基于遗传-细菌觅食组合算法的非线性模型优化

doi:10.16180/j.cnki.issn1007-7820.2019.05.004

Abstract

Abstract:

A combination of genetic algorithm and bacterial foraging optimization algorithm (GA-BFO) was presented to solve the nonlinear model optimization problems. Firstly, GA-BFO employed genetic algorithm to conduct global search and reduce the exploiting range of global optimum. Secondly, GA-BFO employed the bacterial foraging optimization algorithm to conduct local search in the reduced range. This combined search strategy could both enhance the convergence of GA-BFO and balance global search and local search. Three typical nonlinear function models including unimodal, multi-peak and complex multi-peak models were used to test the performance of the proposed algorithm. Experimental results showed that GA-BFO could achieve 30% and 50% precision improvements for GA and BFO respectively. Above results indicated the combined optimization approach had faster convergence speed and higher calculation precision, and it was more suitable for solving large-scale nonlinear problems with multiple optima.

Key words: genetic algorithm, bacterial foraging optimization, combination algorithm, global search, local search, nonlinear model

CLC Number:

TP18

LI Yapin,ZOU Dexuan,DUAN Na. Optimization of Nonlinear Model Based on GA-BFO Combination Algorithm[J].Electronic Science and Technology, 2019, 32(5): 16-20.

Figures/Tables 10

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References 16

[1]	Passino K M . Biomimicry of bacterial foraging for distributed optimization and control[J]. IEEE Control Systems Magazine, 2002(22):52-67.
[2]	邹彦艳, 吴宇轩, 宋振宇 , 等. 基于改进遗传算法的模糊控制器设计[J]. 自动化技术与应用, 2013,32(11):6-10.
	Zou Yanyan, Wu Yuxuan, Song Zhenyu , et al. Design of fuzzy controllers based on improved genetic algorithms[J]. Techniques of Automation and Applications, 2013,32(11):6-10.
[3]	王婷, 符小卫, 高晓光 . 基于改进遗传算法的异构多无人机任务分配[J]. 火力与指挥控制, 2013,38(5):37-41.
	Wang Ting, Fu Xiaowei, Gao Xiaogauang . Cooperative task a-ssignment for heterogeneous Multi-UAVs based on improved genetic algorithm[J]. Fire Control & Command Control, 2013,38(5):37-41.
[4]	祝慧灵, 计明军 . 集装箱船舶全航线配载优化模型与改进遗传算法[J]. 交通运输工程学报, 2014,14(5):59-67.
	Zhu Huiling, Ji Mingjun . Optimal model and improved genetic algorithm of containership stowage on full route[J]. J-ournal of Traffic and Transportation Engineering, 2014,14(5):59-67.
[5]	雷伟军, 程筱胜, 戴宁 , 等. 基于改进遗传算法的多模型加工路径规划[J]. 机械工程学报, 2014,50(11):153-161.
	Lei Weijun, Cheng Xiaosheng, Dai Ning , et al. Multi model machining path planning based on improved genetic algorithm[J]. Journal of Mechanical Engineering, 2014,50(11):153-161.
[6]	任乃飞, 于璐 . 基于混合遗传算法的协同制造系统调度研究[J]. 电子科技, 2016,29(6):29-33.
	Ren Naifei, Yu Lu . Research on scheduling for collaborative manufacturing system based on hybrid genetic algorithm[J]. Electronic Science and Technology, 2016,29(6):29-33.
[7]	王丽萍, 王渤权, 李传刚 , 等. 基于均匀自组织映射遗传算法的梯级水库优化调度[J]. 系统工程理论与实践, 2017,37(4):1072-1079.
	Wang Liping, Wang Boquan, Li Chuangang , et al. Optimiz-ation operation of cascade reservoirs by uniform self-organizing map-genetic algorithm[J]. Systems Engineering-Th-eory & Practice, 2017,37(4):1072-1079.
[8]	王飞平, 洪慧 . 基于改进型遗传算法的动态更新TTCAN系统技术[J]. 电子科技, 2017,30(8):40-43.
	Wang Feiping, Hong Hui . Dynamic update TTCAN system technique based on improved genetic algorithm[J]. Electronic Science and Technology, 2017,30(8):40-43.
[9]	郑史雄, 朱进波, 唐煜 , 等. 基于遗传混合算法的二维耦合颤振方法[J]. 西南交通大学学报, 2018,53(1):64-71.
	Zheng Shixiong, Zhu Jinbo, Tang Yu , et al. Two dimension-nal coupled flutter method based on genetichybrid algorithm[J]. Journal of Southwest Jiaotong University, 2018,53(1):64-71.
[10]	王振东, 陈峨霖, 胡中栋 . 混沌优化细菌觅食算法的传感器节点部署策略[J]. 传感技术学报, 2018,31(1):110-118.
	Wang Zhendong, Chen Elin, Hu Zhongdong . Sensor no-de deployment strategy of chaotic optimization of bacterial foraging algorithm[J]. Chinese Journal of Sensors and Actuators, 2018,31(1):110-118.
[11]	李珺, 党建武, 王垚 , 等. 多目标细菌觅食优化算法[J]. 计算机应用研究, 2018(7):1-2.
	LI Jun, Dang Jianwu, Wang Yao , et al. Multi-objec-tive bacteria foraging optimization algorithm[J]. Application Research of Computers, 2018(7):1-2.
[12]	张翼鹏, 葛丽娜, 王红 , 等. 基于改进细菌觅食算法的舆情热点话题发现[J]. 计算机工程与设计, 2017,38(10):2832-2837.
	Zhang Yipeng, Ge Lina, Wang Hong , et al. Cluster of p-eople opinion analysis based on improved bacterial f-oraging optimization[J]. Computer Engineering and Design, 2017,38(10):2832-2837.
[13]	马溪原, 吴耀文, 方华亮 , 等. 采用改进细菌觅食算法的风/光/储混合微电网电源优化配置[J]. 中国电机工程学报, 2011,31(25):17-25.
	Ma Xiyuan, Wu Yaowen, Fang Hualiang , et al. Optimal sizing of hybrid solar-wind distributed generation in an islanded microgrid using improved bacterial for aging algorithm[J]. Proceedings of the CSEE, 2011,31(25):17-25.
[14]	姜建国, 周佳薇, 郑迎春 , 等. 一种自适应细菌觅食优化算法[J]. 西安电子科技大学学报, 2015,42(1):75-81.
	Jiang Jianguo, Zhou Jiawei, Zheng Yingchun , et al. Adaptive bacterial foraging optimization algorithm[J]. Journal of Xidian University, 2015,42(1):75-81.
[15]	Kim D H, Cho J H. Adaptive tuning of PID controller for multivariable system using bacterial foraging based optimization[C]. Lodz:Advances in Web Intelligence Third International Atlantic Web Intelligence Conference,AWIC, 2005.
[16]	Kim D H, Cho J H . A biologically inspired intelligent PID controller tuning for AVR systems[J]. International Journal of Control,Automation and Systems, 2006,4(5):624-636.

算法	最优值	误差	代数	x₁	x₂	用时/s
GA	-173.131	7.26%	60	-0.862 7	-1.381	43
BFO	-184.961	0.93%	7	-0.908 3	-1.422 5	84
GA-BFO	-186.684	0.01%	14+3	-0.795 5	-1.422 5	24
真实值	-186.7			-0.800 3	-1.425 1

算法	最优值	误差	代数	x	y	用时/s
GA	1.983 8	7.77%	54	±0.417 7	±0.396 3	17
BFO	1.841 2	0.02%	10	±0.418 4	±0.448 7	33
GA-BFO	1.840 9	0.005%	26+2	±0.463 8	±0.464 2	12
真实值	1.840 8			±0.469 1	±0.464 4

算法	最优值	误差	代数	x	y	用时/s
GA	35.584 2	8.4%	77	9.321 4	5.144 0	17
BFO	33.641 0	13.4%	4	8.228 8	5.076 1	45
GA-BFO	36.844 6	5.7%	52+3	11.119 3	5.104 4	28
真实值	38.850 0			11.626 0	5.725 1

Optimization of Nonlinear Model Based on GA-BFO Combination Algorithm

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