊	INFORMATION SCIENCES
ISSN	0020-0255
	2020-05-01
卷号	518 页码:256-271
通讯作者	Cai, Xingjuan(caixingjuan@tyust.edu.cn) ; Wang, Hui(huiwang@whu.edu.cn)
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.
关键词	Coal production Many-objective optimization problems Evolutionary operators Particle swarm optimization (PSO)
DOI	10.1016/j.ins.2020.01.018
关键词[WOS]	MULTIOBJECTIVE OPTIMIZATION ; EVOLUTIONARY ALGORITHM ; RATE ALLOCATION ; SELECTION ; NETWORKS ; MODEL ; POWER
收录类别	SCI
语种	英语
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] ; 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]
项目资助者	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
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000517658600017
出版者	ELSEVIER SCIENCE INC
引用统计	被引频次：161[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/38366
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
通讯作者	Cai, Xingjuan; Wang, Hui
作者单位	1.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
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.