[1] QU B, SUGANTHAN P N, DAS S. A distance-based locally informed particle swarm model for multimodal optimization [J]. IEEE Transactions on Evolutionary Computation, 2013,17(3):387-402. [2] XU S, LONG W. Differential evolution algorithm with dynamically adjusting number of subpopulation individuals [J]. Journal of Computer Applications, 2011,31(11):3101-3103.(徐松金,龙文.动态调整子种群个体的差分进化算法[J]. 计算机应用,2011,31(11):3101-3103.) [3] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimization [J]. Advances in Engineering Software, 2014,69(7):46-61. [4] MADADI A, MOTLAGH M M. Optimal control of DC motor using grey wolf optimizer algorithm [J]. Technical Journal of Engineering and Applied Science, 2014,4(4):373-379. [5] EMARY E, ZAWBAA H M, GROSAN C, et al. Feature subset selection approach by gray-wolf optimization [C]//Proceedings of the International Afro-European Conference on Industrial Advancement. Berlin:Springer, 2014:1-13. [6] MIRJALILI S. How effective is the grey wolf optimizer in training multilayer perceptrons [J]. Applied Intelligence, 2015,42(4):608-619. [7] EI-GAAFARY A A M, MOHAMED Y S, HEMEIDA A M, et al. Grey wolf optimization for multi input multi output system [J]. Universal Journal of Communications and Networks, 2015,3(1):1-6. [8] SONG H M, SULAIMAN M H, MOHAMED M R. An application of grey wolf optimizer for solving combined economic emission dispatch problems [J]. International Review on Modelling and Simulations, 2014,7(5):838-844. [9] WU L, WANG Y, ZHOU S, et al. Differential evolution for nonlinear constrained optimization using non-stationary multi-stage assignment penalty function [J]. Systems Engineering Theory and Practice, 2007,27(3):128-133.(吴亮红,王耀南,周少武,等.采用非固定多段映射罚函数的非线性约束优化差分进化算法[J].系统工程理论与实践,2007,27(3):127-133.) [10] PARSOPOULOS K E, VRAHATIS M N. Particle swarm optimization method for constrained optimization problems [EB/OL]. [2015-01-03]. http://www.researchgate.net/publication/2527227_Particle_swarm_optimization_method_for_constrained_optimization_problems. [11] HAUPT R, HAUPT S. Practical genetic algorithm [M]. New York: John Wiley & Sons, 2004. [12] ZHANG L, ZHANG B. Good point set based genetic algorithm [J]. Chinese Journal of Computers, 2001,24(9):917-922.(张铃,张钹.佳点集遗传算法[J].计算机学报,2001,24(9):917-922.) [13] POWELL M J D. A fast algorithm for nonlinearly constrained optimization calculations [C]//Numerical Analysis, Lecture Notes in Mathematics 630. Berlin: Springer, 1978: 144-157. [14] WU J, ZHANG J, CHEN H. Particle swarm optimization algorithm combination with Powell search method [J]. Control and Decision, 2012,27(3):343-348.(吴建辉,章兢,陈红安.融合Powell搜索法的粒子群优化算法[J].控制与决策,2012,27(3):343-348.) [15] AMIRJANOV A. The development of a changing range genetic algorithm [J]. Computer Methods in Applied Mechanics and Engineering, 2006,195(19/20/21/22):2495-2508. [16] BOUSSAID I, CHATTERJEE A, SIARRY P, et al. Biogeography-based optimization for constrained optimization problems [J]. Computers and Operations Research, 2012,39(12):3293-3304. [17] LU H Y, CHEN W Q. Self-adaptive velocity particle swarm optimization for solving constrained optimization problems [J]. Journal of Global Optimization, 2008,41(3):427-445. [18] GANDOMI A H, YANG X S, ALAVI A H, et al. Bat algorithm for constrained optimization tasks [J]. Neural Computing and Applications, 2013,22(6):1239-1255. [19] MEZURA M E, COELLO C A, MORALES E. Simple feasibility rules and differential evolution for constrained optimization [M]. Berlin: Springer, 2004:707-716. [20] KARABOGA D, AKAY B. A modified artificial bee colony algorithm for constrained optimization problems [J]. Applied Soft Computing, 2011,11(3):3021-3031. [21] LONG W, LIANG X M, HUANG Y F, et al. An effective hybrid cuckoo search algorithm for global constrained optimization [J]. Neural Computing and Applications, 2014,25(3/4):911-926. |