DBC优化算法在瓦斯突出预测中的应用

Application of DBC Optimization Algorithm in Gas Outburst Prediction

针对煤矿瓦斯突出因素的复杂性,提出一种新的智能优化算法-双混沌搜索蜂群(DBC)优化算法,应用于煤矿瓦斯突出的预测中.DBC优化算法对人工蜂群算法进行有效改进,在人工蜂群算法的基础上,将混沌优化机制引入蜂群的寻优过程中,利用混沌序列初始化食物源,以提高食物源的质量,防止算法的早熟收敛;同时利用混沌搜索机制进行局部搜索,以改善蜂群的区域搜索能力,解决算法易陷入局部极小值的问题.最后,利用DBC对MLPNN进行训练,建立瓦斯突出预测模型.实验结果表明,该方法对瓦斯突出具有较好的预测结果.

Aiming at the characteristics of the gas outburst disasters, the double chaos search bee colony （DBC） algorithm for gas outburst prediction is proposed. DBC algorithm improves ABC by introducing the chaos optimization idea in the process of population evolutionary. The chaos method generates initial population in order to improve the quality of the food source, to prevent premature convergence of the algorithm. Meanwhile, chaos idea is introduced to the bee swarm local search. Through the method, this drawback of getting in local minima is resolved. Then DBC is merged with MLPNN to construct a gas outburst prediction model by using DBC to train the weights and thresholds. The predicted results show that the method is accurate.