—— 西南科技大学理学院专业数学教研室老师，从事教学和科研工作。
——访问澳大利亚科廷大学建筑信息管理研究中心，从事数学建模和数值计算研究工作。
1. 混合全局最优化算法及其应用：混合全局最优化算法是结合确定性算法和启发式算法的一种新型的求解全局最优化问题的算法，它保留了确定性算法的局部搜索能力，同时又引入了启发式算法的全局搜索特性，是近年来非常热门的一种全局最优化算法。其主要应用领域有通讯中的能耗优化，建筑学中的建筑质量监控，图像处理中的滤波器设计，等等。
2. 多目标规划启发式算法及其应用： 多目标规划是工业应用中非常常见的优化问题，所以多目标规划问题的求解也是非常重要的一个研究方向。多目标规划启发式算法主要是应用基于种群的优化算法，如基因算法、粒子群算法、进化策略、蚁群算法等等来求解多目标规划问题。其主要应用领域有无线移动通讯中的点对点通讯能耗优化、桥梁质量监控，经济学中的投资策略，等等。
3. 机器学习算法及应用：机器学习算法是大数据、人工智能、图像处理、模式识别、智慧城市等现代流行科技的基础。机器学习算法主要分为监督学习、非监督学习、强化学习三个部分。机器学习算法可以应用于日常生活、工业生产、技术开发等各个方面，在未来十年有极大的发展空间，是众多现代科技所依赖的核心技术。
第一作者论文
[1] Qiang Long. The application of genetic algorithm in solving nonsmooth optimization problems. Journal of Chongqing Normal University ( Natural Science Edition), 2013 (01), 12-15.
[2] Qiang Long, Changzhi Wu. A quasisecant method for solving a system of nonsmooth equations. Computers and Mathematics with Applications, 66(2013), 419-431. (SCI)
[3] Qiang Long, Changzhi Wu. A hybrid method combining genetic algorithm and Hooke-Jeeves method for constrained global optimization. Journal of Industrial and Management Optimization, 10(4), (2014), 1279-1296, (SCI).
[4] Qiang Long: A constraint handling technique for constrained multi-objective genetic algorithm. Swarm and Evolutionary Computation, 15 (2014), 66-79. (EI)
[5] Qiang Long, Changzhi Wu. A system of nonsmooth equations solver based upon subgradient method. Applied Mathematics and Computation, 251 (2015), 284-299. (SCI)
[6] Qiang Long, Changzhi Wu, Tingwen Huang. A genetic algorithm for unconstrained multi-objective optimization. Swam and Evolutionary Computation, 22 (2015), 1-14. (SCI)
[7] Qiang Long, Changzhi Wu, Xiangyu Wang, Jueyou Li. A multi-objective genetic algorithm based on a discrete selection procedure. Mathematical Problems in Engineering, 23 (2015), 1-17. (SCI)
[8] Qiang Long, Changzhi Wu, Xiangyu Wang, Zhiyou Wu. A modified quasisecant method for global optimization. Applied Mathematical Modelling, 51 (2017) 21-37. (SCI)
[9] Qiang Long, Xiaohua Liu. Two dimensional code encryption algorithm based on asymmetric Cryptosystem. Journal of Chongqing Normal University (Natural Science Edition), 34(2), (2017), 76-80.
参与论文
[1] Changzhi Wu, Chaojie Li, Qiang Long. A DC programming approach for sensor network localization with uncertainties in anchor positions. Journal of Industrial and Management Optimization, 10(3) (2014), 817-826. (SCI).
[2] Jueyou Li, Changzhi Wu, Zhiyou Wu, Qiang Long. Gradient-free method for nonsmooth distributed optimization. Journal of Global Optimization, 61(2), (2015), 325-340. (SCI)
[3] Jue you Li, Zhiyou Wu, Qiang Long. An objective penalty function approach for solving constrained minimax problems. Journal of Operations Research Society of China, 2 (2014), 93-108.
[4] Jueyou Li, Changzhi Wu, Qiang Long, Xiangyu Wang. An inexact dual fast gradient-projection method for separable convex optimization with linear coupled constraints. Journal of Optimization Theory and Application, 168(1), (2015), 1-19.
[5] Jueyou Li, Zhiyou Wu, Changzhi Wu, Qiang Long, Xiangyu Wang, Jae-Myung Lee, Kwang-Hyo Jung. A fast dual gradient method for separable convex optimization via smoothing. Pacific Journal of Optimization, 12(2), (2016), 289-305.
会议论文
[1] Qiang Long, Junjian Huang. A new hybrid method combining genetic algorithm and coordinate search method. IEEE the fifth international conference on advanced computational intelligence, 2012. (IEEE)
[2] Qiang Long, Changzhi Wu. A Nonsmooth Equation System Solver Based on Subgradient Method. 22nd International Conference on Digital Signal Processing, London, United Kingdom.
[3] Qiang Long, Changzhi Wu, Xiangyu Wang. A nonlinear scalarization method for multiobjective optimization problems. International Conference on Innovative Production and Construction (IPC 2017) , Perth, Australia.
深度学习研究小组：
该研究小组由指导教师、研究生和优秀本科生组成。主要的研究方向为机器学习的理论与算法、深度学习、深度强化学习。应用方向有图像处理、自然语言处理、数字音频处理、基于深度强化学习的交通控制问题，等。
深度学习研究小组欢迎具有良好数学基础，熟悉python编程的优秀本科生加入。