Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

m-MBOA: a novel butterfly optimization algorithm enhanced with mutualism scheme

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

Abstract

The simplicity and effectiveness of a recently proposed metaheuristic, butterfly optimization algorithm (BOA) have gained huge popularity among research community and are being used to solve optimization problems in various disciplines. However, the algorithm is suffering from poor exploitation ability and has a tendency to show premature convergence to local optima. On the other hand, the mutualism phase of another popular metaheuristic symbiosis organisms search (SOS) is known for its exploitation capability. In this paper, a novel hybrid algorithm, namely m-MBOA is proposed to enhance the exploitation ability of BOA with the help of mutualism phase of SOS. To evaluate the effectiveness of m-MBOA, thirty-seven (37) classical benchmark functions are considered and the performance of m-MBOA is compared with the performance of ten (10) state-of-the-art algorithms. Statistical tools have been employed to observe the efficiency of the m-MBOA qualitatively, and obtained results confirm the superiority of the proposed algorithm compared to the state-of-the-art metaheuristic algorithms. Finally, four real-life optimization problem, namely gear train design problem, gas compressor design problem, cantilever beam design problem and three-bar truss design problem are solved with the help of the newly proposed algorithm, and the results are compared with the obtained results of different popular state-of-the-art optimization techniques and found that the proposed algorithm is more efficient than the compared algorithms.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abdechiri M, Meybodi MR, Bahrami H (2013) Gases brownian motion optimization: an algorithm for optimization (GBMO). Appl Soft Comput 13(5):2932–2946

    Article  Google Scholar 

  • Abdel-Basset M, Shawky LA (2018) Flower pollination algorithm: a comprehensive review. Artif Intell Rev. https://doi.org/10.1007/s10462-018-9624-4

    Article  Google Scholar 

  • Absalom EE, Prayogo D (2019) Symbiotic organisms search algorithm: theory, recent advances and applications. Expert Syst Appl 119:184–209

    Article  Google Scholar 

  • Al-Sharhan S, Omran MGH (2018) An enhanced symbiosis organisms search algorithm: an empirical study. Neural Comput Appl 29(11):1025–1043

    Article  Google Scholar 

  • Anandita S, Rosmansyah Y, Dabarsyah B, Choi JU (2015) Implementation of dendritic cell algorithm as an anomaly detection method for port scanning attack. In: 2015 international conference on information technology systems and innovation (ICITSI), pp 1–6

  • Arora S, Anand P (2019) Binary butterfly optimization approaches for feature selection. Expert Syst Appl 116:147–160

    Article  Google Scholar 

  • Arora S, Singh S (2015) Butterfly algorithm with levy flights for global optimization. In: International conference on signal processing, computing and control. IEEE, Solan, pp 220–224

  • Arora S, Singh S (2016) An improved butterfly optimization algorithm for global optimization. Adv Sci Eng Med 8:711–717. https://doi.org/10.1166/asem.2016.1904

    Article  Google Scholar 

  • Arora S, Singh S (2017a) A hybrid optimization algorithm based on butterfly optimization algorithm and differential evolution. Int J Swarm Intell 3(2–3):152–169

    Article  Google Scholar 

  • Arora S, Singh S (2017b) An effective hybrid butterfly optimization algorithm with artificial bee colony for numerical optimization. Int J Interact Multimed Artif Intell 4(4):14–21

    Google Scholar 

  • Arora S, Singh S (2017c) An improved butterfly optimization algorithm with chaos. J Intell Fuzzy Syst 32:1079–1088

    Article  Google Scholar 

  • Arora S, Singh S (2017d) Node localization in wireless sensor networks using butterfly optimization algorithm. Arab J Sci Eng 42:3325–3335

    Article  Google Scholar 

  • Arora S, Singh S (2018) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23:715. https://doi.org/10.1007/s00500-018-3102-4

    Article  Google Scholar 

  • Arora S, Singh S, Yetilmezsoy K (2018) A modified butterfly optimization algorithm for mechanical design optimization problems. J Braz Soc Mech Sci Eng 40(1):21

    Article  Google Scholar 

  • Aydilek B (2018) A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl Soft Comput 66:232–249

    Article  Google Scholar 

  • Chen X, Tianfield H, Mei C, Du W, Liu G (2017) Biogeography-based learning particle swarm optimization. Soft Comput 21(24):7519–7541

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Chuanwen J, Bompard E (2005) A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation. Math Comput Simul 68:57–65

    Article  MathSciNet  Google Scholar 

  • Colak M, Varol A (2015) A novel intelligent optimization algorithm inspired from circular water waves. Elektronika Elektrotechnika 21:3–6. https://doi.org/10.5755/j01.eee.21.5.13316

    Article  Google Scholar 

  • Dasgupta D, KrishnaKumar K, Wong D, Berry M (2004) Negative selection algorithm for aircraft fault detection. In: Nicosia G, Cutello V, Bentley PJ, Timmis J (eds) Artificial immune systems. ICARIS lecture notes in computer science. Springer, Berlin, p 3239

    Google Scholar 

  • Dhanya KM, Kanmani M (2019) Mutated butterfly optimization algorithm. Int J Eng Adv Technol 8(3):375–381

    Google Scholar 

  • Do DTT, Lee J (2017) A modified symbiotic organisms search (msos) algorithm for optimization of pin-jointed structures. Appl Soft Comput 61:683–699

    Article  Google Scholar 

  • Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Article  Google Scholar 

  • Fang Y, Liu G, He Y, Qiu Y (2003) Tabu search algorithm based on insertion method. In: International conference on neural networks and signal processing. Proceedings of the 2003, vol 1, pp 420–423

  • Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  • Rechenberg I (1978) Evolutionsstrategien. In: Schneider B, Ranft U (eds) Simulationsmethoden in der medizin und biologie. Medizinische informatik und statistik, vol 8, pp 83–114

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

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks, vol 4, pp 1942–1948

  • Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680. https://doi.org/10.1126/science.220.4598.671

    Article  MathSciNet  MATH  Google Scholar 

  • Koza JR (1994) Genetic programming: on the programming of computers by means of natural selection. Stat Comput 4:87. https://doi.org/10.1007/BF00175355

    Article  Google Scholar 

  • Mafarja MM, Mirjalili S (2019) Hybrid binary ant lion optimizer with rough set and approximate entropy reducts for feature selection. Soft Comput 23(15):6249–6265

    Article  Google Scholar 

  • Mirjalili S (2015) Moth-flame optimization algorithm. Knowl Based Syst 89(C):228–249

    Article  Google Scholar 

  • Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multiobjective problems. Neural Comput Appl 27(4):053–1073

    Article  Google Scholar 

  • Mortazavi A, Toan V, Nuholu A (2018) Interactive search algorithm: a new hybrid metaheuristic optimization algorithm. Eng Appl Artif Intell 71:275–292

    Article  Google Scholar 

  • Nama S, Saha AK, Ghosh S (2016) A new ensemble algorithm of differential evolution and backtracking search optimization algorithm with adaptive control parameter for function optimization. Int J Ind Eng Comput 7(2):323–338

    Google Scholar 

  • Nama S, Saha AK (2018) An ensemble symbiosis organisms search algorithm and its application to real world problems. Decis Sci Lett 7(2):103–118

    Article  Google Scholar 

  • Nama S, Saha A, Ghosh S (2016) Improved symbiotic organisms search algorithm for solving unconstrained function optimization. Decis Sci Lett 5(3):361–380

    Article  Google Scholar 

  • Nama S, Saha AK, Ghosh S (2017) A hybrid symbiosis organisms search algorithm and its application to real world problems. Memet Comput 9(3):261–280

    Article  Google Scholar 

  • Nama S, Saha AK (2018) A new hybrid differential evolution algorithm with self-adaptation for function optimization. Appl Intell 48(7):1657–1671

    Article  Google Scholar 

  • Panda A, Pani S (2016) A symbiotic organism search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems. Appl Soft Comput 46:344–360

    Article  Google Scholar 

  • Polap D, Wozniak M (2017) Polar bear optimization algorithm: metaheuristic with fast population movement and dynamic birth and death mechanism. Symmetry 9(10):203. https://doi.org/10.3390/sym9100203

    Article  Google Scholar 

  • Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

    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 43:303–315

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Riahi V, Kazemi M (2015) A hybrid heuristic algorithm for the nowait flowshop scheduling problem. In: 2015 international symposium on computer science and software engineering (CSSE), pp 1–6

  • 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 

  • Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010

    Article  Google Scholar 

  • Sharma A, Sharma D (2011) Clonal selection algorithm for classification. In: Lio P, Nicosia G, Stibor T (eds) Artificial immune systems. Springer, Berlin, pp 361–370

    Chapter  Google Scholar 

  • Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  Google Scholar 

  • Tan Y, Zhu Y, (2010) Fireworks algorithm for optimization. In: Tan Y, Shi Y, Tan KC (eds) Advances in swarm intelligence. ICSI 2010. Lecture notes in computer science, vol 6145. Springer, Berlin, Heidelberg, pp 355–364

  • Tian X, Yang H, Deng F (2006) A novel artificial immune network algorithm. In: 2006 international conference on machine learning and cybernetics, pp 2159–2165

  • Wang GG, Deb S, Cui Z (2015) Monarch butterfly optimization. Neural Comput Appl. https://doi.org/10.1007/s00521-015-1923-y

    Article  Google Scholar 

  • Xia X, Gui L, He G, Xie C, Wei B, Xing Y, Wu R, Tang Y (2017) A hybrid optimizer based on firefly algorithm and particle swarm optimization algorithm. J Comput Sci 26:488–500

    Article  Google Scholar 

  • Yang X, Deb S (2009) Cuckoo search via lvy flights. In: 2009 world congress on nature biologically inspired computing (NaBIC), pp 210–214

  • Yang XS (2010a) Firefly algorithm, Lévy flights and global optimization. In: Bramer M, Ellis R, Petridis M (eds) Research and development in intelligent systems XXVI. Springer, London

    Google Scholar 

  • Yang XS (2010b) A new metaheuristic bat-inspired algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Studies in computational intelligence, vol 284. Springer, Berlin, pp 65–74

    Chapter  Google Scholar 

  • Yi Y, He R (2014) A novel artificial bee colony algorithm. In: 2014 sixth international conference on intelligent human–machine systems and cybernetics, vol 1, pp 271–274

  • Yu VF, Redi AANP, Yang CL, Ruskartina E, Santosa B (2017) Symbiotic organisms search and two solution representations for solving the capacitated vehicle routing problem. Appl Soft Comput 52(C):657–672

    Article  Google Scholar 

  • Zhou Y, Su K, Shao L (2018) A new chaotic hybrid cognitive optimization algorithm. Cognit Syst Res 52:537–542. https://doi.org/10.1016/j.cogsys.2018.08.001

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to express their sincere thanks to the referees and editor for their useful suggestion and recommendations which have proved to be a great help towards the improvement of the paper.

Author information

Authors and Affiliations

  1. Department of Mathematics, National Institute of Technology, Agartala, Tripura, 799046, India

    Sushmita Sharma & Apu Kumar Saha

Authors
  1. Sushmita Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Apu Kumar Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Apu Kumar Saha.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human participants

This study does not contain any studies with human participants or animals performed by any of the authors.

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

Sharma, S., Saha, A.K. m-MBOA: a novel butterfly optimization algorithm enhanced with mutualism scheme. Soft Comput 24, 4809–4827 (2020). https://doi.org/10.1007/s00500-019-04234-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04234-6

Keywords

Search

Navigation