Skip to main content
SpringerLink
Log in

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

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

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.

References

  • Aidley DJ (1981) Animal migration. Cambridge University Press, Cambridge

    Google Scholar 

  • Alcala-Fdez J et al (2009) KEEL: a software tool to assess evolutionary algorithms to data mining problems. Soft Comput 13(3):307–318

    Article  Google Scholar 

  • Arora JS (2004) Introduction to optimum design. Academic Press, Cambridge

    Book  Google Scholar 

  • Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

  • Baruah RD, Angelov P (2014) DEC: dynamically evolving clustering and its application to structure identification of evolving fuzzy models. IEEE Trans Cybern 44(9):1619–1631

    Article  Google Scholar 

  • Belegundu AD (1983) Study of mathematical programming methods for structural optimization. Dissertation abstracts international B the sciences and engineering

  • Bernardino H, Barbosa I, Lemonge A (2007) A hybrid genetic algorithm for constrained optimization in mechanical engineering. In: Proceedings of IEEE congress on evolutionary computation, pp 646–653

  • Braha D (2012) Global civil unrest: contagion, self-organization, and prediction. Plos one 7(10):1–9

  • Chang PC, Chen SS, Zhang QF (2008) MOEA/D for flowshop scheduling problems. In: Proceeding of congress of evolutionary computation 2008 (CEC 2008), Hong Kong

  • Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139(15):98–112

    Article  Google Scholar 

  • Chickermane H, Gea H (1996) Structural optimization using a new local approximation method. Int J Numer Methods Eng 39:829–846

    Article  MathSciNet  MATH  Google Scholar 

  • Coelho LDS (2010) Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 37(2):1676–1683

    Article  Google Scholar 

  • Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127

    Article  Google Scholar 

  • Coello Coello CA (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287

    Article  MathSciNet  MATH  Google Scholar 

  • Coello Coello CA, Mezura Montes E (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inf 16(3):193–203

    Article  Google Scholar 

  • Cuevas E, Echavarria A, Ramrez-Ortegon MA (2014) An optimziation algorithm inspired by the states of matter that improves the balance between exploration and exploitation. Appl Intell 40(2):256–272

    Article  Google Scholar 

  • Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Article  Google Scholar 

  • Dingle H, Drake VA (2007) What is migration. Bioscience 57(2):113–121

    Article  Google Scholar 

  • Duan HB, Li P (2014) Bio-inspired computation in unmanned aerial vehicles. Springer, Berlin

    Book  Google Scholar 

  • Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, pp 39–43

  • Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166

    Article  Google Scholar 

  • Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183

    Article  Google Scholar 

  • Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    Article  MathSciNet  MATH  Google Scholar 

  • Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Article  Google Scholar 

  • Gandomi A, Yang X-S, Alavi A, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255

    Article  Google Scholar 

  • Hansen N, Muller SD, Kounoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11(1):1–18

    Article  Google Scholar 

  • He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99

    Article  Google Scholar 

  • Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–72

    Article  Google Scholar 

  • Hsieh TJ (2014) A bacterial gene recombination algorithm for solving constrained optimization problems. Appl Math Comput 231:187–204

    MathSciNet  MATH  Google Scholar 

  • Huang FZ, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356

    MathSciNet  MATH  Google Scholar 

  • Jiang QY, Wang L, Hei XH (2015) Parameter identification of chaotic systems using artificial raindrop algorithm. J Comput Sci 8:20–31

    Article  MathSciNet  Google Scholar 

  • Kannan B, Kramer SN (1994) An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Des 116(2):405–411

    Article  Google Scholar 

  • Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112–113:283–294

    Article  Google Scholar 

  • Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput 27(1):155–182

    Article  MATH  Google Scholar 

  • Kayabekir AE, Bekdas G, Nigdeli SM, Yang XS (2018) A comprehensive review of the flower pollination algorithm for solving engineering problems. In: Nature-inspired algorithms and applied optimization. Springer, Cham

  • Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simmulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  • Li XT, Zhang J, Yin MH (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877

    Article  Google Scholar 

  • Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algrithm for optimization: virus colony search. Adv Eng Softw 92:65–88

    Article  Google Scholar 

  • Liang JJ, Qin AK, Suganthan PH, Basker S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  • Liang J, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC2014 special session and competition on single objective real-parameter numerical optimization. In: Technical report, pp 1–32

  • Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579

    MathSciNet  MATH  Google Scholar 

  • Milner-Gulland EJ, Fryxell JM, Sinclair ARE (2011) Animal migration: a synthesis. Oxford University Press, Oxford

    Book  Google Scholar 

  • Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249

    Article  Google Scholar 

  • Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  • Precup RE, Sabau MC, Petriu EM (2015) Nature-inspired optimal tuning of input membership functions of Takagi–Sugeno–Kang fuzzy models for anti-lock braking systems. Appl Soft Comput 27:575–589

    Article  Google Scholar 

  • Ragsdell K, Phillips D (1976) Optimal design of a class of welded structures using geometric programming. J Eng Ind 98(3):1021–1025

    Article  Google Scholar 

  • Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    Article  Google Scholar 

  • Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  • Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13(5):2592–2612

    Article  Google Scholar 

  • Sandgren E (1988) Nonlinear integer and discrete programming in mechanical design, pp 95–105

  • Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimization algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  • Serdio F, Lughofer E, Zavoianu AC, Pichler K, Pichler M, Buchegger T, Efendic H (2017) Improved fault detection employing hybrid memetic fuzzy modeling and adaptive filters. Appl Soft Comput 51:60–82

    Article  Google Scholar 

  • Siddall JN (1972) Analytical decision-making in engineering design. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Simon D (2008) Biogeograph-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  • Stefanescu C et al (2012) Multi-generational long-distance migration of insects: studying the painted lady butterfly in the western Palaearctic. Ecography 36(4):474–486

    Article  Google Scholar 

  • Vrkalovic S, Teban TA, Borlea ID (2017) Stable Takagi–Sugeno fuzzy control designed by optimization. Int J Artif Intell 15(2):17–29

    Google Scholar 

  • Wang Y, Cai ZX, Zhang QF (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66

    Article  Google Scholar 

  • Wesche T, Goertler G, Hubert W (1987) Modified habitat suitability index model for brown trout in southeastern Wyoming. J Fish Manage 7:232–237

    Article  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  • Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NISCO 2010), studies in computational intelligence, vol 284. Berlin, pp 65–74

  • Yang X-S (2010) Nature-inspired meta-heuristic algorithms, 2nd edn. Luniver Press, Beckington

    Google Scholar 

  • Yang X-S, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

  • Yang SY, Yao X (2013) Evolutionary computation for dynamic optimization problems. Springer, Heidelberg

    Book  MATH  Google Scholar 

  • Zhang ZH, Zhang J, Li Y, Shi YH (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847

    Article  Google Scholar 

Download references

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.

Author information

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

Authors
  1. Qingyang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ronggui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrew Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Francisco Chiclana
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shengxiang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingyang Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee, and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3381-9

Keywords

Search

Navigation