Computer Science ›› 2015, Vol. 42 ›› Issue (6): 101-106.doi: 10.11896/j.issn.1002-137X.2015.06.023

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Adaptive Bacterial Foraging Optimization Algorithm Based on Dynamic Gaussian Mutation and Random One for High Dimensional Functions

ZHANG Xin-ming, YIN Xin-xin and FENG Meng-qing   

  • Online:2018-11-14 Published:2018-11-14

Abstract: In view of the shortcomings of facterial foraging optimization (BFO),such as the bad optimization perfor-mance and generalization in its application of high dimensional function optimization,an adaptive bacterial foraging optimization algorithm based on combing dynamic Gaussian mutation and random one was proposed in this paper.First,the original elimination-dispersal operator is replaced with a new one based on combining random mutation to add population diversity and dynamical Gaussian mutation to raise convergence rate.Then a chemotactic step mechanism is adopted with dynamical adjusting and self-adapting adjusting.Finally,a new communication mechanism is added to the improved BFO.The simulation results on 14 high-dimensional functions indicate that the proposed optimization algorithm is rapid and has good performance and generalization,and outperforms the current global optimization algorithms such as SBFO,POLBBO,BFAVP and RABC.

Key words: Optimization method,Bacterial foraging optimization(BFO),Gaussian mutation,High dimensional function optimization,Dynamical adjusting

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