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.

煤炭生产的关键方面是实现安全有效的开采，以最大限度地利用资源。实现可持续的经济发展的要求是实现绿色煤炭生产，这受煤炭经济，能源，生态，煤gang石经济和社会效益等因素的影响。为了平衡这些因素，本文提出了具有五个目标的绿色煤炭生产的多目标优化模型。此外，设计了一种混合多目标粒子群优化算法（HMaPSO）来求解所建立的模型。通过使用不同的进化算子可以生成替代池的新后代。采用环境选择机制来选择和存储优秀的解决方案。进行了两组实验以验证所提出方法的有效性：首先，在DTLZ函数上测试了HMaPSO算法，并将其性能与几种广泛使用的多目标算法进行了比较。其次，应用HMaPSO算法求解多目标绿色煤炭生产优化模型。计算结果证明了该方法的有效性，仿真结果表明所设计的方法可以为区域规划中的决策者提供有希望的选择。