【作者】 郭一楠； 王春； 杨继超；

【Author】 Guo Yinan Wang Chun Yang Jichao (School of Information and Electrical Engineering,China University of Mining and Technology,Xuzhou 221116,China)

【机构】 中国矿业大学信息与电气工程学院；

【摘要】 以矿井通风网络的总功率最小为目标建立了矿井通风网络的非线性优化数学模型.针对模型中风量平衡和风压平衡的约束条件,采用外点罚函数法将其转化模型目标中的惩罚项.面向约束转化后模型,采用文化粒子群优化算法实现寻优.该算法在种群空间采用粒子群优化算法实现粒子进化;通过构建上层信度空间来挖掘进化过程中优势粒子的隐含信息,并以知识形式加以保存;最终通过影响函数,使知识作用于种群空间实现对粒子进化的引导.面向一个典型通风网络结构与其他智能优化方法优化结果比较可知,基于该算法获得的调风方案具有较小的总能耗,且能满足通风网络的需风量需求.更多还原

【Abstract】 Taking the minimum total power of the mine ventilation network as the objective,a nonlinear mathematical model for optimizing the structure of the mine ventilation network is established. Two constraints,which are the balance conditions for the air quantity and the air pressure,are converted to the penalty part in the model by exterior point penalty function methods.In order to optimize the converted model,the cultural particle swarm optimization algorithm is adopted.In the algorithm, particle swarm optimization is introduced to realize the essential evolution process in the population space.The belief space is constructed to extract effective implicit information during the evolution process.The information is stored as knowledge and used to guide the particles’ evolution in population space by the influence function.Taking a typical mine ventilation network as an example, simulation results indicate that compared with other intelligent optimization methods,the optimal scheme obtained by the cultural particle swarm optimization algorithm has the minimum total energy-consuming, which satisfies the air requirements of the mine ventilation network.更多还原