基于ICGSO算法的瓦斯突出预测研究  

Research of gas outburst prediction model based on ICGSO algorithm

作　　者：高敏[1,2] 周孟然[2] 张烁[2] 

机构地区：[1]淮南职业技术学院信息与电气工程系 [2]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《淮南师范学院学报》2013年第3期32-34,共3页Journal of Huainan Normal University

基　　金：国家自然科学基金和神华集团有限公司联合资助(51174258);安徽省自然科学基金(11040606M103);安徽高校省级科研项目(KJ2011B162);淮南职业技术学院科研项目(HKJ10-3)

摘　　要：针对萤火虫群优化(GSO)算法在解决全局优化问题时出现的易陷入局部最优、收敛速度慢、求解精度不高等问题,提出一种改进的混沌萤火虫群优化(ICGSO)算法,修改了GSO算法动态决策域半径更新公式,并采用自适应动态步长,引入混沌优化算法提高局部搜索能力。实验结果表明将ICGSO算法应用于建立在神经网络预测模型上的瓦斯突出预测中的有效性。Glowworm Swarm Optimization（GSO） algorithm has some drawbacks such as getting in local minima,convergence rate is slow and the precision of the solution is not high.In order to solve this problem,an improved Chaos Glowworm Swarm Optimization algorithm was presented in this paper.The new algorithm with a new formula to replace the standard dynamic decision domain radius updating formula of GSO algorithm adopted adaptive dynamic step length,and chaotic optimization algorithm is introduced to improve the ability of the local search.The new algorithm is introduced into the construction of gas outburst prediction model,experimental results prove the validity of the model.

关 键 词：混沌优化 人工萤火虫群优化 瓦斯突出预测 自适应 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]