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Bacterial foraging optimization based on improved chemotaxis process and novel swarming strategy

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Abstract

Bacterial foraging optimization (BFO), a biological-inspired optimization algorithm, has been applied in various fields, such as complex function optimization, robot path planning. However, there still exist several insufficiencies in BFO algorithm due to the fixed chemotaxis step-size, the less-efficient search direction for tumbling and the swarming strategy with lower convergence rate. In order to deal with these issues, based on the Lévy flight step-size and particle swarm optimization (PSO) operator, this paper proposes the improved BFO algorithm (LPBFO). To reduce the mutual interference among different dimensions, each bacterium selects one dimension for tumbling randomly during the chemotactic process in LPBFO. The step-size of each bacterium is determined by the stochastic flight lengths of the improved Lévy flight which can generate small step-size with high frequency and big step-size occasionally; moreover, the stochastic step-size is also reduced adaptively based on the evolutionary generations, which makes the bacteria transform from global search to local search. Furthermore, inspired by the social information term in PSO, this paper employed the global best solution to improve the swarming performance. Several experiments on benchmark functions are carried out with the purpose of evaluating the performance of the proposed method. Experimental results show that the proposed algorithm achieves noticeable improvement compared with other competitive algorithms.

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Acknowledgements

This research is supported by the NSFC (National Nature Science Foundation of China)under grant no. 61573213, 61473174, 61473179, by the Natural Science Foundation of Shandong Province under grant no. ZR2015PF009, ZR2017PF008, by the China Postdoctoral Science Foundation under grant no. 2017M612270, by Shandong Province Science and Technology Development Program under grant no. 2014GGX103038, and Special Technological Program of Transformation of Initiatively Innovative Achievements in Shandong Province under grant no. 2014ZZCX04302.

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Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, 250061, China

    Bao Pang & Hongling Wang

  2. School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Weihai, 264209, China

    Yong Song, Chengjin Zhang & Runtao Yang

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  1. Bao Pang
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  2. Yong Song
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  3. Chengjin Zhang
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  4. Hongling Wang
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  5. Runtao Yang
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Correspondence to Yong Song.

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Pang, B., Song, Y., Zhang, C. et al. Bacterial foraging optimization based on improved chemotaxis process and novel swarming strategy. Appl Intell 49, 1283–1305 (2019). https://doi.org/10.1007/s10489-018-1317-9

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