Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (5): 16-20.doi: 10.16180/j.cnki.issn1007-7820.2019.05.004

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Optimization of Nonlinear Model Based on GA-BFO Combination Algorithm

LI Yapin,ZOU Dexuan,DUAN Na   

  1. School of Electrical Engineering and Automation,Jiangsu Normal University,Xuzhou 221116,China
  • Received:2018-05-07 Online:2019-05-15 Published:2019-05-06
  • Supported by:
    National Natural Science Foundation of China(61403174);National Natural Science Foundation of China(61573172)


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