[1] KENNEDY J, EBERHART R C. Particle swam optimization[C]//Proceedings of the 1995 IEEE International Conference on Neural Networks. Piscataway, NJ:IEEE, 1995, 4:1942-1948. [2] SHI Y, EBERHART R C. A modified swarm optimizer[C]//Proceedings of the IEEE Congress on Evolutionary Computation. Piscataway, NJ:IEEE,1998:69-73. [3] 卢锦玲, 苗雨阳, 张成相, 等.基于改进多目标粒子群算法的含风电场电力系统优化调度[J].电力系统保护与控制, 2013, 41(17):25-31.(LU J L, MIAO Y Y, ZHANG C X, et al. Power system optimal dispatch considering wind farms based on improved multi-objective particle swarm algorithm[J]. Power System Protection and Control, 2013, 41(17):25-31.) [4] 纪震, 廖惠连, 吴青华. 粒子群算法及应用[M]. 北京:科学出版社, 2009:72-87.(JI Z, LIAO H L, WU Q H. Particle Swam Algorithm and Application[M]. Beijing:Science Press, 2009:72-87.) [5] 吴聪, 杨建辉.基于改进粒子群算法的物流配送车辆调度优化[J]. 计算机工程与应用, 2015, 51(13):259-262.(WU C, YANG J H. Vehicle routing problem of logistics distribution based on improved particle swarm optimization algorithm[J]. Computer Engineering and Applications, 2015, 51(13):259-262.) [6] 敖永才, 师奕兵, 张伟, 等.自适应惯性权重的改进粒子群算法[J].电子科技大学学报, 2014, 43(6):874-879.(AO Y C, SHI Y B, ZHANG W, et al. Improved particle swarm optimization with adaptive inertia weight[J]. Journal of University of Electronic Science and Technology of China, 2014, 43(6):874-879.) [7] 赵远东, 方正华.基于权重函数学习因子的粒子群算法[J].计算机应用, 2013, 33(8):2265-2268.(ZHAO Y D, FANG Z H. Particle swarm optimization algorithm with weight function's learning factor[J]. Journal of Computer Applications, 2013, 33(8):2265-2268.) [8] MENDES R, KENNEDY J, NEVES J. The fully informed particle swarm:simpler, maybe better[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3):204-210. [9] PARSOPOULOS K E, VRAHATIS M N. UPSO——a unified particle swarm optimization scheme[C]//Proceedings of the International Conference of Computational Methods in Sciences and Engineering. Berlin:Springer, 2004, 1:868-873. [10] 占栋辉, 卢厚清, 郝文宁, 等. 一种高斯反向学习粒子群优化算法[J].小型微型计算机系统, 2015, 36 (5):1064-1068.(ZHAN D H, LU H Q, HAO W N, et al. Particle swarm optimization algorithm with Gaussian opposition-based learning[J]. Journal of Chinese Computer Systems, 2015, 36(5):1064-1068.) [11] 于海平, 刘会超, 吴志健.基于模拟退火的自适应粒子群优化算法的改进策略[J].计算机应用究, 2012, 29(12):4448-4450.(YU H P, LIU H C, WU Z J. Strategy of adaptive simulated annealing particle swarm optimization algorithm[J]. Application Research of Computers, 2012, 29(12):4448-4450.) [12] 金敏, 鲁华祥.一种遗传算法与粒子群优化的多子群分层混合算法[J].控制理论与应用, 2013, 30(10):1231-1238.(JIN M, LU H X. A multi-subgroup hierarchical hybrid of genetic algorithm and particle swarm optimization[J]. Control Theory & Applications, 2013, 30(10):1231-1238.) [13] LIANG J, QIN K, SUGANTHAN P N, et al. Comprehensive learning particle swarm optimizer for global optimization of multi-modal function[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3):281-295. [14] 阳春华, 谷丽姗, 桂卫华.自适应变异的粒子群优化算法[J].计算机工程, 2008, 34(16):188-190.(YANG C H, GU L S, GUI W H. Particle swarm optimization algorithm with adaptive mutation[J]. Computer Engineering, 2008, 34(16):188-190.) [15] ARULAMPALAM M S, MASKELL S, GORDON N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Transactions on Signal Processing, 2002, 50(2):174-188. [16] DOUCET A, DE FREITAS N, GORDON N. Sequential Monte Carlo Methods in Practice[M]. Berlin:Springer, 2001:3-13. [17] GORDON N J, SALMOND D J, SMITH A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation[J]. IEEE Proceedings F (Radar and Signal Processing), 1993, 140(2):107-113. [18] 胡士强, 敬忠良.粒子滤波算法综述[J].控制与决策, 2005, 20(4):361-365.(HU S Q, JING Z L. Overview of particle filter algorithm[J]. Control and Decision, 2005, 20(4):361-365.) [19] HOL J D, SCHON T B, GUSTAFSSON F. On re-sampling algorithms for particle filters[C]//Proceedings of the 2006 IEEE Nonlinear Statistical Signal Processing Workshop. Piscataway, NJ:IEEE, 2006:79-82. [20] DOUCET A, GODSILL S, ANDRIEU C. On sequential Monte Carlo sampling method for Bayesian filtering[J]. Statistics and Computing, 2000, 10(3):197-208. [21] KENNEDY J, MENDES R. Population structure and particle swarm performance[C]//CEC 2002:Proceedings of the 2002 Congress on Evolutionary Computation. Piscataway, NJ:IEEE, 2002:1671-1676. [22] PERAM T, VEERAMACHANENI K, MOHAN C K. Fitness-distance-ratio based particle swarm optimization[C]//SIS 2003:Proceedings of the 2003 IEEE Swarm Intelligence Symposium. Piscataway, NJ:IEEE, 2003:174-181. |