计算机科学 ›› 2017, Vol. 44 ›› Issue (9): 93-98.doi: 10.11896/j.issn.1002-137X.2017.09.019
• CRSSC-CWI-CGrC 2016 • 上一篇 下一篇
张新明,涂强,康强,程金凤
ZHANG Xin-ming, TU Qiang, KANG Qiang and CHENG Jin-feng
摘要: 灰狼优化(Grey Wolf Optimization,GWO)算法是近年被提出的一种新型智能优化算法,具有收敛速度快和优化精度高的特点,但对于一些复杂优化问题易陷入局部最优。差分进化(Differential Evolution,DE)算法的全局搜索能力强,但其性能对参数敏感,且局部搜索能力不足。为了发挥二者各自的优点并弥补存在的缺陷,提出了一种灰狼优化与差分进化的混合优化算法。首先使用嵌入趋优算子的GWO算法搜索,以便在更短的过程中获得更高的优化精度和更快的收敛速度;然后采用自适应调节参数的差分进化策略来进一步提高算法对复杂优化函数的寻优性能,从而获得一种高性能的混合优化算法,以便能更高效地解决各种函数优化问题。对12个高维函数的优化结果表明,与标准GWO,ACS,DMPSO及SinDE相比,新的混合优化算法不仅具有更好的收敛速度和优化性能,而且具有更好的普适性,更适用于解决各种函数优化问题。
[1] JIANG J Q,BO Y L,SONG C Y,et al.Hybrid algorithm based on particle swarm optimization and artifical fish swarm algorithm [J].Lecture Notes in Computer Science,2012,7:607-614. [2] LI Q,ZHAO C,CHEN P,et al.Improved ant colony optimization based on particle swarm optimization [J].Control and Decision,2013,28(6):873-878.(in Chinese) 李擎,张超,陈鹏,等.一种基于粒子群参数优化的改进蚁群算法 [J].控制与决策,2013,28(6):873-878. [3] BEHNAMIAN J,ZANDIEH M,FATEMI G S M T.Parallelmachine scheduling problems with sequence-dependent setup times using an ACO,SA and VNS hybrid algorithm [J].Expert Systems with Applicatoins,2009,6(6):9637-9644. [4] ZHANG X M,TU Q,YIN X X,et al.Chemotaxis operator embedded particle swarm optimization algorithm and its application to multilevel thresholding[J].Computer Science,2016,43(2):311-315.(in Chinese) 张新明,涂强,尹欣欣,等.嵌入趋化算子的PSO算法及其在多阈值分割中的应用[J].计算机科学,2016,43(2):311-315. [5] ZHOU Y L.Hybrid strategy of intelligent optimization algo-rithm:analysis,design and model[J].Application Research of Computers,2010,7(12):4423-4426.(in Chinese) 周雅兰.智能优化算法的混合策略分析、设计和建模[J].计算机应用研究,2010,7(12):4423-4426. [6] MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer [J].Advances in Engineering Software,2014,69(3):46-61. [7] MIRJALILI S.How effective is the grey wolf optimizer in trai-ning multi-layer perceptrons[J].Applied Intelligence,2015,43(1):150-161. [8] SULAIMAN M H,MUSTAFFA Z,MOHAMED M R,et al.Using the grey wolf optimizer for solving optimal reactive power dispatch problem[J].Applied Soft Computing,2015,32:286-292. [9] 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. [10] LONG W,ZHAO D Q,XU S J.Improved grey wolf optimization algorithm for constrained optimization problem[J].Journal of Computer Applications,2015,35(9):2590-2595.(in Chinese) 龙文,赵东泉,徐松金.求解约束优化问题的改进灰狼优化算法[J].计算机应用,2015,5(9):2590-2595. [11] GAO Y L,LIU J M.Dynamic differential evolution algorithm with random mutation[J].Journal of Computer Applications,2009,29(10):2719-2722.(in Chinese) 高岳林,刘俊梅.一种带有随机变异的动态差分进化算法[J].计算机应用,2009,9(10):2719-2722. [12] PILOT M,BRANICKI W,JEDRZEJEWSKI W,et al.Phylogeographic history of grey wolves in Europe [J].Bmc Evolutionary Biology,2010,10(1685):1-11. [13] MURO C,ESCOBEDO R,SPECTOR L,et al.Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations [J].Behavioural Processes,2011,88(3):192-197. [14] DRAA A,BOUZOUBIA S,BOUKHALFA I.A sinusoidal differential evolution algorithm for numerical optimization[J].Applied Soft Computing,2015,27:99-126. [15] LI J Y,WANG Y,LI C L.Particle Swarm Optimization Algorithm with Double-Flight Modes[J].Pattern Recognition and Artificial Intelligence,2014,27(6):533-539.(in Chinese) 李景洋,王勇,李春雷.采用双模飞行的粒子群优化算法[J].模式识别与人工智能,2014,7(6):533-539. [16] NAIK M,NATH M R,WUNNAVA A,et al.A new adaptive Cuckoo search algorithm[C]∥2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS).IEEE,2015:1-5. [17] HAKLI H,UGUZ H.A novel swarm optimization algorithmwith Levy flight[J].Applied Soft Computing,2014,23(5):333-345. [18] TANWEER M R,SURESH S,SUNDARARAJAN N.Self regu-lating particle swarm optimization algorithm[J].Information Sciences,2015,294:182-202. |
No related articles found! |
|