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Biology migration algorithm: a new nature-inspired heuristic methodology for global optimization

  • Methodologies and Application
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Abstract

In this paper, inspired by the biology migration phenomenon, which is ubiquitous in the social evolution process in nature, a new meta-heuristic optimization paradigm called biology migration algorithm (BMA) is proposed. This optimizer consists of two phases, i.e., migration phase and updating phase. The first phase mainly simulates how the species move to new habits. During this phase, each agent should obey two main rules depicted by two random operators. The second phase mimics how some species leave the group and new ones join the group during the migration process. In this phase, a maximum number of iterations will be set to predetermine whether a current individual should leave and be replaced by a new one. Simulation results based on a comprehensive set of benchmark functions and four real engineering problems indicate that BMA is effective in comparison with other existing optimization methods.

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Notes

  1. www.ntu.edu.sg/home/epnsugan/.

  2. http://www.alimirjalili.com/Projects.html.

  3. https://uk.mathworks.com/matlabcentral/profile/authors/2652824.

  4. https://uk.mathworks.com/matlabcentral/fileexchange/65846.

  5. https://uk.mathworks.com/matlabcentral/profile/authors/2652824.

  6. https://uk.mathworks.com/matlabcentral/profile/authors/2152124.

  7. https://uk.mathworks.com/matlabcentral/fileexchange/52901.

  8. http://www.alimirjalili.com/Projects.html.

  9. https://uk.mathworks.com/matlabcentral/fileexchange/52898.

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Acknowledgements

The authors express their sincere thanks to Prof. X. S. Yang for providing the codes. The authors would also like to thank the anonymous reviewers for their constructive suggestions. This work is partly supported by the National Natural Science Foundation of China under Grant 61075032 and the Anhui Provincial Natural Science Foundation under Grant J2014AKZR0055.

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Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Hefei, 230009, China

    Qingyang Zhang, Ronggui Wang & Juan Yang

  2. School of Information and Communication Technology, Griffith University, Nathan Campus, Brisbane, 4111, Australia

    Andrew Lewis

  3. School of Computer Science and Informatics, De Montfort University, Leicester, LE1 9BH, UK

    Francisco Chiclana & Shengxiang Yang

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  1. Qingyang Zhang
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  2. Ronggui Wang
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Correspondence to Qingyang Zhang.

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Zhang, Q., Wang, R., Yang, J. et al. Biology migration algorithm: a new nature-inspired heuristic methodology for global optimization. Soft Comput 23, 7333–7358 (2019). https://doi.org/10.1007/s00500-018-3381-9

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