|
|
Dynamic Double Subgroups Cooperative Fruit Fly Optimization Algorithm |
HAN Jun-Ying,LIU Cheng-Zhong,WANG Lian-Guo |
College of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070 |
|
|
Abstract In order to overcome the demerits of basic Fruit Fly Optimization Algorithm(FOA),such as low convergence precision and easily relapsing into local optimum,a dynamic double subgroup cooperative Fruit Fly Optimization Algorithm (DDSCFOA) is presented. Firstly,the whole group is dynamically divided into advanced subgroup and backward subgroup according to its own evolutionary level. Secondly,a finely local searching is made for advanced subgroup in the neighborhood of local optimum with Chaos algorithm,and a global search with FOA is made for backward subgroup,so that the whole group keeps in good balance between the global searching ability and local searching ability. Finally,two subgroups exchange information by updating the overall optimum and recombining the subgroups. DDSCFOA can jump out of local optimum and avoid falling into local optimum. The experimental results show that the strategy of dynamic double subgroup cooperative evolution is effective and feasible,DDSCFOA is much better than basic FOA in convergence velocity and convergence precision.
|
Received: 27 February 2013
|
|
|
|
|
[1] Pan W T. A New Fruit Fly Optimization Algorithm: Taking the Financial Distress Model as An Example.Knowledge-Based Systems, 2012, 26(2): 69-74 [2] Pan W T. Fruit Fly Optimization Algorithm. Taipei, China: Tsang Hai Book Publishing Co., 2011: 10-12 (in Chinese) (潘文超.果蝇最佳化演算法.台北,中国:沧海书局, 2011: 10-12) [3] Pan W T. Using Fruit Fly Optimization Algorithm Optimized General Regression Neural Network to Construct the Operating Performance of Enterprises Model. Journal of Taiyuan University of Technology: Social Sciences Edition, 2011, 29(4): 1-5 (in Chinese) (潘文超.应用果蝇优化算法优化广义回归神经网络进行企业经营绩效评估.太原理工大学学报:社会科学版, 2011, 29(4): 1-5) [4] Tian Yuan, Zhang Bide, Liu Daiwei, et al. Condition Trend Prediction of Hydroelectric Sets Based on Fruit Fly Optimization Algorithm Optimized GRNN. Water Resources and Power, 2012, 30(12): 127-129 (in Chinese) (田 源,张彼德,刘代伟,等.基于果蝇优化算法的GRNN水电机组状态趋势预测.水电能源科学, 2012, 30(12): 127-129) [5] Wang Xin, Du Kang, Qin Bin, et al. Drying Rate Modeling Based on FOALSSVR.Control Engineering of China, 2012, 19(4): 630-633 (in Chinese) (王 欣,杜 康,秦 斌,等.基于果蝇优化算法的LSSVR干燥速率建模.控制工程, 2012, 19(4): 630-633) [6] Wang Xuegang, Zou Zoujian. Identification of Ship Manoeuvring Response Model Based on Fruit Fly Optimization Algorithm. Journal of Dalian Maritime University, 2012, 38(3): 1-5 (in Chinese) (王雪刚,邹早建.基于果蝇优化算法的船舶操纵响应模型的辨识.大连海事大学学报, 2012, 38(3): 1-5) [7] Xiao Zhengan. Application of Improved FOA on Audio Signal Blind Separation [EB/OL]. [2012-06-05]. http://www.cnki.net/kcms/detail/11.2127.TP.20120605.1519.002.html (in Chinese) (肖正安.改进FOA算法在语音信号盲分离中的应用 [EB/OL]. [2012-06-05]. http://www.cnki.net/kcms/detail/11.2127.TP.20120605.1519.002.html [8] Hu Wang, Li Zhishu. A Simpler and More Effective Particle Swarm Optimization Algorithm. Journal of Software, 2007, 18(4): 861-868 (in Chinese) (胡 旺,李志蜀.一种更简化而高效的粒子群优化算法.软件学报, 2007, 18(4): 861-868) [9] Wang Lianguo, Hong Yi, Shi Qiuhong. Global Edition Artificial Fish Swarm Algorithm. Journal of System Simulation, 2009, 21(23): 7483-7486 (in Chinese) (王联国,洪 毅,施秋红.全局版人工鱼群算法.系统仿真学报, 2009, 21(23): 7483-7486) [10] Ma D W, Guo X W, Deng L. Ammunition Scheduling of Carrier-Based Aircraft Based on Modified Ant Colony Algorithm. Journal of System Simulation, 2012, 24(6): 1207-1211 (in Chinese) (马登武,郭小威,邓 力.基于改进蚁群算法的舰载机弹药调度.系统仿真学报, 2012, 24(6): 1207-1211) [11] Hu Jie. Research on the Modified Bacteria Foraging Optimization Algorithm and Its Application. Ph.D Dissertation. Wuhan, China: Wuhan University of Technology, 2012 (in Chinese) (胡 洁.细菌觅食优化算法的改进及应用研究.博士学位论文.武汉:武汉理工大学, 2012) [12] Lǚ Zhensu, Hou Zhirong. Particle Swarm Optimization with Adaptive Mutation. Acta Electronica Sinica, 2004, 32(3): 416-420 (in Chinese) (吕振肃,侯志荣.自适应变异的粒子群优化算法.电子学报, 2004, 32(3): 416-420) [13] LI Li, Niu Ben. Particle Swarm Optimization. Beijing, China: Metallurgical Industry Press, 2009 (in Chinese) (李 丽,牛 奔.粒子群优化算法.北京:冶金工业出版社, 2009) [14] Lin Chuan, Feng Quanyuan. New Adaptive Particle Swarm Optimization Algorithm. Computer Engineering, 2008, 34(7): 181-183 (in Chinese) (林 川,冯全源.一种新的自适应粒子群优化算法.计算机工程, 2008, 34(7): 181-183) [15] Angeline P J. Evolutionary Optimization versus Particles Swarm Optimization: Philosophy and Performance Differences // Proc of the 7th International Conference on Evolutionary Progranming. San Diego, USA, 1998: 601-610 [16] Clerc M, Kennedy J. The Particle Swarm-Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Trans on Evolutionary Computation, 2002, 6(1): 58-73 [17] Eberhart R C, Shi Y. Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization // Proc of the Congress on Evolutionary Computation. La Jolla, USA, 2000, I: 84-88 [18] Li M S, Ji T Y, Tang W J, et al. Bacterial Foraging Algorithm with Varying Population. BioSystems, 2010, 100(3): 185-197 [19] Jin Qibing, Zhao Zhenxing, Su Xianjing, et al. PSO Algorithm with High Speed Convergence Based on Particle Health. Journal of Chemical Industry and Engineering, 2011, 62(8): 2328-2333 (in Chinese) (靳其兵,赵振兴,苏晓静,等.基于粒子健康度的快速收敛粒子群优化算法.化工学报, 2011, 62(8): 2328-2333) |
|
|
|