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

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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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