Abstract
To improve the optimization ability of Bacterial Foraging Optimization (BFO), A Modified Bacterial Foraging Optimization algorithm is proposed, which we named MBFO. In MBFO, tumble directions of bacteria are guided by the global best of the population to make bacteria search the optimization area more effectively. Then, chemotactic step size of each bacterium will change dynamically to adapt with the environment. Meanwhile, in reproduction loop, all individuals will be chosen with a probability. To test the global optimization ability of MBFO, we tested it on ten classic benchmark functions. Original BFO, PSO and GA are used for comparison. Experiment results show that MBFO algorithm has significant improvements compared with original BFO and it performs best on most functions among the compared algorithms.
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Acknowledgement
This work was supported by the Project of National Natural Science Foundation of China (Grant No. 71201026, 61503373), Project of Natural Science Foundation of Guangdong (Grant No. 2015A030310274, 2015A030313649), Project of Dongguan Social Science and Technology Development (Grant No. 2013108101011) and Project of Dongguan Industrial Science and Technology Development (Grant No. 2015222119).
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Yan, X., Zhang, Z., Guo, J., Li, S., Zhao, S. (2016). A Modified Bacterial Foraging Optimization Algorithm for Global Optimization. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_62
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DOI: https://doi.org/10.1007/978-3-319-42291-6_62
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