Journal
INFORMATION SCIENCES
Volume 518, Issue -, Pages 256-271Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.01.018
Keywords
Coal production; Many-objective optimization problems; Evolutionary operators; Particle swarm optimization (PSO)
Categories
Funding
- National Key Research and Development Program of China [2018YFC1604000]
- National Natural Science Foundation of China [61806138, 61663028, U1636220, 61961160707, 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]
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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.
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