| 基于加权聚合粒子群算法的矿井火灾救援研究 |
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| 引用本文： | 黄 萍,李兵磊.基于加权聚合粒子群算法的矿井火灾救援研究[J].福州大学学报(自然科学版),2016,44(6):868-873. |
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| 作者姓名： | 黄 萍 李兵磊 |
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| 作者单位： | 福州大学环境与资源学院，福建 福州 350116,福州大学紫金矿业学院，福建 福州 350116 |
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| 基金项目： | 国家自然科学基金项目（面上项目，重点项目，重大项目） |
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| 摘 要： | 提出一种基于加权聚合的最小中心粒子群算法,对粒子群优化算法的搜索范围与目标权重进行改进.仿真实验结果表明,采用此算法在典型的标准函数测试中训练速度快、精度高,对其在矿井火灾救援最佳救援路线优化模型中的性能进行测试和分析,可知该方法有利于避免早熟收敛,增强全局搜索能力,同时提高非劣最优解的精度,可为矿井火灾事故救援的决策提供重要技术支持.
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| 关 键 词： | 矿井 火灾救援 加权聚合 最小中心 粒子群算法 |
| 收稿时间： | 6/6/2016 12:00:00 AM |
Mine fire rescue based on weighted aggregation particle swarm optimization |
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| HUANG Ping and LI Binglei.Mine fire rescue based on weighted aggregation particle swarm optimization[J].Journal of Fuzhou University(Natural Science Edition),2016,44(6):868-873. |
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| Authors: | HUANG Ping and LI Binglei |
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| Institution: | College of Environment and Resources,Fuzhou University,Fuzhou,College of Zijin Mining,Fuzhou University,Fuzhou,College of Automobile and Transportation,Qingdao University of Technology,Qingdao,College of Environment and Resources,Fuzhou University,Fuzhou |
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| Abstract: | This paper presents an minimum center particle swarm optimization based on the weighted aggregation, which improves the hunting zone and goal weight of particle swarm optimization algorithm. The simulation results show that this algorithm has fast speed and high precision in typical standard function test training. Its performance in the mine fire rescue optimization route model is tested and analyzed. It was found that the method helps to avoid premature convergence, and enhance the global search capability, while increasing the accuracy of Pareto optimal solution, which can provide important technical support for mine fire accident rescue decisions. |
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| Keywords: | weighted aggregation minimum center particle swarm optimization mine fire rescue |
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