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Root system growth biomimicry for global optimization models and emergent behaviors

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Abstract

Terrestrial plants have evolved remarkable adaptability that enables them to sense environmental stimuli and use this information as a basis for governing their growth orientation and root system development. In this paper, we explain the foraging behaviors of plant root and develop simulation models based on the principles of adaptation processes that view root growing as optimization. This provides us with new methods for global optimization. Accordingly a novel bioinspired optimizer, namely the root system growth algorithm (RSGA), is proposed, which adopts the root foraging, memory and communication and auxin-regulated mechanism of the root system. Then RSGA is benchmarked against several state-of-the-art reference algorithms on a suit of CEC2014 functions. Experimental results show that RSGA can obtain satisfactory performances on several benchmarks in terms of accuracy, robustness and convergence speed. Moreover, a comprehensive simulation is conducted to investigate the explicit adaptability of root system in RSGA. That is, in order to be able to climb noisy gradients in nutrients in soil, the foraging behaviors of root system are social and cooperative that is analogous to animal foraging behaviors.

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Acknowledgments

This research is partially supported by National Natural Science Foundation of China under Grant 61503373; Natural Science Foundation of Liaoning Province under Grant 2015020002; and Natural Science Foundation of Guangdong Province under Grant 2015A030310274.

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Authors and Affiliations

  1. College of Software, Northeastern University, Shenyang, 110318, China

    Lianbo Ma

  2. School of Computer Science and Software, Tianjin Polytechnic University, Tianjin, China

    Hanning Chen & Xiaodan Liang

  3. Benedictine University, Lisle, IL, USA

    Xu Li

  4. College of Information Science and Engineering, Central South University, Changsha, 410083, China

    Xiaoxian He

Authors
  1. Lianbo Ma
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  2. Hanning Chen
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  3. Xu Li
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  4. Xiaoxian He
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  5. Xiaodan Liang
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Corresponding author

Correspondence to Hanning Chen.

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The authors declare that they have no conflict of interest.

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Communicated by V. Loia.

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Ma, L., Chen, H., Li, X. et al. Root system growth biomimicry for global optimization models and emergent behaviors. Soft Comput 21, 7485–7502 (2017). https://doi.org/10.1007/s00500-016-2297-5

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