[1] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of the IEEE International Conference on Neural Networks. Piscataway, NJ:IEEE, 1995, 4:1942-1948. [2] 吕莉,赵嘉,孙辉.具有反向学习和自适应逃逸功能的粒子群优化算法[J].计算机应用,2015,35(5):1336-1341.(LYU L, ZHAO J, SUN H. Particle swarm optimization algorithm using opposition-based learning and adaption escape[J]. Journal of Computer Applications, 2015, 35(5):1336-1341.) [3] 汤可宗,肖绚,贾建华,等.基于离散式多样性评价策略的自适应粒子群优化算法[J].南京理工大学学报,2013,37(3):344-349.(TANG K Z, XIAO X, JIA J H, et al. Adaptive particle swarm optimization algorithm based on discrete estimate strategy of diversity[J]. Journal of Nanjing University of Science and Technology, 2013, 37(3):344-349.) [4] 罗磊,陈恳,杜峰坡,等.基于改进型粒子群算法的曲面匹配与位姿获取[J].清华大学学报(自然科学版),2015,55(10):1061-1066.(LUO L, CHEN K, DU F P, et al. Surface fitting and position-pose measurements based on an improved SA-PSO algorithm[J]. Journal of Tsinghua University (Science and Technology), 2015, 55(10):1061-1066.) [5] LU C, SHENG W, HAN Y, et al. Phase-only pattern synthesis based on gradient-descent optimization[J]. Journal of Systems Engineering and Electronics, 2016, 27(2):297-307. [6] 许少华,宋美玲,许辰,等.一种基于混合误差梯度下降算法的过程神经网络训练[J].东北石油大学学报,2014,38(4):92-96.(XU S H, SONG M L, XU C, et al. Training algorithm of process neural networks based on hybrid error gradient descent[J]. Journal of Northeast Petroleum University, 2014, 38(4):92-96.) [7] 韩文花,徐俊,沈晓晖,等.自学习粒子群与梯度下降混杂的漏磁反演方法[J].火力与指挥控制,2015,40(1):88-91.(HAN W H, XU J, SHEN X H, et al. Hybrid of self-learning particle swarm optimization and gradient descent based magnetic flux leakage lnversion[J]. Fire Control & Command Control, 2015, 40(1):88-91.) [8] 汤可宗,李慧颖,李娟,等.一种求解复杂优化问题的改进粒子群优化算法[J].南京理工大学学报,2015,39(4):386-391.(TANG K Z, LI H Y, LI J, et al. Improved particle swarm optimization algorithm for solving complex optimization problems[J]. Journal of Nanjing University of Science and Technology, 2015, 39(4):386-391.) [9] RIGET R, VESTERSTRØM J S. A diversity-guided particle swarm optimizer-the ARPSO[R]. Aarhus, Denmark:University of Aarhus, 2002. [10] 汤可宗.遗传算法与粒子群优化算法的改进及应用研究[D].南京:南京理工大学,2011.(TANG K Z. Improvement and application of genetic algorithm and particle swarm algorithm research[D]. Nanjing:Nanjing University of Technology Institute of Computer Science and Engineering, 2011.) [11] 周利军,彭卫,曾小强,等.基于杂交变异的动态粒子群优化算法[J].计算机科学,2013,40(11A):143-146.(ZHOU L J, PENG W, ZENG X Q, et al. Dynamic particle swarm optimization based on hybrid variable[J]. Computer Science, 2013, 40(11A):143-146.) [12] 白俊强,尹戈玲,孙智伟,等.基于二阶振荡及自然选择的随机权重混合粒子群算法[J].控制与决策,2012,27(10):1459-1464.(BAI J Q, YIN G L, SUN Z W, et al. Random weighted hybrid particle swarm optimization algorithm based on second order oscillation and natural selection[J]. Control and Decision, 2012, 27(10):1459-1464.) [13] 魏建香,孙越泓,苏新宁.一种基于免疫选择的粒子群优化算法[J]. 南京大学学报(自然科学版),2010,46(1):1-9.(WEI J X, SUN Y H, SU X N. A novel particle swarm optimization based on immune selection[J]. Journal of Nanjing University (Natural Sciences), 2010,46(1):1-9.) [14] ZHAN Z H, ZHANG J, LI Y, et al. Adaptive particle swarm optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics, 2009, 39(6):1362-1381. [15] 温正.精通MATLAB智能算法[M].北京:清华大学出版社,2015:106-192. |